Download DEVELOPMENT OF NEW SYSTEMATIC TECHNIQUES FOR RETROFIT OF WATER NETWORK

DEVELOPMENT OF NEW SYSTEMATIC TECHNIQUES
FOR RETROFIT OF WATER NETWORK
TAN YIN LING
UNIVERSITI TEKNOLOGI MALAYSIA
PSZ 19:16 (Pind. 1/97)
UNIVERSITI TEKNOLOGI MALAYSIA
BORANG PENGESAHAN STATUS TESIS♦
JUDUL: DEVELOPMENT OF NEW SYSTEMATIC TECHNIQUES FOR
RETROFIT OF WATER NETWORK
SESI PENGAJIAN : 2004/2005
Saya
TAN YIN LING
(HURUF BESAR)
mengaku membenarkan tesis (PSM/Sarjana/Doktor Falsafah)* ini disimpan di Perpustakaan
Universiti Teknologi Malaysia dengan syarat-syarat kegunaan seperti berikut :
1.
2.
Tesis adalah hakmilik Universiti Teknologi Malaysia.
Perpustakaan Universiti Teknologi Malaysia dibenarkan membuat salinan untuk tujuan
pengajian sahaja.
Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara
institusi pengajian tinggi.
**Sila tandakan (P )
3.
4.
P
SULIT
(Mengandungi maklumat yang berdarjah keselamatan atau
kepentingan Malaysia seperti yang termaktub di dalam
AKTA RAHSIA RASMI 1972)
TERHAD
(Mengandungi maklumat TERHAD yang telah ditentukan
oleh organisasi/badan di mana penyelidikan dijalankan)
TIDAK TERHAD
Disahkan oleh
(TANDATANGAN PENULIS)
(TANDATANGAN PENYELIA)
Alamat Tetap:
14-D, LORONG PULAU TIKUS,
10350 PULAU PINANG.
13 June 2005
Tarikh:
CATATAN:
*
**
®
P. M. DR. ZAINUDDIN ABDUL MANAN
Nama Penyelia
Tarikh:
13 June 2005
Potong yang tidak berkenaan.
Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi
berkenaan dengan menyatakan sekali sebab dan tempoh tesis ini perlu dikelaskan sebagai
SULIT atau TERHAD.
Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah dan Sarjana secara
penyelidikan, atau disertasi bagi pengajian secara kerja kursus dan penyelidikan, atau
Laporan Projek Sarjana Muda (PSM).
“I hereby declare that I have read this thesis and in my
opinion this thesis is sufficient in terms of scope and quality for the
award of the degree of Master of Engineering (Chemical)”
Signature
:
Name of Supervisor :
P. M. DR. ZAINUDDIN ABDUL MANAN
Date
13 June 2005
:
DEVELOPMENT OF NEW SYSTEMATIC TECHNIQUES
FOR RETROFIT OF WATER NETWORK
TAN YIN LING
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Master of Engineering (Chemical)
Faculty of Chemical and Natural Resources Engineering
Universiti Teknologi Malaysia
JUNE 2005
ii
I declare that this thesis entitle “Development of New Systematic Techniques for
Retrofit of Water Network ” is the result of my own research except as cited in the
references. This thesis has not been accepted for any degree and is not concurrently
submitted in candidature of any other degree.
Signature
:
Name
:
TAN YIN LING
Date
:
13 June 2005
iii
To my beloved parents, brother and Derek
iv
ACKNOWLEDGEMENTS
Firstly, I would like to express my sincere gratitude to my supervisor,
Associate Professor Dr. Zainuddin Abdul Manan, the current Head of Chemical
Engineering Department, Universiti Teknologi Malaysia (UTM), for his dedication,
support and guidance throughout the whole period of this research work. I also
appreciate his guidance on the research and the freedom tha t he had given me in
exploring the scope of my research.
My sincere thank is also directed to Mr. Dominic Foo Chwan Yee for his
thought and insight of the research project.
I am grateful to Ministry of Science, Technology and Environment for
providing National Science Fellowship (NSF) scholarship for this project.
Finally, I would like to thank my parents, brother and Soh Chze Min Derek
for their support and understanding during my difficulties. I also appreciate my
fellow friends who have directly and indirectly contribute to the success of this
project.
v
ABSTRACT
Grassroots synthesis of maximum water recovery network based on Pinch
Analysis has been rather well established. In contrast, less work has been done on
retrofit of water network. There is a clear need to develop procedures to retrofit an
existing water network. Four new systematic techniques for retrofit of water network
based on Pinch Analysis concept have been developed in this work, i.e. retrofit of
water network for mass transfer-based operations; retrofit of water network for nonmass transfer-based operations; retrofit of water network with regeneration unit(s)
optimisation; retrofit of water network with the addition of new regeneration unit(s).
Retrofit technique for water network with mass transfer-based operations involves
two key steps namely utility targeting and network design. During targeting, utility
and capital cost targets were determined for a particular capital expenditure. Lastly,
the existing network was retrofitted to meet the targets. Retrofit method for nonmass transfer-based operations precludes targeting and only requires retrofit design.
A new graphical tool called concentration block diagram (CBD) has been introduced
to diagnose, retrofit and evolve the existing water network. The new techniques
proposed for retrofit of water network with existing regeneration unit(s)
optimisation/ additional new regeneration unit(s) consist of two stages. The first
stage locates the various retrofit targets, where utility savings and capital investment
were determined for a range of process parameters (i.e. total flowrate and/or outlet
concentration of the regeneration unit). Next, the existing water network was redesigned to achieve the chosen targets. Application of the new retrofit techniques on
paper mill plants proves that the techniques are both highly interactive as well as
viable for implementation.
vi
ABSTRAK
Sintesis asas bagi rangkaian perolehan air yang maksimum berdasarkan
Analisis Pinch telah banyak diterokai. Sebaliknya, hanya sedikit kajian yang telah
dilakukan terhadap pengubahsuaian rangkaian air. Ini jelas menunjukkan bahawa
prosedur pengubahsuaian rangkaian air amat diperlukan. Empat teknik baru yang
sistematik bagi pengubahsuaian rangkaian air telah dibangunkan, khususnya,
pengubahsuaian rangkaian air bagi operasi yang melibatkan pindah jisim;
pengubahsuaian rangkaian air bagi operasi yang tidak melibatkan pindah jisim;
pengubahsuaian rangkaian air dengan pengoptimuman unit penjanaan semula;
pengubahsuaian rangkaian air dengan penambahan unit penjanaan semula. Teknik
pengubahsuaian rangkaian air bagi operasi yang melibatkan pindah jisim melibatkan
dua langkah utama iaitu penetapan sasaran dan rekadentuk rangkaian air. Semasa
penetapan sasaran, sasaran utility dan kos modal telah diperolehi berdasarkan
pelaburan yang tetap.
Akhirnya, rangkaian yang sedia ada diubahsuai untuk
mencapai sasaran yang ditetapkan. Pengubahsuaian rangkaian air bagi operasi yang
tidak melibatkan pindah jisim hanya memerlukan pengubahsuaian rangkaian.
Gambar rajah blok kepekatan telah diperkenalkan untuk menganalisis, mengubahsuai
dan membangunkan rangkaian air yang sedia ada.
Teknik-teknik baru yang
dicadangkan bagi pengubahsuaian rangkaian air dengan pengoptimuman unit
penjanaan semula/ penambahan unit penjanaan semula melibatkan dua peringkat.
Dalam peringkat pertama, beberapa sasaran pengubahsuaian termasuk pengurangan
utiliti dan pelaburan telah diperolehi bagi satu lingkungan parameter proses.
Seterusnya, rangkaian air yang sedia ada diubahsuai bagi mencapai sasaran yang
telah ditetapkan.
Penggunaan teknik-teknik pengubahsuaian baru ini ke atas
beberapa kajian kes kilang kertas telah membuktikan bahawa teknik-teknik ini
adalah ama t interaktif dan praktikal untuk dilaksanakan.
TABLE OF CONTENTS
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENTS
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xiii
LIST OF FIGURES
xiv
LIST OF SYMBOLS
xx
INTRODUCTION
1
1.1
Problem Background
1
1.2
The Water Management Hierarchy
4
1.3
Problem Statement
5
1.4
Objectives
6
1.5
Scopes of Research
6
1.6
Research Contribution
7
1.7
Summary of This Thesis
8
viii
2
FUNDAMENTAL THEORY
11
2.1
Introduction
11
2.2
Process Synthesis
11
2.3
Pinch Analysis
13
2.4
Retrofit of Heat Exchange Network Using Pinch
14
Analysis
2.5
Mass Exchange Network
16
2.5.1
What is a Mass Exchanger?
16
2.5.2
Sizing and Costing of Mass Exchanger
17
Unit
2.5.3
Grassroots Synthesis of Mass Exchange
23
Network
2.5.3.1
The Targeting Approach for
24
Mass Exchange Networks
2.5.3.2
2.5.4
Network Design
Retrofit Synthesis of Mass Exchange
29
32
Network
2.6
Water Pinch Analysis
33
2.6.1
Water Pinch Analysis Concept
33
2.6.2
Types of Water-using Operations
34
2.6.2.1
34
Mass Transfer-based Waterusing Operations
2.6.2.2
Non-mass Transfer-based
36
Water-using Operations
2.6.3
Targeting Approach for Maximum
37
Recovery Network through Reuse and
Recycle
2.6.3.1
Limiting Composite Curve
37
2.6.3.2
Water Surplus Diagram
38
2.6.3.3
Water Cascade Analysis
41
ix
2.6.4
Targeting Approach for Maximum
46
Recovery Network through Reuse,
Recycle and Regeneration
2.6.4.1
Limiting Composite Curve
47
2.6.4.2
Water Surplus Diagram and
49
Water Cascade Analysis
2.6.5
Network Design
50
2.6.5.1
Grid Diagram
50
2.6.5.2
Network Design through
52
Source and Demand Approach
2.6.6
3
Water Network Retrofit Constraints
54
LITERATURE REVIEW
55
3.1
Introduction
55
3.2
Heat Exchange Network Retrofit
56
3.3
Mass Exchange Network
58
3.3.1
58
Grassroots Synthesis of Mass Exchange
Network
3.3.2
3.4
Mass Exchange Network Retrofit
60
Water Pinch Analysis
61
3.4.1
61
Grassroots Synthesis of Water Recovery
Network Using Pinch Analysis
3.4.1.1
Grassroots Synthesis for
62
Maximum Recovery Network
through Reuse and Recycle
3.4.1.2
Grassroots Synthesis for
63
Maximum Recovery Network
through Reuse, Recycle and
Regeneration
3.4.2
Retrofit of Water Network
65
x
3.5
The State-of-the-art on Water Network
66
Retrofit Addressing the Research Gap
4
METHODLOGY
68
4.1
Introduction
68
4.2
Retrofit of Water Network with Reuse
68
and Recycling
4.3
Retrofit of Water Network with Reuse,
72
Recycling and Regeneration
4.3.1
Retrofit
of
Water
Network
with
72
Regeneration Units Optimisation
4.3.2
Retrofit of Water Network with the
75
Additional of New Regeneration Units
4.4
5
Chapter Summary
78
RESULTS AND DISCUSSIONS
79
5.1
79
Retrofit of Water Network for Mass Transferbased Operations
5.1.1
Problem Statement and Assumptions
79
5.1.2
Case Study 1
80
5.1.3
Retrofit Targeting
81
5.1.3.1
Minimum Fresh Water Target
82
5.1.3.2
Number of Tray Target
83
5.1.3.3
Nstages versus FW plot
88
5.1.4
Retrofit Design
92
5.1.5
Summary of the Developed Water
96
Network Retrofit for Mass Transferbased Operations
5.2
Retrofit of Water Network for Non- mass
Transfer based Operations
97
xi
5.2.1
Problem Statement and Assumptions
97
5.2.2
Case Study 2
98
5.2.3
Retrofit Design
101
5.2.4
Summary of the Developed Water
108
Network Retrofit for Non- mass
Transfer-based Operations
5.3
Retrofit of Water Network with Regeneration
108
Units Optimisation
5.3.1
Problem Statement and Assumptions
108
5.3.2
Case Study 3
109
5.3.3
Selection of Optimisation Parameter for
114
Existing Regeneration Units
5.3.4
Retrofit Targeting
117
5.3.4.1
117
Comparison of Estimated
Investment Costs
5.3.4.2
Optimisation of SDF2 with
119
Increased Freg
5.3.4.3
Optimisation of DAF with
123
Lowered Cout
5.3.4.4
Discussion
128
5.3.5
Retrofit Design
129
5.3.6
Summary of the Developed Water
135
Network Retrofit with Regeneration
Units Optimisation
5.4
Retrofit of Water Network with the Additional of
136
New Regeneration Units
5.4.1
Problem Statement and Assumptions
136
5.4.2
Case Study 4
136
5.4.3
Retrofit Targeting
137
5.4.3.1
139
Case 1: Vary Freg with Fixed
Cout
xii
5.4.3.2
Case 2: Vary Cout with Fixed
147
Freg
5.4.3.3
Case 3: Vary Cout and Freg
151
5.4.3.4
Discussions
155
5.4.4
Retrofit Design
155
5.4.5
Summary of the Developed Water
161
Network Retrofit with the Additional of
New Regeneration Units
6
CONCLUSIONS AND FUTURE WORKS
162
6.1
Summary and Significance
162
6.2
Future Works
163
REFERENCES
165
xiii
LIST OF TABLES
TABLE NO.
TITLE
PAGE
2.1
Interval water balance table
42
2.2
Water cascade table
46
5.1
Stream data for case study 1
81
5.2
Limiting water data with ε = 0.00021 kmol
83
SO2 /kmol water
5.3
WCT with e = 0.00021 for case study 1
83
5.4
X-Y Table for case study 1
85
5.5
Summary of tray contributions for each gas
87
stream and the total number of trays above and
below the pinch regions for case study 1
5.6
Water demands and sources for case study 2
100
5.7
WCT for case study 2
102
5.8
Comparison of fresh water consumption and
106
Wastewater generation before and after retrofit
5.9
Limiting water data for case study 3
113
5.10
Economic data for regeneration units
113
5.11
WCT for case study 3 in grassroots design mode
115
5.12
WCT for case study 3 with Fupgrade of 290.4 ton/h
126
5.13
WCT for case study 3 with Fupgrade of 435.6 ton/h
130
5.14
Economic data for regeneration units
138
5.15
WCT with 620.9 ton/h of Freg with 30ppm of Cout
141
xiv
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
The water resources of the earth
2
1.2
Global water use
2
1.3
Contribution of main industrial sectors to the
3
production of organic water pollutants
(a) high- income countries
(b) low income countries
1.4
A holistic approach for water minimization
5
through ZM Water Management Hierarchy
1.5
A flow diagram illustrating the conceptual
10
link between the chapters
2.1
The onion diagram for process design
13
2.2
Area versus energy diagram
15
2.3
Savings versus investment diagram
15
2.4
A schematic representation of a mass
17
exchanger
2.5
The McCabe Thiele diagram
18
2.6
Schematic representation of the MEN
24
synthesis problem
2.7
Establishing the corresponding composition
25
scales
2.8
Construction of the rich composite curve
27
2.9
Construction of the lean composite curve
28
2.10
Mass composite curves
28
xv
2.11
A mass exchange match show on a grid
30
Diagram
2.12
(a) Match feasibility immediately above the
31
pinch
(b) Match feasibility immediately below the
pinch
2.13
Stage versus mass load diagram
33
2.14
Mass transfer-based water-using operations:
35
(a) Vessel washing
(b) Sour gas absorption where water demand
and water source exist
2.15
(a) A mass transfer-based water-using process
35
(b) Limiting water profile
2.16
Non-mass transfer-based water-using
36
operations:
(a) a reactor hat consumes water in aniline
production
(b) a reactor that reduces water as a byproduct
in acrylonitrile (AN) production
2.17
Two other common types of the non- mass
36
transfer-based water- using operations:
(a) cooling tower make up
(b) boiler blow-down
2.18
Construction of the limiting composite curve
38
(a) Limiting water profile
(b) Limiting composite curve
(c) Matching the water supply line to
determine the minimum targets
2.19
Source and demand composite
39
2.20
The surpluses and deficits are plotted to form
40
the water surplus diagram
xvi
2.21
Summary of targeting procedure by water
41
surplus diagram
2.22
(a) Water cascade diagram with an assumed
43
fresh water flowrate of 0 ton/h
(b) Pure water cascade is used to check the
feasibility of the water cascade
(c) Interval fresh water demand to determine
the fresh water amount needed in each
purity interval
2.23
A feasible water cascade
45
2.24
Regeneration of water below the pinch for
47
regeneration reuse
2.25
Regeneration of water at the pinch for
48
regeneration reuse
2.26
Regeneration recycling represented on
49
limiting composite curve
2.27
(a) Grid diagram for water network with three
50
loops
(b) Conventional flowsheet for simplified
design (after loop breaking)
2.28
Network design with maximum driving force
51
2.29
Network design with minimum number of
52
water sources
2.30
Network design by source and demand
53
approach
4.1
Overview of the four methodologies
69
developed in this work
4.2
Flow diagram for retrofit of water network for
71
mass transfer-based operations
4.3
Flow diagram for retrofit of water network for
non- mass transfer-based operations
72
xvii
4.4
Retrofit targeting flow diagram for retrofit of
74
water network with regeneration units
optimisation
4.5
Retrofit design flow diagram for retrofit of
75
water network with regeneration units
optimisation
4.6
Retrofit targeting flow diagram for retrofit of
77
water network with the additional of new
regeneration units
4.7
Retrofit targeting flow diagram for retrofit of
78
water network with the additional of new
regeneration units
5.1
Existing conventional flowsheet for case
80
study 1
5.2
(a) An absorption column (counter-current
84
mass exchanger)
(b) absorption column represented on X-Y
diagram
5.3
Nstages versus FWmin plot for case study 1
88
5.4
Nstages versus FW plot for case study 1
89
5.5
Savings versus investment plot for cases
91
study 1
5.6
Existing water network for case study 1
93
5.7
Existing water network for case study 1 with
94
eliminated cross-pinch exchangers
5.8
Retrofitted water network for case study 1
95
5.9
Conventional flowsheet for the retrofitted
96
network for case study 1
5.10
Existing water network for case study 2
99
5.11
Existing water network in CBD form for case
103
study 2
5.12
Identified cross-pinch stream for case study 2
104
5.13
Preliminary retrofit design for case study 2
105
xviii
5.14
Final retrofit design for case study 2
106
5.15
Conventional flowsheet for the retrofitted
107
network for case study 2
5.16
Existing water network for case study 3
110
5.17
FFW versus ∆Freg plot for optimisation of
120
SDF2 through increasing Freg
5.18
Savings versus investment plot for
122
optimisation of SDF 2 through increasing Freg
5.19
FFW versus Fupgrade for optimisation of SDF1
124
and DAF through upgrading Cout
5.20
FFW versus ∆Cout for optimisation of DAF
125
through upgrading Cout
5.21
Savings versus investment for optimisation of
127
DAF through upgrading Cout
5.22
Two kinds of retrofit profiles (a) curve paths
128
(b) straight paths
5.23
Existing water network for case study 3 in
131
CBD
5.24
Existing water network in CBD with
132
optimised regeneration units for case study 3
5.25
Final retrofit design for case study 3
133
5.26
Conventional flowsheet for the retrofitted
134
network for case study 3
5.27
FFW versus Freg (Case 1)
140
5.28
Two kinds of retrofit profiles (case 1) (a)
142
curve paths (b) straight paths
5.29
FFW versus Freg plot with constant a (Case 1)
144
5.30
Savings versus investment plot for DAF
145
(Case 1)
5.31
Savings versus investment plot for SDF
146
(Case 1)
5.32
Two kinds of retrofit profiles (case 2) (a)
curve paths (b) straight paths
148
xix
5.33
FFW versus Cout plot with constant a (Case 2)
149
5.34
Savings versus investment plot for DAF
150
(Case 2)
5.35
Savings versus investment plot for SDF
151
(Case 2)
5.36
FFW, min versus Cout (Case 3)
152
5.37
FFW versus Cout plot with new Cout boundary
153
(Case 3)
5.38
Savings versus investment plot for DAF
154
(Case 3)
5.39
Savings versus investment plot for SDF
154
(Case 3)
5.40
Existing water network for case study 4 in
157
CBD with identified streams for regeneration
5.41
Preliminary retrofit design for case study 4
158
5.42
Final retrofit design for case study 4
159
5.43
Conventional flowsheet for the retrofitted
160
network for case study 4
xx
LIST OF SYMBOLS
SYMBOLS
AF
-
Approach flow
bj
-
Intercept of equilibrium line for the j th MSA
C
-
Contaminant concentration
Ci
-
Contaminant concentration of source i
Cmax,j
-
Maximum acceptable concentration of demand j
Cn
-
Contaminant concentration
CPROC,IN
-
Inlet concentration of process stream
CPROC,OUT
-
Outlet concentration of process stream
CW,IN
-
Inlet concentration of water stream
CW,OUT
-
Outlet concentration of water stream
(C W,IN)max
-
Maximum inlet concentration of water stream
(C W,OUT )max
-
Maximum outlet concentration of water stream
CW in
-
Inlet concentration of water stream
CWout
-
Outlet concentration of water stream
CIT
-
Composite interval table
D
-
Diameter of a column
DAF
-
Dissolved air flotation
DIP
-
De-inking pulper
f
-
Flowrate
fc
-
Total flowrate
F
-
Flowrate
Fc
-
Cumulative net water source or demand for a
process
xxi
FD,j
-
Total flowrate of demand at each
concentration
Fi
-
Total flowrate available from source i
Fj
-
Total flowrate required by demand j
FS,i
-
Total flowrate of source at each concentration
FFW
-
Total flowrate of fresh water
FWW
-
Total flowrate of wastewater
Gi
-
Rich (waste) stream flowrate
h
-
hour
H
-
Height of a column
HEN
-
Heat exchange networks
HENs
-
Heat exc hange networks synthesis
HTUx
-
Overall height of transfer units on the lean phase
HTUy
-
Overall height of transfer units on the rich phase
i
-
Source
j
-
Demand
k
-
Interval
kg
-
Kilogram
kmol
-
Kilo mole
Lj
-
Lean (waste) stream flowrate
Lcj
-
Maximum flowrate of MSA
M
-
Mass Load
mc
-
Total mass load
mj
-
Slope of equilibrium line of component in lean stream
j
MSA
-
Mass separating agents
MEN
-
Mass exchange networks
MENS
-
Mass exchange networks synthesis
Nr
-
Number of real trays
NR
-
Number of rich (waste) streams
NS
-
Number of lean (MSA) streams
NSE
-
Number of external MSAs streams
NSP
-
Number of internal MSAs streams
xxii
Nunit,pinch
-
Minimum number of mass exchange units
NAP
-
Number of actual plate
NTP
-
Total number of plate
NTUx
-
Overall number of transfer units on the lean phase
NTUy
-
Overall number of transfer units on the rich phase
optimum
-
Optimum condition
P
-
Purity
ppm
-
Parts per million
R
-
Set of rich streams
RTD
-
Retrofit thermodynamic diagram
s
-
second
S
-
Set of lean streams
S
-
Tray spacing
ST
-
Stream
ton
-
Tonne
X
-
Limiting water composition
x sj
-
Supply (inlet) composition of lean (MSA) stream
x tj
-
Target (outlet) composition of lena (MSA) stream
x*j
-
Maximum theoretically attainable composition of the
MSA
x jin
-
Inlet composition of lean (MSA) stream
x jout
-
Outlet composition of lean (MSA) stream
x jout,*
-
Maximum theoretically attainable outlet composition
of the MSA
yr
-
Year
yi
-
Rich (waste) stream composition
ysi
-
Supply (inlet) composition of rich (waste) stream
yti
-
Target (outlet) composition of rich (waste) stream
yiin
-
Inlet composition of rich (waste) stream
yj out
-
Outlet composition of rich (waste) stream
yintexisting
-
Intermediate composition of the rich stream leaving
the existing column
xxiii
yintnew
-
Intermediate composition of the rich stream leaving
the new column
youtMEN
-
Outlet composition of mass exchange network
GREEK LETTERS
α
-
Total efficiency
u
-
Velocity
?
-
Density
ω
-
Trade off composition difference
ε
-
Minimum allowable composition difference
ηo
-
Overall exchanger efficiency
ηy
-
Stage efficiency for the rich phase
?
-
Difference
∑
-
Summation
SUBSCRIPTS
D
-
Water demand
existing
-
Existing column
i
-
Rich (waste) stream
IN
-
Inlet
j
-
Lean (MSA) stream
l
-
liquid
max
-
Maximum
Mass Load
-
Total mass load accumulated
MEN
-
Mass exchange networks
xxiv
new
-
New column
o
-
Initial
OUT
-
Outlet
PROC
-
Process
R
-
Rich streams
Regen
-
Regeneration
S
-
Water demand
S
-
Lean streams
SE
-
External MSA streams
SR
-
Internal MSA streams
Stages
-
Number of stages in a column
v
-
vapour
W
-
Water
x
-
Lean phase
y
-
Rich phase
c
-
Maximum
in
-
Inlet
int
-
Intermediate
NTP
-
Total number of plate
out
s
t
-
Outlet
-
Supply
-
Target
W
-
Water
SUPERSCRIPTS
CHAPTER 1
INTRODUCTION
1.1
Problem background
Water is largely taken for granted as it is perceived as the most widely
occurring substance in the Earth.
It is reported that 2.5 % of world water is
freshwater while the rest is salt. However, only 0.3 % of the world’s freshwater is
available in rivers or lake. Almost all the rest is held up by icecaps and glaciers or
buried deep in underground aquifers (Figure 1.1) (Shiklomanov, 1999).
Global freshwater consumption raised six fold between 1990 and 1995, which
is more than twice the rate of population growth. Thus, about one-third of the
world’s population already lives in countries with moderate to high water stress
(UNEP, 1999). Current predictions are that by 2050 at least one in four people is
likely to live in countries affected by chronic or recurring shortages of freshwater
(World Water Assessment Programme, WWAP, 2000).
2
Figure 1.1: The water resources of earth (Shiklomanov, 1999)
Demands for water come not only from the need to drink and the need to deal
with waste. The primary consumers of water include industry as well as agriculture
sectors (Figure 1.2). Consequently, water pollution created from these demands has
significantly contributed towards the scarcity of freshwater in the world. About two
million tons of waste is dumped everyday into rivers, lakes and streams, with one
litre of wastes sufficient to pollute about eight litre of water (WWAP, 2000).
Industry
(22%)
Domestic
(8%)
Agriculture
(70%)
Figure 1.2: Global water use (UNEP, 1999)
UNEP has also stated that industrial wastes are significant sources of water
pollution. Industrial wastes often give rise to contaminant with heavy metals and
persistent organic compounds. Some 300-500 million tons of heavy metals, solvent,
toxic sludge and other wastes accumulate each year from industry (United Nations
3
Industrial Development Organisation, UNIDO, 1998). Figure 1.3 shows the global
estimates of emissions of organic water pollutants by different industry sector (World
Bank, 2001). A study of 15 Japanese cities showed that 30 % of all groundwater
supplies are contaminated by chlorinated solvents from industry. In some cases, the
solvents from spills travelled as far as 10 km from the source of pollution. As a
result, strict enforcement of environmental regulations has been carried out to
minimise the water pollution.
(a)
(b)
Figure 1.3: Contributions of main industrial sectors to the production of organic
water pollutants (a) high- income countries (b) low- income countries
In most countries, industrial water tariff has been increasing from time to
time. One of the main reasons that causes this is the current inflation level, which
resulted in higher chemical cost, labour cost and construction cost. Besides, the need
for more advanced wastewater treatment techniques with higher wastewater
treatment costs to treat highly polluted water has also become one of the driving
forces towards water tariff increment. The need to fund addition of water utility to
meet rising demand for clean fresh water has also causes water supply companies to
increase the water tariff.
Therefore, rising cost of industrial freshwater and stringent environmental
regulations have been functional to reduce the water requirement from the industry.
Thus, it became necessary for the industries to look for better water management
4
system to reduce their freshwater consumption and wastewater generation. To solve
this problem, many companies have applied the systematic technique based on water
pinch analysis (WPA) through efficient water utilisation.
Our experience and analysis have shown that WPA is well suited for
grassroots design but has limitations when applied to existing processes. This is
mainly caused by the existence of numerous constraints and problems related to the
operability of an existing plant. Consequently, there is a need of new systematic
techniques for retrofit of water network.
1.2
The Water Management Hierarchy
It is quite common to find the environmental issue considered during the last
stage of process design. Wastewater produced often goes through the end-of-pipe
treatment where wastewater is treated with treatment processes such as biological
treatment, filtration, membranes, etc. to a form suitable for discharged to the
environment.
Over the past decade, water minimisation through WPA has become an
important issue in the chemical process industries to achieve optimum water utility
network. This approach does achieve beneficial goals such as reducing the water
utility, bigger process throughput, lower capital and operating costs as well as
improving the public perception towards the company.
To obtain the optimum water utility design for a water network, Manan et al.,
(2004b) established a hierarchical approach for fresh water conservation called ZM
water management hierarchy (Figure 1.4). This is a general guideline for fresh water
conservation.
The hierarchy consists of five levels, namely source elimination,
source reduction, direct reuse, reclamation, and discharge after treatment. Each level
represents various water management options. The levels are arranged in order of
preference, from the most preferred option at the top of the hierarchy (level 1) to the
5
least preferred at the bottom (level 5). Water minimisation is concerned with the first
to the fourth level of the hierarchy.
Source elimination and source reduction at the top of the hierarchy is
concerned with the complete avoidance of fresh water usage. When it is not possible
to eliminate or reduce fresh water at source, wastewater recycling and regeneration
should be considered. Discharge after treatment should only be considered when
wastewater cannot be recycled. Through the ZM water management hierarchy, the
end-of-pipe treatment may not be eliminated, but it will become economically
legitimate.
Source
Elimination
Source
Reduction
Reuse
Regeneration Reuse
Discharge after Treatment
Figure 1.4: A holistic approach for water minimisation through the ZM Water
Management Hierarchy (Manan et al., 2004b)
1.3
Problem Statement
Water is used in the process industry for a wide range of applications.
Increased cost of wastewater treatment and rising demand for high quality industrial
water have created a pressing need for efficient water utilisation and wastewater
reuse. The synthesis of optimal water utilisation networks has dealt with grass-root
design, where the emphasis is on the minimisation of raw water and maximisation of
6
water reuse and regeneration. To date, very little has been accomplished on the use
of heuristic techniques for the retrofit of existing water network in contrast to the
work done on grassroots designs.
There is a clear need to develop systematic
techniques for water network retrofit with and without regeneration to help achieve
water savings for existing processes.
The water network retrofit problem is summarised as follows:
Given a set of mass transfer-based and/or non- mass transfer-based water-using
processes, with/without a set of treatment processes, it is desired to perform
retrofit synthesis on the existing water network with/without integration of new
treatment process(es) or optimisation of existing treatment process(es). The
various streams in the process are re-structured to simultaneously accomplish
the best savings in operating costs, subject to a minimum payback period
or/and maximum capital expenditure.
1.4
Objective
The main objective of this research is to develop new systematic techniques
for the retrofit of water network with and without regene ration that includes utility
targeting and/or network design.
1.5
Scope of Research
The scopes of this work include:
7
•
Analysis of the state-of-art technique
It involved analysis of the previous approach for retrofit, their
advantages and disadvantages and the improvements required.
•
Development of retrofit targeting techniques
Three new systematic targeting techniques for water network with
and/or without regeneration have been established. These procedures
are used according to different types of water network. Capital and
operating costs as well as piping cost estimations are taken into
consideration in these targeting procedures.
•
Establishment of retrofit design procedure
A systematic retrofit design methodology has been introduced to meet
the retrofit targets. This methodology is also applicable for cases
without retrofit targeting procedure.
1.6
Research Contributions
The main contributions of this research are summarised as follows:
i. As far as it can be found in the literature, this is the first work on the
Water Cascade Analysis (WCA)-based water network retrofit synthesis.
The basic concept of pinch analysis for heat exchange network, mass
exchange network and water network are the basic of this work.
ii. A new systematic retrofit technique for water network with mass transferbased operations involving two key steps namely utility (water) targeting
and network design has been established. In the targeting stage, fresh
water and wastewater targets, and capital cost targets were determined for
a particular capital expenditure.
retrofitted to meet the targets.
Lastly the existing network was
8
iii. A new systematic retrofit design methodology for non- mass transferbased operations has been established.
A new graphical tool called
concentratio n block diagram (CBD) has been introduced to diagnose,
retrofit and evolve the existing water network.
iv. A new two-stage systematic technique for the retrofit of water network
with existing regeneration unit(s) optimisation has been developed. The
first stage of the retrofit task was to locate the various retrofit targets,
where utility savings and capital investment were determined for a range
of process parameters (flowrate increment or outlet concentration
reduction of the existing regeneration unit).
Next, the existing water
network was re-designed to achieve the chosen targets.
v. A new systematic retrofit methodology, which incorporates new
regeneration unit(s) into water network retrofit has been developed. In the
targeting stage, retrofit targets (utility savings and capital investment)
were determined for a range of process parameters (total flowrate and/or
outlet concentration of the new regeneration unit) to obtain a savings
versus investment curve. Lastly the existing network was retrofitted to
meet the targets.
1.7
Summary of This Thesis
In this thesis, a set of new systematic targeting and design techniques for the
retrofit of water network have been developed.
The basic concept of pinch
technology utilised for retrofit of heat integration and mass integration has been
extended to retrofit of water network.
Chapter 2 provides a review of the relevant theories of this thesis related to
the development in pinch technology for heat exchange network, mass exchange
network and water network.
9
A review of the relevant literatures of this thesis is provided in Chapter 3.
The development of pinch technology for heat exchange network, mass exchange
network and water network are reviewed.
Mathematical approaches for heat
integration are also covered in these chapters.
Chapter 4 gives an overview of the new retrofit methodologies for water
network developed in this work. Two new methods for retrofit water network are
discussed. These involve retrofit with mass transfer-based and of non- mass transferbased operations. Retrofit targeting and design procedure for water network with
mass transfer-based operations, which includes capital and operating costs
constraints are presented.
For water network with non- mass transfer-based
operations, only network design is described since no equipment investment other
than those for pipework modifications is usually required during retrofit.
The methodologies for water network retrofit with optimisation of existing
regeneration units and addition of new regeneration units are also discussed in
Chapter 4.
During retrofit targeting, various retrofit alternatives based on the
different combinations of constraints to establish the optimum retrofit targets are
examined. To achieve the targets, retrofit design is then conducted.
The detailed methodologies for retrofit of water network as well as the
analysis and discussions of the results of applying the systematic retrofit techniques
on different case studies are presented in Chapter 5.
Chapter 6 concluded the thesis by summarising the main points and
contributions discussed and exploring the potential area for future development for
water network retrofit.
10
THESIS INTRODUCTION
CHAPTER 2 & 3: FUNDAMENTAL THEORY AND
LITERATURE REVIEW
A review and analysis work on:
§ Heat exchange network retrofit
§ Mass exchange network synthesis and retrofit
§ Water pinch analysis
CHAPTER 4 & 5: METHODOLOGY DEVELOPMENT AND
DISCUSSION
§ Retrofit water network with reuse and recycling
o Retrofit of water network with mass transferbased operations
o Retrofit of water network with non-mass
transfer-based operations
§ Retrofit of water network with regeneration units
optimisation
§ Retrofit of water network with the addition of new
regeneration units
CONCLUSIONS
AND FUTURE WORKS
Figure 1.5: A flow diagram illustrating the conceptual link between the chapters
CHAPTER 2
FUNDAMENTAL THEORY
2.1
Introduction
Pinch technology was initially developed for the optimal synthesis of heat
exchange network (HEN). Since its establishment in 1970’s, its application in heat
exchange network synthesis (HENS) for grassroots and retrofit design has become
well developed. During late 1980’s, the concept of HENs has been successfully
applied for mass exchange networks (MENs). In mid 1990’s, Water Pinch Analysis
(WPA), which is a special case of MENs was introduced. However, there are still
rooms for improvement in the WPA, particularly for retrofit problems. This chapter
begins with the description of the fundamental theory for HEN retrofit.
The
extension of Pinch Analysis principles for MENs synthesis and retrofit is reviewed
next. The last part of this chapter focuses on the established principles of WPA
techniques.
2.2
Process Synthesis
Process synthesis may be defined as (Westerberg, 1987): “ the discrete
decision- making activities of conjecturing which of the many available component
parts one should use, and how they should be interconnected to structure the optimal
solution to a given design problem.” Therefore, process synthesis involves activities
12
in which the process elements are integrated and the flowsheet of the system is
generated to meet certain objectives.
Without a systematic approach of process synthesis, a designer normally
synthesise process alternatives based on experience and corporate preference. The
designer will select the flowsheet with the most promising economic potential and
used it as the ‘optimum’ solution of the problem. By doing this, a designer risk
missing the true optimal design of a problem.
Two main synthesis approaches which can be applied to determine the
optimum solution (El- Halwagi, 1997) are the structure- independent and structurebased techniques. The structure-independent, also known as targeting approach is
based on tackling the synthesis task in a sequence of stages. Within each stage, a
design target is identified and being used in subsequent stages.
This approach
reduces the problem dimensionality to a manageable size and also offers valuable
insights into the system performance and characteristics. The second category of
process synthesis strategy is structure-based. It involves the development of a
framework that embeds on potential configuration of interest. Examples of this
framework include process graph, state-space representations and superstructures.
A hierarchical approach of process design cycle will be a useful tool to obtain
the optimum design for a flowsheet. Smith (1995) established another hierarchical
approach of process design called the Onion Model (Figure 2.1). The process design
begins from the reactor, which is position in the centre of the onion. The optimum
reactor design strongly depends on the optimisation of reactor yield and conversion.
The reactor design influences the recycle and separation structure of the flowsheet.
The heat exchanger network is designed after the reactor, recycle and separation
system design are fixed.
In the last stage, process utility system is designed to
provide additional heating and cooling requirements that cannot be satisfied through
heat recovery.
13
Reactor
Separation and
Recycle System
Heat Exchanger
Networks
Utilities
Figure 2.1: The onion diagram for process design (Smith, 1995)
2.3
Pinch Analysis
Pinch analysis was initiated for HENS in the late 1970s. Application of pinch
analysis has been well-established for HENS as well as mass exchange network
synthesis (MENS). In the former, transfer of energy from a set of hot streams to a set
of cold streams is optimised in order to maximise the heat recovery and energy
efficiency in a process plant (Linnhoff et al., 1982). MENS on the other hand is
concerned with gene ration of a cost effective network of mass exchangers.
In the mid 1990s, water minimisation or water pinch analysis (WPA), a
special case of MENS was introduced. WPA is a systematic procedure to achieve
maximum water recovery through water reuse, recycle and regeneration. The role of
WPA is to efficient use of water through minimum water consumption.
14
2.4
Retrofit of Heat Exchange Network Using Pinch Analysis
A systematic technique for HEN retrofit using pinch analysis was first
introduced by Tjoe and Linnhoff (1986). The objective is to make use of the existing
area in the most efficient manner.
This retrofit method consists of two stages,
namely targeting and network design.
In the targeting stage, the optimal level of heat recovery and the total area
required for a range of ∆Tmin is achieved via heat composite curves and are targeting
methods. Doing this, an area versus energy recovery diagram can be formed (Figure
2.2). A retrofit path in this diagram can be constructed based on the concept of
surface area efficiency, a which is defined as
 Areat arg et 

α Area = 

Area
existing  Energy

(2.1)
where the energy consumption is attained with the existing area, Aexsiting in the
network, but could have been obtained with the area according to target, Atarget.
However, when a is very low (i.e. a < 0.9), the usage of an incremental value of ∆a =
1 is recommended (Ahmad and Polley, 1990). ∆a is defined as:
 ∆Area t arg et 

∆α Area = 

∆
Area
existing  ∆ Energy'

(2.2)
where ∆Areaexisting is the new area installed in retrofit for reducing the energy
consumption by ∆Energy and ∆Areatarget is the minimum targeted new area needed to
reduce energy by ∆Energy.
15
Targeted retrofit design
with constant a
Targeted retrofit
design with ∆a = 1
Area
∆Area
Optimum
grassroots
design
∆Energy
Existing
design
Energy
Figure 2.2: Area versus energy diagram
As can be seen in Figure 2.2, the energy consumption is reduced and
additional area is added when we move towards the left of this diagram. A savings
versus investment plot (Figure 2.3) can be attained by converting the additional area
into capital investment cost and the reduction of energy consumption into savings in
the operating cost for every point on the retrofit profile. By specifying an acceptable
payback period or investment limit, a global ∆Tmin in accordance with these
economic criteria can be determined (Tjoe and Linnhoff, 1986).
Payback period
1 year
2 years
5 years
Saving
per year
Best retrofit
Investment
Figure 2.3: Savings versus investment diagram
16
During retrofit, it is desired to achieve a network structure that meets the
economics targets. Firstly, the existing network is drawn on grid diagram using the
identified ∆Tmin from the targeting stage. Using this diagram, heat exchangers
crossing the pinch are eliminated. Next, the network is retrofitted by reusing the
existing exchangers eliminated in the pervious step and positioning new exchangers
to meet the targeted heat recovery. Lastly, the network is improved via heat loadloops and paths.
During network design, one will have to make use of the existing area and
new heat exchangers as efficiently as possible. It is also proposed that the surface
area efficiency of the resulting design should not be poorer than the original surface
area efficiency.
2.5
Mass Exchange Network
2.5.1
What is A Mass Exchanger?
A mass exchanger is any direct contact mass transfer unit that uses a mass
separating agent (MSA) (or a lean phase) to selectively remove certain components
from the waste streams (or a rich phase). The MSA should be partially or totally
immiscible in the rich phase. Whe n the two phases are in intimate contact, the solute
are redistributed between the two phases and causes depletion in the rich phase and
enrichment in the lean phase (El- Halwagi, 1997).
Figure 2.4 shows a rich (waste) stream, i, with a flowrate Gi. Its content of
the pollutant must be reduced from an inlet composition, yin i to an outlet
composition, youti. An MSA (lean stream), j (whose flowrate is Lj, inlet composition
is x in j and outlet composition is x outj) flows countercurrently to selectively remove the
pollutant.
17
Rich (Waste) Stream
Flowrate : Gi
Inlet composition : yi in
Outlet
composition : yj out
Mass Exchanger
Lean (MSA) Stream
Flowrate : Lj
Inlet composition : xj in
Outlet
composition : xi out
Figure 2.4: A schematic representation of a mass exchanger
Various flow configurations may be adopted but emphasis will be given to
the countercurrent system because of its efficiency and industrial importance.
Separation systems that fall under the category of mass exchange operations include
adsorption, absorption, extraction, ion exchange, leaching and stripping.
2.5.2
Sizing and Costing of Mass Exchanger Unit
The main objective of a mass exchanger is to provide adequate surface
contact for the rich and the lean phases. Such contact can be accomplished by using
various types of mass exchanger units and internals. In particular, there are two
primary categories of mass exchange devices: multistage and differential contactors.
In multistage mass exchanger, each stage provides intimate contact between the rich
and lean phases followed by phase separation. With sufficient mixing time, the two
phrases leaving a stage are essentially in equilibrium.
In order to determine the size of a multi-stage mass exchanger, one should
consider the isothermal mass exchanger (Figure 2.4). One way of calculating the
number of equilibrium stages (or number of theoretical plates, NTP) for a mass
exchanger is the graphical McCabe-Thiele method. To illustrate this procedure, let’s
assume that over the operating range of composition, the equilibrium relation
governing the transfer of the pollutant from the rich stream to the MSA can be
presented by the linear expression described by the following equation:
18
yi = mj x*j + bj,
(2.3)
Equation 2.3 indicates that for a waste stream of composition yi, the
maximum theoretically attainable composition of the MSA is x*j.
A material
balance on the pollutant that is transferred from the waste rich stream to the MSA
may be expressed as
Gi (yiin – yiout) = Lj (xjout – x jin )
(2.4)
On a y- x (McCabe-Thiele) diagram, this equation represents the operating
line which extends between the points (yiin , x jout) and (yiout, x jin ) and has a slope of
Lj/Gi, as shown in Figure 2.5. Furthermore, each theoretical stage can be represented
by a step between the operating line and the equilibrium line. Hence, NTP can be
determined by “stepping off” stages between the two ends of the exchanger.
yi,in
y
Operating line
Equilibrium line
yi,out
xj,in
xj,out
x
Figure 2.5: The McCabe Thiele diagram
Alternatively, for the case of isothermal, dilute mass exchange with linear
equilibrium, NTP can be determined through the Kremser (1930) equation:
 m j Gi  yiin − m j x inj − b j
 out
ln 1 −

L
− m j xinj − b j

j  yi
NTP =
 L 
ln  j 
 m jGi 
 m jGi 
+


L j 

(2.5)
19
Equation 2.6 represents another form of the Kremser equation:
,*


L  xiin − x out
L 
j
+ j 
ln 1 − j  out
out ,* 
 m j Gi  x j − x j  m jGi 
NTP = 
m G 
ln  j i 
 Lj 
(2.6)
where
x
out ,*
j
=
yiin − b j
mj
(2.7)
Also,
 Lj 


=
yiout − m j xinj − b j  m j Gi 
yi − m j x j − b j
in
out
NTP
(2.8)
If contact time is not enough for each stage to reach equilibrium, one way to
calculate the number of actual plates “NAP” is by incorporating contacting
efficiency.
Two principal types of efficiency may be employed: i.e. overall
efficiency and stage efficiency. The overall exchanger efficiency, ηo , can be used to
relate NAP and NTP as follows:
NAP =
NTP
ηo
(2.9)
The stage efficiency may be defined based on the rich phase or the lean
phase. For instance, when the stage efficiency is defined for the rich phase, ηy,
Equation 2.5 becomes
 m G  yiin − m j x inj − b j  m jGi 
+
ln 1 − j i  out

L j  yi − m j xinj − b j 
L j 

NTP =

 m G   
− ln 1 + η y  j i  − 1 

 L j   
(2.10)
20
The second type of mass-exchange units is the differential (or continuous)
contactor. In this category, the two phases flow through the exchanger in continuous
contact without intermediate phase separation and re-contacting.
Examples of
differential contactors include packed columns, spray towers and mechanically
agitated units.
The height of a differential contactor, H, may be estimated as follows:
H = HTUyNTUy
(2.11)
= HTUxNTUx
(2.12)
where HTUy and HTUx are the overall height of transfer units based on the rich and
lean phases, respectively, while NTUy and NTUx are the overall number of transfer
units based on the rich and lean phases, respectively. The overall height of a transfer
unit may be provided by the packing (or unit) manufacturer or estimated using
empirical correlations (typically by dividing superficial velocity of one phase by
overall mass transfer coefficient). On the other hand, the number of transfer units
can be theoretically estimated for the case of isothermal, dilute mass exchangers with
linear equilibrium as follows:
NTPy =
yiin − y iout
( yi − yi* ) logmean
(2.13a)
where
( y i − yi* ) logmean =
out
( yiin − m j x out
− m j xinj − b j )
j − b j ) − ( yi
 yiin − m j x out
j − bj
ln  out
 y − m x in − b
j j
j
 i




(2.13b)
and
NTPx =
x inj − x out
j
( x j − x *j ) logmean
(2.14a)
21
where

 y ini − b j   in  yiout − b j
out

 − x −
x
−
 j
 m   j  m

j
j

 

*

( x j − x j ) logmean =

 yiin − b j  
out
 
 x j − 

 
m
 
j


ln 

out
  in  yi − b j  
 x j −  m
 
j


 






(3.14b)
If the terminal compositions or Lj /Gi are unknown, it is convenient to use the
following form:
 m G  yiin − m j x inj − b j
ln 1 − j i  out
L j  yi − m j xinj − b j

NTP =
mG 
1 −  j i 
 Lj 
 m jGi 
+


L j 

(2.15)
The column diameter depends on the flowrate and properties of the streams
passing through it. Each column should be wide enough for the vapour velocity to
be below that would cause excessive liquid entrainment or high pressure drop. The
following equation is determined to estimate the maximum allowable gas velocity,
umax,
u max = ( −0.171S 2 + 0.27 S − 0.047 )
ρl − ρv
ρv
(2.16)
where S is the tray spacing (m) and ?l and ?v are liquid and vapour density (kg/m3 ),
respectively.
The actual gas velocity, uv is taken as 80% of umax. The diameter of the
column is presented as:
22
D=
4Gm
πρv u v
(2.17)
where Gm refers to the gas flowrate in kg per second.
The tray spacing, S (m) which normally depends on the column diameter (m)
is recommended as (Ulrich, 1984),
S=1
S = 0.5D0.3
for 0 < D < 1
(2.18a)
for D = 1
(2.18b)
The column height, H is determined by multiplying the number of real trays,
Nr by the tray spacing, S and adding and inactive height of 3m to account for vapour
disengagement space and liquid sump (Ulrich 1984) as presented by the following
equation,
H = (Nr * S) + 3
(2.19)
The capital cost of a column is related to the shell and trays of the column.
The shell and trays are cost separately depending on the column’s height and
diameter. The following are the equations for column capital cost (Caulson, 1993).
Capital cost for shell
= £ 6400 H0.95 D0.6
(2.20)
Capital cost for each tray
= £ 304e0.8 D
(2.21)
23
2.5.3
Grassroots Synthesis of Mass Exchange Network
In many industrial situations, there are several rich streams (sources) from
which mass has to be removed, and many mass separating agents (MSAs) that can be
used for removing the targeted species. The problem of selecting, designing and
operating a mass exchange system should not be confined to assessing the
performance of individual mass excha nger. Therefore, a mass exchange system is
selected and designed by screening all candidate mass exchange operations to
identify the optimum system with significant technical and economical benefits.
The problem of synthesising MENs can be stated as follows (El-Halwagi, 1997):
Given a number NR of waste (rich) streams (Sources) and a number NS of
MSAs (lean streams), it is desired to synthesise a cost-effective network of
mass exchangers that can preferentially transfer certain undesirable species
from the waste streams to the MSAs. Given also are the flowrate of each
waste stream, Gi, its supply (inlet) composition ysi, and its target composition
yti, where i = 1,2,…,NR. In addition, the supply and target compositions, x sj
and x tj, are given for each MSA, where j = 1,2,…, NS . The flowrate of each
MSA is unknown and is to be determined so as minimise the network cost.
Figure 2.6 is a schematic representation of the problem statement.
The candidate lean streams can be classified into NSP process MSAs and NSE
external MSAs (where NSP + NSE = NS ). The process MSAs already exists on
plant site and can be used for removal of the undesirable species at a very low
cost (virtually free). The flowrate of each process MSA that can be used for
mass exchange is bounded by its availability in the plant and may not exceed
Lcj. On the other hand, the external MSAs can be purchased from the market.
This flowrate are to be determined according to the overall economic
considerations of the MEN.
24
MSA (Lean Streams) In
Waste
(Rich)
Streams
(Sources)
In
Mass
Exchange
Network
Waste
(Rich)
Streams
(Sources)
Out
MSA (Lean Streams) Out
Figure 2.6: Schematic representation of the MEN synthesis problem
The target composition of the undesirable species in each MSA is obtained
based on the specific circumstances of the application.
The nature of the
circumstances may be physical (e.g. maximum solubility of the pollutant in the
MSA), technical (e.g. to avoid excessive corrosion, viscosity or fouling),
environmental (e.g. to comply with environmental regulations), safety (e.g. to stay
away from flammability limits), or economic (e.g. to optimise the cost of subsequent
regeneration of the MSA).
In this proposal, several mass exchange operations will be considered
simultaneously. It is emphasised that the terminology used is the same as in ElHalwagi (1997) where y always refers to the composition in the rich streams and x
always refers to the composition in the lean streams.
2.5.3.1 The Targeting Approach for Mass Exchange Network
In MENs synthesis, two useful targets had been established. These are the
minimum cost of MSAs and minimum number of mass exchangers units.
El-Halwagi and Manousiothakis (1989a) developed minimum cost of MSAs
targeting approach which is analogous to the minimum energy target in HENS. In
order to minimise the cost of MSAs, it is important to make maximum use of process
25
MSAs before considering the application of external MSAs. The MENs targeting
can be carried out graphically using the “pinch diagram”.
The thermodynamic
limitations of mass exchange must be considered in the application of the process
MSAs and this is accounted in this targeting approach.
The initial step in constructing the pinch diagram is to specify the minimum
allowable composition difference, ε.
This is important to ensure feasible mass
transfer throughout the networks and it is similar to ∆Tmin used in HENs. Let us
consider a mass exchanger for which the equilibrium relation governing the transfer
of pollutant from the waste stream, i, to MSA, j, is given by the linear expression
yi = mj x*j + bj,
(2.22)
which indicates that for a waste stream of composition yi, the maximum theoretically
attainable composition of the MSA is x*j.
By using a minimum allowable
composition difference of ε j, one can draw a “practical- feasibility line” that is
parallel to the equilibrium line but offset to its left by a distance ε j (Figure 2.7).
y
Practical Feasibility Region
Practical
Feasibility
Line
εj
εj
Equilibrium Line
x*j = (y – b j ) / mj
xj
Figure 2.7: Establishing the corresponding composition scales
26
It is important to derive the mathematical expression relating yi and x j on the
practical feasibility line. For a given yi, the value of x j can be obtained by evaluating
x*j that is equilibrium with yi then subtracting ε j,
x j = x*j – ε j
(2.23)
Substituting (2.23) into (2.22), one obtains
yi = mj (x j – ε j) + bj,
(2.24a)
or
xj =
yi − b j
mj
−ε j
(2.24b)
Equation (2.24) can be used to generate a one-to-one correspondence among
all composition scales for which mass exchange is feasible.
According to El-
Halwagi (1997), since most environmental applications involve dilute systems, one
can assume that these systems behave ideally. Hence, the transfer of pollutant is
different to the existence of other species in the waste stream.
It is assumed that the stream flowrates remain constant in MENs.
This
assumption is reasonable when relatively small composition changes are required or
if some counter diffusion is assumed to happen (El- Halwagi and Manousiouthakis,
1989a). However, for cases where the flowrate do change significantly, one should
use the flowrate and the composition of the inert (non-transferred) components in
each stream instead of the whole stream.
The initial step in constructing the pinch diagram is to create a global
representation for all the rich streams. This global representation is accomplished by
plotting the mass exchanged by each rich stream versus its composition. Having the
representation the individual rich streams, one can now construct the rich composite
stream. A rich composite stream represents the cumulative mass of the pollutant loss
27
by all the rich streams and is obtained by applying linear superposition to all the rich
streams (Figure 2.8).
y
y
R1
R2
R1
R1 + R2
R1
Mass
Mass
Figure 2.8: Construction of the rich composite curve
Construction of the lean composite curve is not as straighfoward. Because
each MSA has its own equilibrium relation, the lean stream composites are not
equivalent.
El-Halwagi and Manousiouthakis (1989a) introduced the concept of
corresponding composite scales in order to consider all MSAs on a basis. This tool
established a one-to-one correspondence among the compositions of all streams for
which mass transfer is thermodynamically feasible. This correspondence depends on
the equilibrium relation and ε value for each MSA. As shown in Figure 2.9, each
MSA composition, x j is mapped as a corresponding y value and this allow all MSAs
to be represented on the same plot.
Note that this accounts for driving force
considerations since ε values are included in this transformation. For this purpose,
the MSA flowrates are initially set at their maximum values, Lcj.
Next, both composite curves are plotted on the same set of axes. The lean
composite curve is shifted horizontally until it touches the rich composite curve (see
Figure 2.10).
The point where the two composite curves touch is called mass
transfer pinch and it is the thermodynamic bottleneck for mass transfer between
process streams.
28
x2 x1 y
x2 x1 y
S2
S2
S1 + S2
S1
S1
Mass
Mass
Figure 2.9: Construction of the lean composite curve
The vertical overlap between the composite curves shows the maximum
amount of pollutants that can be transfered from the rich streams to the process
MSAs. The horizontal distance of the lean composite curve which extends past the
rich composite curve represents the excess capacity of the process MSAs.
It
corresponds to the capacity of the process MSAs to remove pollutants that cannot be
used due to thermodynamic infeasibility. This can be overcomed by lowering the
flowrate and/or composition of the one or more process MSAs. The distance by
which the rich composite curve extends past the lean composite curve shows the
mass that needs to be removed by an external MSA. This quantity can then be used
to give the minimum cost of MSAs required.
Excess capacity of
process MSAs
x2 x1 y
Rich composite curve
Mass transfer pinch
Lean composite curve
Load for external MSA
Mass
Figure 2.10: Mass composite curves
29
The mass transfer pinch decompose the synthesis problem into two regions:
above the pinch (containing all streams or parts of streams richer that the pinch
composition) and below the pinch (containing all streams or parts of streamd leaner
that the pinch composition). Above the pinch, only process MSAs are required.
However, external MSAs are needed below the pinch. In order to meet the minimum
MSA target, no mass should be transferred across the pinch. In other words, external
MSAs should not be used above the pinch.
Another useful target in MENs synthesis is the minimum number of mass
exchanger units. In order to be consistent with the minimum MSA target, the pinch
division is taken into account. That is:
N units, pinch = (S’ – 1) Above pinch + (S’ – 1) Below pinch
(2.25)
where S’ now refers to the total number of rich and lean streams (including external
MSAs).
According to El- Halwagi and Manousiouthakis (1989a), this target attempts
to minimise indirectly the capital cost of the network, since the cost of each mass
exchanger is usually a function of the unit size. It is also desirable from a practical
point of view.
2.5.3.2 Network Design
For MENs, network design is carried out using a grid diagram (El- Halwagi
and Manousiouthakis, 1989a).
An example is presented in Figure 2.11.
Rich
streams are drawn running from right to left and lean streams the opposite way.
Stream flowrates and equilibrium constants are also shown.
Composition (mass fractions in this case) are shown above each stream.
Exchangers are represented as a pair of joined circles with the amount of mass
30
transferred shown below. Notice that the lean stream compositions at the pinch are
different due to the inequality of the equilibrium relations.
Flowrate
(kg/s)
Pinch
0.01
0.0168
0.006
0.0168
0.05
1
0.03
R1
2
R2
1
mj
S1
0.005
S2
S3
0
0.0074
0.015
0.01
0.03
0.0132
0.01
5
2
2.08
1.53
0.1133
0.02
Figure 2.11: A mass exchange match show on a grid diagram
In order to meet the minimum MSA target, the region above and below the
pinch are designed separately with no mass being transferred across the pinch.
Design should start at the pinch and move away from it as the pinch is the most
constrained part of the network. Matches made at the pinch have a driving force
equal to ε j.
There are two feasibility criteria for matching streams at the pinch. They are:
1.
Stream population
Immediately above the pinch,
NR, above pinch ≤ NS, below pinch
(2.26)
and immediately below the pinch,
NR, above pinch ≥ NS, below pinch
(2.27)
31
2.
Operating line versus equilibrium line
Consider a match made immediately above the pinch. An example is
shown as an operating line in Figure 2.12 (a). The relevant equilibrium
line is also shown. Now, at the pinch, the composition difference is
exactly ε j. If this is to be the minimum driving force, the operating line
and equilibrium line must diverge away from the pinch. Thus the slope
of the operating line should be greater than or equal to the slope of the
equilibrium line or:
(Lj / mj) above pinch ≤ Gi, above pinch
(2.28)
Immediately below the pinch, the opposite must be true (Figure 2.12b):
(Lj / mj) above pinch ≥ Gi, above pinch
y
yi,in
(2.29)
y
Operating line
Slope = Lj /Gi
yi,in
εj
Operating line
Slope = Lj /Gi
Equilibrium line
Slope = mj
yi,out
εj
xj,in
Equilibrium line
Slope = mj
yi,out
Pinch
xj,out
(a)
x
Pinch
xj,in
xj,out
x
(b)
Figure 2.12: (a) Match feasibility immediately above the pinch (b) Match feasibility
immediately below the pinch
32
2.5.4
Retrofit Synthesis of Mass Exchange Network
During early 2000’s, the concept of HENs retrofit has been successfully
applied for MENs retrofit (Fraser and Hallale, 2000). With this adaptation, achieving
the best savings in operating costs, subject to a minimum payback period or a
minimum capital expenditure has became the aim for the retrofit of MENs.
Retrofit of MENs also consists of two stages. In the targeting stage, the
established grassroot methods for MENs to determine utility (MSA) targets (ElHalwagi and Manousiouthakis, 1989) and number of stages (Hallale and Fraser,
1989a) are applied. The savings versus investment curve for MEN retrofit is desired
from the stage versus load plot (Figure 2.13) which is the analog of the area versus
energy plot. Figure 2.13 shows total number of stages versus the mass load of the
external MSA.
In the stages versus load diagram, a retrofit path is then chosen, which allows
one to determine the savings achieved for an increment in mass exchanger size. This
retrofit path is constructed from the plant current design by assuming a constant stage
efficiency, a stage between the plant design and the target. astage is defined as,
 Staget arg et 

α Stage = 
 Stageexisting 

 MassLoad
(2.30)
Costing this yields the same savings versus investment diagram as in the case
of HENs retrofit.
Lastly, elimination of cross-pinc h mass exchangers and
appropriate use of driving force are utilised as the key principles for the retrofit
design stage.
33
Targeted retrofit design
with constant a
Optimum
grassroots
design
Stage
Existing
design
Mass Load
Figure 2.13: Stage versus mass load diagram
2.6
Water Pinch Analysis
2.6.1
Water Pinch Analysis Concept
The process industry contributes substantially to the world economy with
annual production exceeding $5 trillion, creating a significant economical and
environmental incentive for promoting water reuse and wastewater minimisation in
this industry (Mann and Liu, 1999).
Water is mainly used for process uses and utility uses in a manufacturing
facility. Currently, it is quite common to segregate wastewater streams and treat
each one separately with the most effective technique in a distributed effluenttreatment system to a form suitable for discharge to the environment.
Water pinch technology represents a systematic approach for the
optimisation of industrial water reuse, wastewater minimisation and effluenttreatment system design. The technology comprises three areas:
34
(1)
Water-pinch Analysis – Identifying, a priori, targets for minimum fresh
water consumption and minimum wastewater generation in water- using
operations.
(2)
Water-pinch Synthesis – Designing a water-using network that achieves
these targets through water reuse, regeneration and recycle.
(3)
Water-pinch Retrofit – Modifying an existing water-using network to
maximise water reuse and minimise wastewater generation through
effective process changes.
2.6.2
Types of Water-using Operations
Water-using operations in chemical process plants can be classified into two
main categories. The first category is the mass transfer-based water- using operations
and the second group is the non- mass transfer-based water- using operations.
2.6.2.1 Mass Transfer-based Water-using Operations
A mass transfer-based water-using operation is characterised by the
preferential transfer of species from a rich stream to water, which is being
utilised as a lean stream or a mass separating agent (MSA) (Manan et al.,
2004a). A typical example of such operation is the cleaning of a process
vessel using fresh or recycle water. During cleaning, water is fed into the
vessel (as a demand) while wastewater is generated (as a source) as shown in
Figure 2.14 (a). Another example of the mass transfer-based water-using
operation is the absorption process where water is the MSA used to remove
contaminants such as H2 S and SO2 from a sour gas stream ( Figure 2.14b).
35
Water for
vessel
washing
Sweetened
gas
Water as absorption
solvent
Wastewater
generated
from
Vessel
vessel washing
Absorption
column
Sour gas
Water to
regeneration unit
(a)
(b)
Figure 2.14: Mass transfer-based water- using operations : (a) Vessel washing;
(b) Sour gas absorption where water demand and water source exist
For a given set of constraints on water reuse, one can identify the minimum
fresh water flowrate for this operation by using the limiting water profile, through a
plot of contaminant versus mass load. In order to maximise the possibility of water
reuse from other operations, one needs to specify water with the highest possible
inlet and outlet concentration. Figure 2.15 (b) represents the limiting water profile
for a mass transfer-based water-using process in Figure 2.15 (a). Any water supply
line, which is below the limiting water profile will meet the requirements of the
process.
C
CPROC,IN
CPROC,OUT
PROCESS
CPROC,OUT
CPROC,IN
(CW,OUT)max
PROCESS
CW,IN
CW,OUT
(CW,IN)max
(a)
Limiting Water
Profile
(b)
Figure 2.15: (a) A mass transfer-based water- using process (b) Limiting water
profile
m
36
2.6.2.2 Non-mass Transfer-based Water-using Operations
The non-mass transfer-based water- using operation covers functions of water
other than as a mass separating agent (Manan et al., 2004a). A typical example
includes water being fed as a raw material, or being withdrawn as a product or a byproduct in a chemical reaction (Figure 2.16). The non- mass transfer-based operation
also covers cases where water is being utilised as heating or cooling media. For such
operations, usually, only water demands or water sources exist as shown in Figure
2.17. Note that, for the non- mass transfer-based water-using operations, the water
flowrate is more important than the amount of contaminant accumulated.
O2
C6 H5NO2
Fe
C6 H5NH2 +
Fe 3 O4
NH3
AN + H 2 O
C3 H6
H2 O
(a)
(b)
Figure 2.16: Non- mass transfer-based water-using operations: (a) a reactor that
consumes water in aniline production; (b) a reactor that produces water as a
byproduct in acrylonitrile (AN) production
Cooling tower
make-up water
Cooling
tower
Boiler
(a)
(b)
Boiler
blowdown
Figure 2.17: Two other common types of the non-mass transfer-based water- using
operations: (a) cooling tower make up; (b) boiler blow-down
37
2.6.3
Targeting Approach for Maximum Recovery Network through Reuse
and Recycle
There are a few WPA targeting approaches for maximum water recovery
through reuse and recycle. They include limiting composite curve by Wang and
Smith (1994), water surplus diagram by Hallale (2002) and Water Cascade Analysis
by Manan et al. (2004a). The details about these approaches will be discussed.
2.6.3.1 Limiting Composite Curve
Limiting composite curve is a graphical targeting approach used to determine
the minimum fresh water requirements for a water system via reuse and recycle
(Wang and Smith, 1994). The basic concept underlying this approach is that the
water-using processes are modelled as mass transfer operations.
To construct a limiting composite curve, all the water using operations is
plotted individually on a contaminant concentration (C) versus mass load (m) graph
according to their inlet and outlet concentration and mass load removed (see Figure
2.18a). By drawing horizontal lines at the inlet and outlet concentrations for each
operation, the contaminant concentration (y-axis) is divided into intervals.
Next, the mass loads of all water-using operations present between
concentration intervals are summed to draw a composite line corresponding to the
sum of all water using operations which exists between the intervals in question. By
repeating this step for all the other concentration intervals and connecting them
together, the limiting composit e curve is constructed (Figure 2.18b).
38
C
Concentration
interval
C
C
pinch
Water
supply line
m
(a)
m
(b)
m
(c)
Figure 2.18: Construction of the limiting composite curve (a) Limiting water profile
(b) Limiting composite curve (c) Matching the water supply line to determine the
minimum targets
Figure 2.18 (c) shows the water supply line touches against the limiting
composite curve and create a pinch in the design. The water supply line must lie
below the limiting composite curve so that there is always a contaminant
concentration differential that will allow for mass transfer of the contaminant from
the process stream to the water stream. The starting point of the water supply line is
zero as fresh water is utilised. To achieve minimum fresh water consumption and
wastewater generated, the outlet concentration of the water supply line is maximised.
2.6.3.2 Water Surplus Diagram
Water surplus diagram is another graphical tool that considers water reuse
and recycle to target the minimum fresh water consumption and wastewater
generation in a water recovery network (Hallale, 2002). This tool can be used for
water-using processes that are modelled as mass transfer operations and also waterusing processes that are modelled as non- mass transfer operations. However, it is a
trial-an-error method.
39
The first step of this tool is to plot the demand and source composite curves
with the water purity as the y-axis and the flowrate as the x-axis using the limiting
water data (Figure 2.19). An initial value of fresh water flowrate has been assumed
and is included in the source composite curve.
Guess a fresh water value
Water Purity
Demand composite curve
Source composite curve
Surplus
Deficit
Flowrate
Figure 2.19: Source and demand composite
Next, two criteria for feasibility of the assumed fresh water flowrate are
tested. The first is that the total water sources flowrate should be equal or greater
than the total flowrate of water demands. This can be achieved by inspecting the
source and demand composite curve in Figure 2.19. As shown, the source composite
extend to the right of the demand composite which meet the first criterion. The other
criterion is to ensure sufficient pure water at all points in the water network. To test
this criterion, the water surplus diagram is needed.
To construct water surplus diagram, it should be noticed that there are regions
(rectangles) where the composite curves are above or below one another in Figure
2.19. When the source composite is above the demand composite, this is a region
with a surplus of pure water as indicated by a positive sign. On the other hand, there
is a deficit of pure water in a region when the source composite is above the demand
composite (negative sign). The pure water surplus and deficit in each region can be
determined by calculating the area enclosed by each rectangle.
40
The calculated pure water surplus and deficit values are then plotted against
the water purity to form the water surplus diagram (Figure 2.20). If the water surplus
diagram touches the y-axis, it means that the initial value of fresh water flowrate is
the minimum fresh water target. However, if part of the water surplus diagram falls
on the negative region, there is not sufficient water purity in the network and more
fresh water must be added. On the other hand, if the water surplus diagram falls on
the positive region without touching the y-axis, it shows that there is surplus of fresh
water in the network and less fresh water is required. Therefore, when the water
surplus diagram with the initial water flowrate does not touch the y-axis, all the
above steps have to be repeated for different fresh water flowrate. The summary of
the graphical targeting procedure by water surplus diagram is presented in Figure
2.21.
Figure 2.20: The surpluses and deficits are plotted to form the water surplus diagram
41
Estimate an external
fresh water flowrate
Draw the water demand and
source composite curves
Calculate the area between
two composite curves
Draw the water surplus
diagram
Does the water surplus
diagram touch the y-axis ?
Yes
The estimated fresh water
flowrate is the minimum va lue
No
Need a smaller
fresh water
flowrate
Need a larger
fresh water
flowrate
Yes
No
Does the water surplus diagram
appear in the region to the right of yaxis?
Figure 2.21: Summary of targeting procedure by water surplus diagram
2.6.3.3 Water Cascade Analysis
In order to eliminate the trial-an-error steps and compliment the graphical
method, a numerical equivalent of the water surplus diagram similar to the
composition interval table in mass integration known as Water Cascade Analysis
(WCA) has been developed (Manan et al., 2004a).
The first step in the WCA is to set up the interval water balance table (Table
2.1) to determine the net water source or water demand at each purity level. The first
column of Table 2.1 contains the contaminant concentration levels (C) arranged in
ascending order. Each concentration level is expressed in terms of the water purity
(P) in the second column. With the concentration of pure water set at one million
ppm, the fraction of pure water in a contaminated stream, or the water purity, can be
expressed as:
Purity, P =
1000000 − C
1000000
(2.31)
42
where:
C = contaminant concentration in ppm.
The number of purity intervals (n) equals the number of water demands (ND)
and the number of water sources (NS) minus any duplicate purity (NDP ):
n = ND + NS – NDP
(2.32)
Next the water purity difference (∆P) in Column 3 of Table 2.1 is calculated
as the difference between purity level at intervals k and k+1, as follow:
∆P = Pn – Pn+ 1
(2.33)
Table 2.1: Interval water balance table
Concentration
Cn (ppm)
Purity,
Pn
0
1.000000
∆P
ΣFD, j
(ton/h)
ΣFS, i ΣFD, j + ΣFS, i
Net water
source / demand
(ton/h)
(ton/h)
0
-
-435.6
Demand
0.000020
20
0.999980
-435.6
0.000080
100
0.999900
150
0.999850
-169.2
169.2
0
-
-1130.4
1566
435.6
Source
0.000050
0.000010
160
0.999840
-1332
1332
0
-
250
0.999750
-68.4
68.4
0
-
1000000
0
Columns 4 and 5 contain the flowrates for the water demands ( ∑ FD, j ) and
j
water sources ( ∑ FS, i ) at their corresponding purity levels. The flowrate of water
i
demand is fixed as negative, and the water source positive. These flowrates are
summed up at each purity level to give the net interval water flowrate,
∑F
D, j
j
+ ∑ FS, i , column 6); (+) representing net water source, (-) net water demand
i
(column 7).
43
The next key step in the WCA is to establish the fresh water and waste water
targets for the process.
In doing so, it is important to consider both the water
flowrate balance and the concentration driving force (water purity) so that the true
minimum water targets can be obtained. The water flowrate balance involves using
the water cascade diagram shown (Figure 2.22) to get the cumulative net water
source/demand for a process (FC).
Figure 2.22: (a) Water cascade diagram with an assumed fresh water flowrate of 0
ton/h (b) Pure water cascade is used to check the feasibility of the water cascade
(c)
Interval fresh water demand to determine the fresh water amount needed in each
purity interval
For the water cascade diagram in Figure 2.22 (a), a fresh water flowrate (FFW )
of 0 kg/s is assumed. Here, the net water demand of -435.6 ton/h at the second purity
level is cascaded to the forth purity level to meet another water source of 435.6 ton/h,
giving a cumulative net of 0 ton/h. This cumulative value is cascaded to yield
wastewater flowrate (FWW ), of 0 ton/h at the lowest purity level of the water cascade
diagram. The cumulative net water source/demand for the process (FC) at each
purity interval forms the net interval water cascade diagram. The water cascade
diagram is similar to the interval heat balance table for the problem table algorithm
44
in heat integration (Linnhoff et al., 1982) and the table of exchangeable loads for
mass exchange cascade diagram in mass integration (El-Halwagi, 1997).
The water cascade diagram depicting the preliminary water balance (i.e., with
FFW = 0 kg/s) is essential as a basis to generate a feasible water cascade, and
ultimately, the true minimum water targets.
Note again that, in addition to
considering the water flowrate balance, the true minimum targets can only be
realised by also taking into account the pure water surplus or deficit, which is a
product of the cumulative net water source/demand (FC) and the purity difference
(∆P) across three purity levels (Figure 2.22b). A pure water surplus (+) means that
water is available with purity higher than what is required in this region. On the
other hand, a pure water deficit (-) means that water of higher purity than those
available is required (Hallale, 2002). Cascading the pure water surplus/deficit down
the purity intervals yields the pure water cascade that represents the cumulative
amount of pure water surplus/deficit (Figure 2.22b). The cumulative pure water
surplus/deficit at each purity level is a numerical representation of the water surplus
diagram introduced by Hallale (2002).
Notice that all the purity levels (i.e. P1 , P2 and P3 ) of the pure water cascade
in Figure 2.22 (b) consist of cumulative pure water deficits. The deficits on the pure
water cascade, which correspond to the negative region of water surplus diagram,
indicate that the pure water cascade is “infeasible”. These deficits mean that there is
insufficient fresh water in the network and are the result of assuming zero fresh water
flowrate (FFW ) during water cascading. Thus, additional fresh water should be
supplied to remove all pure water deficits and yield a feasible pure water cascade.
Fresh (or pure) water is to be supplied at the highest purity level.
To
minimize fresh water, it is necessary to determine the minimum flowrate of fresh
water, or, the interval fresh water demand that will satisfy the total water
requirement at each purity level. The interval fresh water demand will restore a
feasible pure water cascade throughout the entire water network. Figure 2.22 (c)
shows that the cumulative fresh water flowrate (FFW,cum ) for each purity k is obtained
45
by dividing the cumulative pure water surplus/deficit by the contaminant
concentration, 1 – P as follows,
FFW, cum =
cumulative pure water surplus/de ficit
1- P
(2.34)
Referring to Figure 2.22 (c), a negative FFW,cum means that there is
insufficient fresh water whereas a positive FFW,cum means that there is excess fresh
water at the given purity level. In order to ensure that there is sufficient fresh water
at all points in the network, a fresh water flowrate (FFW ) of exactly the same
magnitude as the absolute value of the largest negative FFW,cum should be supplied at
the highest purity level of a feasible water cascade (Figure 2.23). FFW,cum of -377.52
ton/h found at the forth purity level (P4 ) of the cumulative fresh water cascade in
Figure 2.22 (b) is the largest negative FFW,cum . This quantity of fresh water is added
at the highest purity level of the feasible water cascade in Figure 2.23. Note that a
feasible water cascade is the one that results in positive, or at least, zero cumulative
pure water surplus value in the pure water cascade. The feasible water cascade yields
the true minimum fresh water flowrate target of 377.52 ton/h and exactly the same
amount wastewater flowrate target of 377.52 ton/h.
Figure 2.23: A feasible water cascade
46
At the fourth purity level (P = 0.999850) where there is zero cumulative pure
water surplus, there exists the pinch for the paper mill problem. The pinch is the
most constrained part of the ne twork that results in maximum water recovery. Note
that through the WCA, we have obtained the utility targets ahead of design. The
water cascade and the pure water surplus cascade diagrams can be integrated with the
interval water balance table to form the water cascade table (WCT) (Table 2.2).
Table 2.2: Water cascade table
2.6.4
Targeting Approach for Maximum Recovery Network through Reuse,
Recycle and Regeneration
By coupling reuse and recycle strategies with regeneration, further reduction
of fresh water consumption and wastewater generation in a water network is
possible. Many types of process can be used to regenerate wastewater, e.g. gravity
settling, filtration, membranes etc.
A few WPA targeting approach related to
regeneration has been developed. Here, further discussions will be focused on the
notions of limiting composite curve, water surplus diagram and water cascade
analysis with regeneration.
47
2.6.4.1 Limiting Composite Curve
Through limiting composite curve, the minimum water utility targets which
distinguish between regeneration reuse and regeneration recycling cases can be
determined.
First consider the placement of regeneration process involving water reuse,
which obviously represents a reduction in fresh water flowrate (Figure 2.24a). As
shown water supply line is taken to concentration CREGEN and dropped to Co after
regeneration. It is assumed that the water flowrate before and after regeneration
remains unchanged. This is evident from the same slope of water supply line before
and after regeneration. However, to determine whether the fresh water is minimised
a composite of the water supply lines before and after regeneration is matched
against the limiting composite curve (see Figure 2.24b).
C
C
PINCH
PINCH
CPINCH
CREGEN
CPINCH
CREGEN
Regeneration
CREGEN to Co
Co
Co
Water supply line
Water supply
line
m
(a)
m
(b)
Figure 2.24: Regeneration of water below the pinch for regeneration reuse
Figure 2.25 (a) illustrates the same limiting composite curve from Figure 2.24
except that the water reaches the pinch concentration before regeneration. This
figure seems to be infeasible as the water supply line crosses the limiting composite
curve. However, the overall feasibility is determined by the composite water supply
line before and after regeneration, which is shown in Figure 2.25 (b). Therefore, the
placement of regeneration at pinch concentration is feasible and fresh water is
minimised.
48
When one make comparison on the slope of water before regeneration in
Figure 2.24 (a) and Figure 2.25 (a), it can be seen that the slope of water before
regeneration in Figure 2.25 (a) is steeper than the one in Figure 2.24 (a). This has
proven that regeneration of water at the pinch reduces more fresh water compared to
regeneration of water below the pinch. Therefore, by allowing the water supply line
to achieve pinch concentration before regeneration, a process is able to achieve the
minimum fresh water flowrate and minimum concentration reduction during
regeneration process.
C
C
PINCH
PINCH
CPINCH
CPINCH
Regeneration
Co
Co
Water supply line
Water supply
line
m
(a)
m
(b)
Figure 2.25: Regeneration of water at the pinch for regeneration reuse
If recycling is allowed, the fresh water flowrate can be further reduced
compared to if only reuse was considered. The slope of limiting composite curve
below Co represents the fresh water flowrate requirement for regeneration cases with
recycling (Figure 2.26a). If this fresh water flowrate reaches pinch concentration and
being regenerated, there is insufficient water to satisfy the limiting composite curve
after regeneration. This is because the slope of water supply line before regeneration
is steeper than after regeneration, which indicates that more water flowrate is
required after regeneration. Therefore, the water flowrate after regeneration have to
be increased and can only be done through recycling (Figure 2.26b). The total
flowrate of water being rege nerated refers to the slope of the water supply after
regeneration.
49
C
C
PINCH
PINCH
CPINCH
Co
Regeneration
CPINCH
Water supply line
with unrecycle +
recycle water
m
(a)
Co
Water supply line with
fresh water + unrecycle
+ recycle water
m
(b)
Figure 2.26: Regeneration recycling represented on limiting composite curve
2.6.4.2 Water Surplus Diagram and Water Cascade Analysis
The fresh water and wastewater targets for processes with regeneration unit
can also be attained through water surplus diagram or WCA.
However, these
methods are different from limiting composite curve as they consider water reuse and
recycling simultaneously and are not limited to mass transfer-based operations.
Water surplus diagram and WCA approaches have proven that regeneration
placement above or across the pinch enable water utility reduction. This is because
the region above the pinch is the most constrained in term of purity and thus
increasing either the purity or amount of water available in this region reduces water
utility consumption. Regeneration across the pinch will be the best option as water is
taken from a region of surplus into a region with deficit of pure water.
50
2.6.5
Network Design
For water recovery network with and without regeneration, network design
can be carried out using a grid diagram and a network design by source and demand
approach.
2.6.5.1 Grid Diagram
Network design of water-using processes can be represented by using the grid
diagram which can be simplified by the conventional flowsheet (Figure 2.27). Two
different approaches through grid diagram are possible that achieve different
objectives, whilst both allowing minimum utility targets to be achieved in the design.
The network design obtained from both approaches can then be simplified by
breaking the loop in the network (Figure 2.27).
Loop
1
Fresh water
60 ton/h
Process 1
2
3
4
Process
2
Process
3
Process
3
Process
4
W
Wastewater
60 ton/h
(a)
(
b
Figure 2.27: (a) Grid diagram for water network with three loops
(b) Conventional flowsheet for simplified design (after loop breaking)
51
The first approach maximises the driving force in the resulting design. The
limiting composite curve is divided vertically to form mass load intervals (see Figure
2.28). Wherever there is a change in slope on the limiting composite curve, a mass
load interval occurs. Then, network design is conducted according to these intervals.
C (ppm)
800
Mass load
interval
400
100
50
41
9
m (kg/h)
4
3
2
1
11.25 ton/h
FW
72 ton/h
58.25 ton/h
90 ton/h
22.5 ton/h
18 ton/h
Figure 2.28: Network design with maximum driving force
In network design, it is also important to ensure minimum matches are made.
Therefore, the second approach enables a designer to achieve the minimum number
of water matches in network design.
Instead of following mass load interval,
concentration interval is followed (Figure 2.29). Only sufficient water is used in
each match to maintain network feasibility. Excess water supply will be bypassed
for later use when it is more than required. For this approach, bypassing and mixing
are required to achieve the water targets.
52
C (ppm)
800
44.2
ton/h
Concentration
interval
400
4
100
3
50
20 ton/h
2
1
9
20 ton/h
m (kg/h)
41
50 ton/h
FW
90 ton/h
Figure 2.29: Network design with minimum number of water sources
2.6.5.2 Network Design through Source and Demand Approach
In order to achieve the flowrate targets, it is necessary to observe the pinch
division. This means that water sources above the pinch (including fresh water) may
not feed demands below the pinch, and may also may not be mixed with sources that
are below the pinch concentration. The source at the pinch concentration is an
exception, as part of it belongs to the region below the pinch. This guideline must be
observed during the network design.
Other constraints for network design between water source i and demand j are
stated as follows:
(a) Demands
(i) Flowrate
∑F
i,j
= Fj
i
where Fj is the flowrate required by Demand j.
(2.35)
53
(ii) Concentration
∑F C
∑F
i, j
i
i
≤ Cmax, j
i, j
(2.36)
i
where Cj is the contaminant concentration of source i and Cmax,j is
the maximum acceptable contaminant concentration of demand j.
The constraints can be written in terms of water purity, in which
case the inequality sign would be reversed.
(b) Sources
(i) Flowrate
∑F
i, j
≤ Fi
(2.37)
i
where Fi is the total flowrate available from source i.
Figure 2.30 shows one possible network design by source and demand. It is
emphasised that this is only one of the many possible solutions that can achieve the
target. A designer can influence the solution by imposing other constraints such as
forbidden or forced connection for safety or geographic reasons.
Maximum or
minimum constraints may also be set. However, these additional constraints can
sometimes result in water penalty.
Fresh water
30 ton/h
Fresh water
35 ton/h
D1
20 ton/h
S1
Fresh water
5 ton/h
D2
30 ton/h
D4
D3
35 ton/h
65 ton/h
10 ton/h
S2
60 ton/h
10 ton/h
S3
S4
Figure 2.30: Network design by source and demand approach
50 ton/h
54
2.6.6
Water Network Retrofit Constraints
The retrofit synthesis of water recovery network can be very complex. It may
involve various kinds of constraints as well as cost implications including the need to
re-pipe stream to reduce the utility requirement, optimisation of existing equipment,
installation and materials of construction for the new equipment, .
Therefore it is important to identify and evaluate retrofit constraints that limit
water reuse/recycling in an existing water network. This can be achieved through
determination and evaluation of the specific contaminants present in each water
source, together with the physical, chemical and biological water quality factors that
influence water reuse/recycling in the existing water network. In other words, this
may involve making a complete inventory of water flowrates and qualities for each
water stream. Furthermore, the physical location of each water stream within the
plant and the corresponding pipework requirements for reuse/recycle the also needed
to be recognised.
Other retrofit constraints that may limit water reuse/recyc ling in the existing
water network, such as regulatory issues, resource limitations, economic
considerations, public perception and environmental stewardship should also be
considered. Detailed description of these constraints is presented in Byers et al.
(1995).
CHAPTER 3
LITERATURE REVIEW
3.1
Introduction
Most process plants undergo at least one major revamp in their lifetime to
take advantage of the advances in process technology, to improve utility efficiency,
or to increase the plant production. During such revamps, retrofit of process heat
exchanger network (HEN), mass exchange network (MEN) and water network are
needed to ensure that the processes are attained under new operating conditions. A
designer need to produce several alternative designs by varying among other things,
the capital and operating costs in order to achieve the new operating requirements.
Then, the final design is selected from the alternatives.
To date, researches have been focused on retrofit of HENs and MENs.
However no work on retrofit of water network has been developed. The aim of this
research is to develop methodologies to perform retrofit for water network focuses
with and without regeneration. So, it is important to have a firm grasp of the HEN
and MEN retrofit concepts. This chapter will therefore begin by presenting the early
developments made for HENs retrofit. It will then discuss the extension to MENs
grassroots and retrofit synthesis that has been made up to date. The development of
grassroots WPA with reuse, recycle and regeneration will also be presented.
56
3.2
Heat Exchanger Networks Retrofit
Several methods and approaches have been developed for the grassroots
design of HENs. However, these methods are not directly applicable for retrofit
situations. In retrofit of HENs, the relative importance of various parameters is
different than in grassroots design, so different approaches are necessary.
A few methods for HENs retrofit design have been proposed. HENs retrofit
design using pinch methods consists of two stages. The first stage is the targeting
stage, where optimal targets for heat recovery and exchanger requirement are
obtained. The second stage involves retrofitting HENs to achieve the target obtained
from the first stage using a set of rules and design tools.
The first method for the retrofit of HENs based on pinch technology was
presented by Tjoe and Linnhoff (1986). In the targeting stage, the utility demands
and area requirement are determined for each global ∆Tmin less than the existing one.
These results are compared to the existing utility demands and area to obtain an
investment versus savings plot. With a specific payback period for investment, a
global ∆Tmin in accordance with these economic criteria is identified. In the design
stage, it is desired to design a network that achieves the retrofit targets established
during the first stage. The area efficiency as defied in the previous chapter for the
resulting design should not be lower than the original area efficiency in order to
enable further heat recovery (Tjoe and Linnhoff, 1986).
Later, the initial targeting procedure for suitable ∆Tmin was improved by
Polley and Panjeh Shahi (1990) by including a relationship between pressure drop
and heat transfer coefficient in order to obtain more accurate area calculations. This
method is mainly applicable for uniform processes where area is the dominating cost
and heat exchangers can be relocated between different positions in a network. The
major drawback of this approach is the temperature driving forces for heat exchange
between the streams are considered as the main factor influencing the economy.
This means that other parameters such as piping or types of heat exchangers cannot
57
be taken into account. And in many cases, these parameters may have a greater
influence on the economics of the HENs than the heat exchanger area.
Carlsson et al. (1993) established a HENs retrofit method based on pinch
technology, which aim to minimise the total cost of the network instead as opposed
to the total area of the network.
The methodology consists of a cost matrix,
constructed using the cost of exchanger area, piping and auxiliary equipment,
pumping and maintenance associated with each other potential match. The cost
matrix is then used together with a set of rules to perform the design. There is no
targeting stage for this approach.
The capital-energy trade-off is evaluated by
producing several designs at various heat recovery levels. Even though this approach
do take in consideration of the cost of structural changes implemented in the retrofit
design, but its dependence on accurate piping and other cost data for each potential
match could in some cases make it impractical.
Recently, Reisen et al. (1995) introduced a method for decomposition of the
original HENs into a number of sub-networks. This enables the size of the design
problem to reduce and favour the generation of simple retrofit designs. Each of the
sub- networks generated by decomposition is screened using pinch targeting
technique to identify the sub- networks, which would yield the most cost-effective
retrofit. The sub- networks thus identified then become the subject of retrofit design
to which any of the previously discussed design methods can be applied. This
decomposition method could help reduce the time required to generate pinch designs,
but the effort required to investiga te the alternative sub-network could be
considerable where large HENs are involved.
58
3.3
Mass exchange network
3.3.1
Grassroots Synthesis of Mass Exchange Network
The problem of separation system synthesis has been the subject of rigorous
research effort due to the significant capital and operating cost associated with the
separating processes used in chemical plants. However, these researches have a
common limitation as they have not addressed the problem of minimising the cost of
MSA’s subject to the thermodynamic constraints imposed by the phrase-equilibrium
relations. This serious limitation can be mitigated by introducing the notion of mass
exchange network synthesis (MENS).
El-Halwagi and Manousiouthakis (1989) addressed the problem of
synthesising MENs considering thermodynamic feasibility of mass exchange and
economics.
Using pinch technology principles, they assume linear equilibrium
relations to develop a Composition Interval Table (CIT), analogous to the
Temperature Interval Table of the HENS problem.
A minimum allowable
composition difference between rich and lean streams, ε, is introduced. Minimum
cost of Mass Separating Agents (MSA) is determined subject to the thermodynamic
constraints which are imposed by the equilibrium relations. The Composite Curve
concept is also adopted and based on the identifications of the pinch a number of
rules are developed for the deviation of the network. Although useful guidelines are
provided, the proposed approach assumes decomposition of the original network is
not derived through a systematic procedure.
Later, these authors suggested using the minimum number of units as an
attempt to minimise the capital cost in MENs (El-Halwagi and Manousiouthakis,
1989, 1990). However this is not always sufficient since the size of the exchangers
are also important. They also observed that ε is an optimisable parameter for MENs.
Increasing ε increases the cost of utilities, but results in lower capital costs. As a
result, the annualised total cost of a network would pass through a minimum, which
corresponds to the optimal value of ε. Nevertheless, there was no way knowing the
capital costs until the network was designed and the optimisation could only be done
59
by carrying out many repeated designs. The absence of capital cost target also meant
that there was no guarantee that the capital cost of a network was the minimum
attainable for a specific value of ε.
El-Halwagi and Manousiouthakis (1990a) introduced an automated synthesis
procedure. This procedure first used linear programming to determine the pinch
points and minimum utility targets. MILP was then used to synthesise all possible
networks featuring the minimum number of units. The complete network was then
cost and the one featuring the lowest cost was selected. The main shortcoming of
this procedure is that the capital and operating costs are not considered
simultaneously. Another limitation is that it apparently considers only networks
featuring the minimum number of units, which does not necessary achieve the
minimum capital cost.
El-Halwagi and Manousiouthakis (1990b) proposed a simultaneous synthesis
of mass exchange network and regeneration networks. CIDs are developed for both
networks and feasibility criteria for mass exchange above and below the pinch are
introduced.
Based on these criteria a MINLP formulation is developed for the
identification of the MEN-pinch among a number of candidates. This MINLP
problem results in the minimum MSA cost for both primary and regeneration
network.
In the sequel, the classical MILP transhipment model is solved to
determine the minimum number of units, whereas no systematic methods are
provided for the derivation of the network configuration. It should be noted that the
whole venture cost is dependent on the initial selection of ε.
Within the same scope, El-Hawagi and Srinivas (1992) addressed a number
of specialised problems, as dephenalisation networks and reactive mass exchange
networks, where the appropriate equilibrium relations are derived. Flower et al.
(1993) established a graphical means to represent the total mass balance of the
process and several process blocks as an aid to the engineer at the preliminary design
stages for waste minimisation.
Papalexandri et al. (1994) applied MINLP to the MENS problem and they
attempted to optimise capital and operating costs simultaneously by considering a
60
network hyperstructure where all mass exchange alternatives are taken into account.
However, a great amount of computational effort is required to set up and optimise
the network hyperstructure.
Furthermore, the designer is removed from the
important decision-making and his/her input is limited.
Hallale and Fraser (1998) proved that minimising the number of units in
MENs do not necessarily minimise the capital cost of the network. These authors
developed a first known targeting method for the minimum number of trays in the
network based on a specified ε, which is then translated into a capital cost target.
Besides that, they also performed optimisation before designing the network by
trading off between capital and operating costs. This method was focused on the
special case of water minimisation and it can be further extended to absorbents and
generalised to other mass exchange networks.
3.3.2
Mass Exchange Networks Retrofit
The synthesis of optimal MENs deals with identification of a cost effective
network of mass exchangers that preferentially transfer certain species from rich
streams to lean streams. MENs has mainly dealt with grassroots design and very
little work has been focused on MENs retrofit.
The first approach on retrofit of MENs was recommended by Fraser and
Hallale (2000) by using the work of Tjoe and Linnhoff (1986) as a basis. They
demonstrated that the pinch technology approach for the retrofit of HENs can be
successfully applied for the retrofit of MENs. The aim of retrofit by this approach is
to achieve the best savings in operating costs, subject to a minimum payback period
or a minimum capital expenditure.
In the targeting stage, the established grassroots methods for MENs to
determine utility (MSA) targets and equipment cost targets.
The saving versus
investment curve for MEN retrofit is developed from a stage-load plot representing
the total number of stages versus the mass load of the external MSA. In the size- load
61
diagram, a retrofit path is then chosen, which allows one to determine the savings
achieved for extra size. Costing this yields the saving versus investment diagram.
Lastly, elimination of cross-pinch transfer and appropriate use of driving force are
used as the key principles in the retrofit design stage.
Alfadala et al. (2001) developed another methodology fo r retrofit of MENs.
Firstly, the alternative of series and parallel structural configurations of interest
through heuristics are identified. Then, the retrofitting strategies, those restricted by
no capital expenditure and those involving capital expenditure are the main focus.
When no capital expenditure is involved, the performance of the current system is
enhanced with substituting solvent. The capital expenditure alternative will have
additional new equipment.
A new type of mass-pinch analysis is developed to
maximise the utilisation of existing capital while reconciling added capital with
operating cost. Then, different process alternatives are considered and screened to
attain the final design.
3.4
Water Recovery Network
3.4.1
Grassroots Synthe sis of Water Recovery Network Using Pinch Analysis
Water is one of the most highly used commodities in industry. Its scarcity,
rising energy costs and stricter environmental regulations on industrial effluents has
created different views on water usage in the last few years. The water allocation
problem consists of finding the minimum amount of fresh water that each waterusing process needs, together with the maximum amount of water effluent from these
processes that can be reused in other processes. Many innovative solutions to these
problems have been published via water pinch analysis (WPA).
62
3.4.1.1 Grassroots Synthesis for Maximum Recovery Network through Reuse
and Recycle
The first attempt achieving maximum recovery network by maximising water
reuse and recycling was developed by Wang and Smith (1994). They presented a
graphical approach that was adapted from heat integration using pinch technology.
By plotting the limiting composite curves versus the limiting composition interval,
one can locate the minimum fresh water and wastewater flowrates prior to any
network design. A systematic network design procedure, which allowed the targets
to be met, is also presented. However, the assumption of water utilisation process as
a mass transfer operation incurs some major drawbacks in the analysis.
Dhole et al. (1996) correctly stated that some unit operations such as reactors,
cooling towers and boilers could not be adequately modelled as mass transfer
operations. They in turn proposed a water source and demand composite curves to
be used to locate the minimum fresh water consumption and wastewater generation.
They also showed that proper mixing and bypassing could further reduce the fresh
water consumption. However, it is later pointed out that unless the correct stream
mixing system is identified, the apparent targets could be substantially higher than
the true minimum fresh water and wastewater targets (Polley and Polley, 2000).
The Evolutionary Table was developed by Sorin and Bedard (1996) to target
the minimum fresh water and wastewater numerically. However, Hallale (2002)
showed that the Evolutionary Table failed to locate the correct pinch points when
more than one global pinch points occurred in a water- using process.
Hallale (2002) pointed out that the water source and demand composite
curves do not give a clear picture of the analysis. The targets obtained may not be a
true solution, as they strongly depend on the mixing patterns of the process streams.
In turn, he presented a water surplus diagram in targeting the minimum fresh water
and wastewater. It is similar to the water source and demand composite curves
proposed by Dhole et al. (1996), thereby overcoming the limitations in the mass
transfer-based approach. Furthermore, this new representation automatically builds
63
in all mixing possibilities in order to determine the true pinch point and the reuse
target.
More recently, a tabulated approach by using Water Cascade Analysis
(WCA) is developed by Manan et al. (2004a) to eliminate the tedious graphical
drawing and the trial-an-error method of water surplus diagram. WCA is able to
solve problem involving multiple pinch accurately. Furthermore, the WCA feature
has been integrated into computer software called Heat-MATRIX (Manan et al.,
2003).
3.4.1.2 Grassroots Synthesis for Maximum Recovery Network through Reuse,
Recycle and Regeneration
A number of methods related to synthesis of grassroots maximum recovery
network involving reuse, recycle and regeneration have been published. Methods
published for water regeneration may be classified into two groups. The first group
of methods are those based on WPA while the second group are those based on a
mathematical optimisation approach.
Wang and Smith (1994) proposed the first pinch-based water regeneration
reuse and recycling method. They introduced the concept of limiting composite
curve with regeneration to generate the minimum water targets prior to network
design. This regeneration approach is able to distinguish between regeneration reuse
and regeneration recycling cases.
However, Kuo and Smith (1998) later pointed out that this approach fails to
obtain the true targets when the pinch points were relocated after regeneration. In
return, they developed a new methodology where the minimum water targets are
refined by migrating streams that have been classified. Targeting the number of
regeneration and final effluent treatment units were also added in their approach.
64
Castro et al. (1999) next extended the regeneration reuse algorithm to take
into consideration of the multiple pinch points in the water network. Minimum fresh
water and regenerated water targets are achieved by using water source diagram.
However, the network achieved mostly does not contain the minimum number of
units due to splitting of operations. To overcome this problem, a heuristic rule is
added to their approach, i.e. with additional fresh water consumption.
Yet, the major drawback in the above- mentioned approaches is the
assumption of water utilisation process as a mass transfer operation.
Water as
cooling and heating media in cooling towers and boilers, and as a reactant may not
be appropriately represented as mass transfer operation (Manan et al., 2004a).
To overcome the limitations, Hallale (2002) established the water surplus
diagram which is not restricted to mass transfer-based operations. This approach
targets the minimum fresh water and wastewater for problems with regeneration.
Some guidelines are given for the placement of regeneration units to obtain the
biggest savings in both fresh water and wastewater.
A numerical tabular approach known as Water Cascade Analysis (WCA) was
recently introduced by Manan et al. (2004a) to eliminate the tedious graphical
approach of water surplus diagram. Regeneration and process changes were also
assessed based on the principles of water surplus diagram.
On the other hand, the first mathematical optimisation approach for water
regeneration was introduced by Takama et al. (1980). They addressed the problem
of optimal water recovery network in a petroleum refinery by generating a
superstructure of all possible re- use and regeneration opportunities. Optimisation is
then performed on the superstructure to remove the uneconomic features of the
design.
Alva-Argáez et al. (1998) proposed an automated optimisation approach for
the synthesis of water recovery network based on superstructure decomposition
method.
All possibilities for water reuse, regeneration reuse and regeneration
recycling are considered in the model. The network produced features minimum
65
total annual costs with constraints e.g. geographical, control or safety included.
Moreover, the designer is able to be in-charge of the intricacy of the network design.
Benkó et al. (2000) presented an alternative superstructure-based “Cover and
Eliminate” approach with NLP.
This approach formulates the design of water
recovery network mathematically with minimum fresh water consumption and
regeneration flowrate as well as minimum number of treatment systems.
Bagajewicz and Savelski (2001) introduced a series of linear programming
(LP) and MILP formulation with regeneration to determine the optimal water
utilisation design. With this approach, several alternative designs are produced and a
procedure is presented to select the most cost optimum water recovery network.
Xu et al. (2003) developed a sequential three-step programming method to
target the minimum fresh water and regeneration water flowrates. The problems
were modelled as MINLP before they were solved. The authors argued that the
common believe where water regeneration at the pinch point in the pinch-based
approach (Wang & Smith, 1994; Kuo & Smith, 1998) may not necessarily achieve
the minimum fresh water target.
3.4.2
Retrofit of Water Network
A few methods for water network retrofit have been proposed. However,
these methods are basically based on the use of mathematical programming methods.
By formulating the water network retrofit problem as a mathematical model using a
set of equations or constraints including an objective function, these mathematical
programming methods transform the water network retrofit problem into
optimisation task.
Jödicke et al. (2001) developed a MILP model that required easily accessible
data such as process location and holding tanks and considered expensive piping
needs to generate the wastewater reuse designs. This approach performed as a
66
screening tool to achieve wastewater network design with minimum total cost
(operating costs and investment costs) for a certain time horizon. Regeneration is
also included into the model.
Another mathematical programming model established by Huang et al. (1999)
can be conceivably applied to the preliminary grassroots design or to retrofit an
existing process.
This model consists of design equations of all wastewater
treatments units to generate minimum fresh water usage and minimum wastewater
treatment capacity.
Other works on mathematical programming for water network retrofit
problems were mainly based on grassroots synthesis approaches. These include the
work of Parthasarathy and Krishnagopalan (2001); Jacob et al. (2002); Thevendiraraj
et al. (2003) and Koppol et al. (2003). In these work, the main focus was given
towards the minimisation of utility (fresh water and wastewater flowrates)
consumption.
3.5
The State-of-the-art on Water Network Retrofit – Addressing the
Research Gap
From the studies associated with the retrofit techniques for water network
previously mentioned, we observed a number of issues remain unsolved.
1. The current water network retrofit approaches are generally focused on
mathematical optimisation methods. The main limitations of mathematical
approaches are the lack of user involvement in decision- making due to their
“black box” nature, and the relative difficulty of using and mastering these
programmes. These factors remain the major causes of its low acceptance by
the industry. In addition, the computing cost may be more expensive and
users needed to be well trained with the features of the programmes`.
67
2. Most work on mathematical programming for water network retrofit
problems were mainly based on grassroots synthesis approaches. However, it
is quite impossible to achieve an optimal retrofit without taking into
consideration of the various process and equipment design constraints. This
may cause major modification with long payback period in some cases. A
good retrofit approach should exploit opportunities to maximise usage for
existing facilities while trying to minimise utility cost. This often makes a
retrofitted network looks quite different from the optimum grassroots design
(Tjoe and Linnhoff, 1986).
3. Installation of new regeneration units has become the main focus in most
existing water network retrofit works. However, it is important to realise that
optimisation of existing water regeneration units play an important role
during the revamp of water network. Beneficial goals such as elimination of
capital cost due to new regeneration unit(s) installation, reduced operating
cost as well as minimisation of fresh water consumption and wastewater
generation can be achieved. Moreover, optimisation of existing regeneration
units may also achieve the same water utility savings as in the cases of new
regeneration unit(s) installation.
Therefore, the urge for a friendly, practical and systematic water network
retrofit approach based on WPA has motivated this work. We hereby introduce a
few novel water network retrofit techniques based on WPA, i.e.:
§
water network retrofit for mass transfer-based operations
§
water network retrofit for non- mass transfer based operations
§
water network retrofit with integration of existing regeneration
unit(s) optimisation.
§
water network retrofit with integration of new regeneration unit(s)
CHAPTER 4
METHODOLOGY
4.1
Introduction
Four new systematic methodologies for retrofit of water network based on
Pinch Analysis concept have been developed in this work, i.e. retrofit of water
network for mass transfer-based operations; retrofit of water network for non- mass
transfer-based operations; retrofit of water network with regeneration unit(s)
optimisation; retrofit of water network with the addition of new regeneration unit(s).
Figure 4.1 gives an overview of the water network retrofit techniques developed in
this work.
4.2
Retrofit of Water Network with Reuse and Recycling
Water-using operations in a process plant can generally be classified into
mass transfer-based and non-mass transfer-based operations, as described in sections
2.6.2.1 and 2.6.2.2. For mass transfer-based operations, the driving force involve
equilibrium relationships and the design is more complex than non- mass transfer
based operations. The amount of contaminant accumulated is the main concern for
mass transfer-based operations while for non-mass transfer-based operations, water
flowrate is more important. As a result, water flowrate fed to mass transfer-based
operations are not necessarily the same amount as water flowrate in the limiting data
WATER NETWORK RETROFIT
Retrofit of Water Network
with Reuse & Recycling
Retrofit of Water Network
for Mass Transfer-based
Operations
Retrofit of Water Network
for Non- mass Transferbased Operation
Retrofit of Water Network
with Reuse, Recycling &Regeneration
Retrofit of Water Network
with Regeneration Units
Optimisation
Figure 4.1: Overview of the four methodologies developed in this work
Retrofit of Water Network
with the Additional of
New Regeneration Units
70
as long as the desired mass load is accumulated. However, for non- mass transferbased operations, water flowrate fed must be the same as water flowrate in the
limiting data.
During retrofit, capital investment required can be determined by the size of
the equipment. Among the most common mass transfer-based equipment is stage
column with number of stages as the main sizing parameter. On the other hand,
sizing of non- mass trans fer-based operations relies on the feed water flowrate. Since
water flowrate fed to non- mass transfer processes is always constant, no capital
investment is required due to no changes in the size of these operations. Therefore, it
is important to develop different retrofit methods for water network with mass
transfer-based and water network with non- mass transfer-based water- using
operations.
Two different techniques have been developed for retrofit of water network
with reuse and recycling for different types of water- using operations. Retrofit
technique for water network with mass transfer-based operations involves two key
steps namely utility targeting and network design. During retrofit targeting, FFW, min
and Nstages targets for a range of e were set. Two retrofit profiles were introduced to
select the retrofit targets based on a minimum payback period. During network
design, cross pinch exchangers were eliminated before retrofitting the existing
network to achieve the retrofit targets.
Retrofit method for non-mass transfer-based operations precludes targeting
and only requires retrofit design. A new graphical tool called concentration block
diagram (CBD) has been introduced to diagnose, retrofit and evolve the existing
water network. Retrofit was performed after elimination of all cross pinch streams
which results in excessive use of existing water utility. Figures 4.2 and 4.3 represent
the flow diagram for the retrofit techniques developed with reuse and recycling.
71
Retrofit Targeting
Select an εn value where 0 < εn = ε existing
Calculate the limiting data for ε n through equilibrium equation
Target the minimum utility (FFW,min & FWW,min)
and the total number of stages (Nstages) for εn
Does n > 20?
No
Yes
Plot Nstages versus FFW graph of
grassroots design and retrofit profile
Calculate the utility savings and capital investment for each εn
Plot savings versus investment graph
Select a preferred payback
period
Obtain retrofit target, ε target
Retrofit Design
Draw the existing water network with ε target
Identify and eliminate cross-pinch mass exchangers
Retrofit the water network to achieve final design
Figure 4.2: Flow diagram for retrofit of water network for mass transfer-based
operations
72
Retrofit Design
Draw the existing water network in
concentration block diagram
Identify and eliminate cross-pinch streams
Retrofit the water network to achieve preliminary design
Reusing the wastewater in the water network
to achieve final design
Figure 4.3: Flow diagram for retrofit of water network for non- mass transfer-based
operations
4.3
Retrofit of Water Network with Reuse, Recycling and Regeneration
4.3.1
Retrofit of Water Network with Regeneration Units Optimisation
Water regeneration and reuse thus has become a common practise in process
industry. A water regeneration unit can be defined as any process unit that is used to
partially purify process effluent for in-plant reuse and recycle. However, most
regeneration units operate at lower performance than what it can actually achieve in
practise. It is also observed that when a retrofit project is carried out to debottleneck
an existing water network, optimisation of existing water regeneration units is often
overlooked. Hence, installation of new regeneration units has become the main
focus in most water network retrofit works. This practise is often associated with
capital investment due to the purchase of new water regeneration units.
However, it is important to realise that optimisation of existing water
regeneration units play an important role during the revamp of water network. As
will be shown in this chapter, beneficial goals such as elimination of capital cost due
73
to new units installation, reduced operating cost as well as the minimisation of fresh
water consumption and wastewater generation can be simultaneously achieved.
Moreover, optimisation of existing regeneration units may also achieve same water
utility savings as in the cases of new regeneration unit(s) installation. Therefore,
optimisation should firstly be considered during any retrofit project of existing water
network.
The new techniques proposed for retrofit of water network with existing
regeneration unit(s) optimisation consist of two stages. In the first stage, various
network retrofit targets are established prior to detailed network design.
This
includes utility savings and capital investment, that were determined for a range of
optimised process parameters of the exiting regeneration unit(s) (i.e. total flowrate
and/or outlet concentration of the regeneration unit). The water network is next redesigned (retrofitted) to meet the established targets.
This methodology has
successfully achieved the retrofit targets prior to design and further minimise fresh
water consumption and wastewater generation in an existing water network. Figures
4.4 and 4.5 represent the retrofit targeting and retrofit design flow diagram for water
network with regeneration units optimisation.
74
Retrofit Targeting
Selection of optimisation parameters for
existing regeneration units (Freg and/or Cout )
Calculation of estimated increment cost
for optimisation parameters (∆CostFreg & ∆CostCout)
Yes
Does ∆CostFreg = ∆CostCout
No
Optimisation with ∆Freg
Optimisation with ∆Cout
Select existing regeneration unit(s)
for optimisation with ∆Freg
Obtain Fupgrade,min
Select existing regeneration unit(s)
for optimisation with ∆Cout
Obtain ∆Freg,max for the selected
existing regeneration unit(s)
Obtain ∆Cout,ma x for the selected
existing regeneration unit(s)
Target the minimum utility for various
∆Freg where 0 = ∆Freg = ∆Freg,max
Target the minimum utility for various
∆Cout where 0 = ∆Cout = ∆Cout,max
Plot FFW versus ∆Freg graph of
grassroots design and retrofit profiles
Plot FFW versus ∆Cout graph of
grassroots design and retrofit profiles
Calculate the utility savings and
capital investment for each ∆Freg
Plot savings versus investment graph
Calculate the utility savings and
capital investment for each ∆Cout
Select a preferred payback period
Plot savings versus investment graph
Obtain retrofit target, ∆Freg,optimum
Select a preferred payback period
Have achieve
∆Freg,optimum& ∆Cout,optimum?
Yes
Retrofit Design
Obtain retrofit target, ∆Cout,optimum
No
No
Have achieve
∆Freg,optimum& ∆Cout,optimum?
Yes
Figure 4.4: Retrofit targeting flow diagram for retrofit of water network with
regeneration units optimisation
75
Retrofit Design
Draw the existing water network in
concentration block diagram
Optimise the existing regeneration units in
the network according to retrofit targets
Identify and eliminate cross-pinch streams
Retrofit the water network to achieve preliminary design
Reusing the wastewater in the water network
to achieve final design
Figure 4.5: Retrofit design flow diagram for retrofit of water network with
regeneration units optimisation
4.3.2
Retrofit of Water Network with the Additional of New Regeneration
Units
The amount of utility reduction through reuse/recycle is rather restricted. By
coupling reuse/recycle strategy with regeneration, a further reduction of utility
consumption in a water network is possible.
Nevertheless, most of the work
incorporating regeneration strategy has been focused on the development of
grassroots design. These approaches may not be applicable for retrofit since various
constraints (e.g. equipment layout, piping constraints) on the existing site needed to
be taken into consideration during retrofit. Besides, economics performance is a key
criterion during water network retrofit project. To achieve larger water savings for
existing processes, there is a clear need to develop a systematic technique for water
network retrofit with regeneration strategy that incorporates economic criterion.
76
As mentioned in the previous chapter, optimisation of existing regeneration
units should firstly be considered during retrofit of water network. However, in
cases where the existing regeneration units are performing at the ir optimum
conditions or no regeneration unit exist, one should consider the additional of new
regeneration units for further utility reduction. Beneficial goals such as reduction in
operating cost as well as the minimisation of fresh water consumption and
wastewater generation can be achieved with the installation of new regeneration units.
A new two-stage retrofit technique has been proposed for water network with
the additional new regeneration unit(s). The optimum design of the new regeneration
unit was based on two process parameters, i.e. regeneration flowrate and/or the outlet
concentration.
The first stage locates the various retrofit targets, where utility
savings and capital investment were determined for a range of process parameters.
Given a fixed payback period or capital expenditure, the retrofit targets were
determined from the saving versus investment diagram. During the network design,
the existing water network was revamped according to pinch design rules to meet the
established retrofit targets. This methodology has successfully achieved the retrofit
targets prior to design and further minimise fresh water consumption and wastewater
generation in an existing water network. Figures 4.6 and 4.7 represent the flow
diagram for the retrofit techniques developed with reuse and recycling.
Retrofit Targeting
Case 1: Vary Freg with Fixed Cout
Case 2: Vary Cout with Fixed Freg
Case 3: Vary Freg & Cout
Obtain Freg,max, Cout,min, & Cout,max
Obtain Freg,max, Cout,min, & Cout,max
Obtain Cout,min, & Cout,max
Target the minimum utility for
various Freg where 0 = Freg = Freg,max
Target the minimum utility for various
Cout where Cout,min = Cout = Cout,max
Obtain Freg,max for various Cout
where Cout,min = Cout = Cout,max
Plot FFW versus Freg graph of
grassroots design and retrofit profiles
Plot FFW versus Cout graph of
grassroots design and retrofit profiles
Target the minimum utility for various Cout
with Freg,max where Cout,min = Cout = Cout,max
Calculate the utility savings and
capital investment for each Freg
Calculate the utility savings and
capital investment for each Cout
Plot FFW versus Cout graph of
grassroots design and retrofit profiles
Plot savings versus investment graph
Plot savings versus investment graph
Calculate the utility savings and capital
investment for each pair of Cout & Freg,max
Select a preferred payback period
Select a preferred payback period
Plot savings versus investment graph
Obtain retrofit target, Freg,optimum
Obtain retrofit target, Cout,optimum
Select a preferred payback period
Obtain retrofit target, Freg,optimum & Cout,optimum
Retrofit Design
Figure 4.6: Retrofit targeting flow diagram for retrofit of water network with the additional of new regeneration units
78
Retrofit Design
Draw the existing water network in
concentration block diagram
Add the targeted new regeneration units in
the network according to retrofit targets
Identify and eliminate cross-pinch streams
Retrofit the water network to achieve preliminary design
Reusing the wastewater in the water network
to achieve final design
Figure 4.7: Retrofit targeting flow diagram for retrofit of water network with the
additional of new regeneration units
4.4
Chapter Summary
Four different types of water network retrofit methodology has been
presented. This include retrofit of water network with mass transfer-based operations,
retrofit of water network with non- mass transfer-based operations, retrofit of water
network with regeneration unit optimisation, and retrofit of water network with the
addition of new regeneration unit have been developed. In order to demonstrate the
applicability of these techniques, four case studies have been utilised and discussed
in the next chapter.
CHAPTER 5
RESULTS AND DISCUSSION
5.1
Retrofit of Water Network for Mass Transfer-based Water-using
Operations
5.1.1
Problem Statement and Assumptions
The problem of retrofitting water network for mass transfer-based processes
can generally be stated as follows:
Given a set of mass transfer-based water-using processes, it is desired to
retrofit an existing water distribution network through re-structuring of
process streams and most effective use of existing process units to accomplish
the best savings in operating costs, subject to a minimum payback period
or/and a maximum capital expenditure.
The following assumptions were made in developing the retrofit procedure:
1. The system operates as a single contaminant system.
2. The system operates isothermally.
3. Reuse / recycling are allowed in the system.
80
5.1.2
Case Study 1
The case study involving the removal of sulphur dioxide (SO2 ) from four
gaseous process streams from Hallale and Fraser (1998) was used as the example for
this study. The process uses stage columns to absorb SO2 from the process streams
using water as an external MSA.
Figure 5.1 shows the existing schematic flowsheet for case study 1 with the
minimum composition difference, e of 0.00027 kmol SO2 /kmol water. The existing
flowsheet consumes 3360.7 kmol/h of fresh water and generates 3360.7 kmol/h of
wastewater.
A total of 20 stages are used to absorb SO2 in the existing water
network.
Wastewater
3360.7 kmol/h
PROCESS 4
(3 stages)
PROCESS 1
(5 stages)
1161.3
kmol/h
PROCESS 3
(2 stages)
PROCESS 2
(5 stages)
1006.6
kmol/h
PROCESS 3
(5 stages)
1192.8
kmol/h
Freshwater
3360.7 kmol/h
Figure 5.1: Existing schematic flowsheet for case study 1
Data for these streams are listed in Table 5.1. It is assumed that the gas
stream mainly consists of air, along with a small amount of other gases. However,
only SO2 is absorbed into water. Note that, the gas flowrates, G are expressed on
SO2-free basis.
These flowrates remain unchanged as SO2 is absorbed.
The
composition of SO2 in the gas streams is expressed as a molar ratio, Y. Each stream
81
is supplied with a feed composition of Ys and is required to reach an equilibrium
composition of Yt. The gas streams and water are all at 20o C.
Table 5.1: Stream data for case study 1
Gas stream
1
Flowrate, G
Ys
Yt
(kmol/h)
(kmol SO 2 /kmol inert gas) (kmol SO 2 /kmol inert gas)
50
0.01
0.004
2
60
0.01
0.005
3
40
0.02
0.005
4
30
0.02
0.015
Cost data used in this case study has been adapted from Coulson et al. (1993)
by Hallale (2002), as presented in section 2.5.2. The capital investment of a column
is made up of the cost of column shell and stages. These costing equations assume
carbon steel as the construction material and sieve stages as the type of stage used. A
total of 8600 annual operating hours was assumed. Fresh water cost is $0.34/ton
(Coulson et al., 1993). A payback period six months was selected.
5.1.3
Retrofit Targeting
A common misconception is that the best retrofit should accomplish the
optimum grassroots design. In practice, achieving the optimum grassroots design
would be impractical and uneconomical as many constraints may be imposed on an
existing network during retrofit. The objective of retrofit in this research is to use the
existing units more effectively. To achieve the retrofit targets for mass transferbased operations, Water Cascade Analysis (WCA) technique by Manan et al. (2004a)
and the capital cost targeting approach by Hallale and Fraser (1998) were utilised.
82
5.1.3.1 Minimum Fresh Water Target
Firstly, we need to obtain the minimum fresh water, FFW, min requirement for
case study 1 for a given value of minimum composition difference, e. This can be
achieved using WCA technique by Manan et al. (2004a). In order to use WCA
technique, the data in Table 5.1 has to be converted to limiting water data. This can
be achieved by applying the equilibrium equation for the range of composition
involved. The limiting water composition corresponding to each gas stream can be
determined using equation 2.3 in Chapter 2.
The equilibrium relation for SO2 removal for this case study is as follow
(Perry, 1984),
X
in
max
Y t − (−0.00326 )
=
−ε
26.1
(5.1a)
Y s − ( −0.00326)
−ε
26.1
(5.1b)
out
X max
=
where Y* is the composition of SO2 in gas stream (molar ratio), X is the composition
of SO2 in water (molar ratio),
Table
5.2
presents
the
limiting
water
data
with e of
0.00021
kmolSO2 /kmolwater. With the limiting water data, WCA can be used to get FFW, min
target. WCA results for the limiting water data with e = 0.00021 kmolSO2 /kmolwater
is illustrated in Table 5.3. As can be seen, the FFW, min for the case study is 2688.8
kmol/h.
The pinch point occurs at 0.000298 kmol SO2 /kmol water which is
corresponds to 0.01 kmol SO2 /kmol inert gas.
83
Table 5.2: Limiting water data with ε = 0.00021 kmol SO2 /kmol water
Xinmax
Gas stream Load removed
(kmol SO 2 /hr) (kmol SO 2 /kmol water)
1
0.3
0.000068
Xout max
(kmol SO 2 /kmol water)
0.000298
2
0.3
0.000106
0.000298
3
0.6
0.000106
0.000681
4
0.15
0.000490
0.000681
Table 5.3: WCT with e = 0.00021 kmolSO2 /kmolwater for case study 1
Concentration
ΣFD, j
ΣFS, I
(kmol SO 2 /
(kmol/h)
(kmol/h)
kmol water)
ΣFD, j +
ΣFS, i
(kmol/h)
FC ,
(kmol/h)
Pure water
surplus
(kmol/h)
Cumulative
pure water
surplus
(kmol/h)
2688.83
0
0
0.000068
0.000106
-1305
0.00049
1383.83
0.0000001
-1226.17
-0.0000002
1644.83
0.0000003
0.0000002
-2610
2871
-783
0.000681
0.0000002
-1305
-2610
0.000298
2688.83
0.0000002
2871
0
-783
1827
1000000
0.0000003
861.83
0.0000002
2688.83
2688.825198
1827
0.0000005
2688.8252
5.1.3.2 Number of Stage Target
Next, target for the minimum number of stages was established. This was
done using the number of stages targeting method for grassroots design initiated by
Hallale and Fraser (1998).
To understand the method for targeting the number of stages, let us first
consider the design of a column (Treybal, 1981). Figure 5.2 (a) shows a column with
gas and water streams where mass is transferred in a countercurrent manner.
Column compositions can be shown on X-Y plot depicting (Figure 5.2b) column
84
equilibrium and operating lines. Note that the operating line is a straight line with a
slope equal to the ratio of water flowrate to the gas flowrate in the column (L/G).
Thus, the operating line must be located above and to the left of the equilibrium line
to have a finite driving force and enable mass to be transferred from the gas stream to
the water stream. With the X-Y plot, the number of equilibrium stages, N can be
achieved by stepping down ‘step’ between the operating and equilibrium lines
(Figure 5.2b).
Y
L, Xin
G, Yout
Yin
Operating line
Absorption
Column
G, Yin
Equilibrium line
Y* = mX + b
Stepping off the
number of stages
out
out
L, X
Slope = L/G
Y
Xin
Xout
X
Figure 5.2: (a) An absorption column (counter-current mass exchanger); (b)
absorption column represented on X-Y diagram
As the operating and equilibrium lines are further apart, less number of
equilibrium stages is required. On the other hand, an infinite number of stages
results when the operating line touches the equilibrium line. Therefore, e plays an
important role to avoid the limiting conditions during the design of a column.
Since the operating and equilibrium lines for this case study are straight lines,
N can also be computed through Kremser equation as given by equation 2.6 in
section 2.5.2 (Treybal, 1980). In order to solve equation 2.6, the changes in water
composition throughout the system are required.
To obtain this, X-Y table
85
established by Hallale and Fraser (1998) is used. Table 5.4 demonstrates the X-Y
table with e = 0.00021 kmolSO2 /kmolwater for case study 1.
Table 5.4: X-Y Table for case study 1
The first targeting step is to arrange Y in ascending order in column 1.
Column 2 represents the gas streams in each interval.
Column 3 contains the
cumulative value of G, (ΣG)k at each interval. The ratio between cumulative gas
flowrate at each interval, (Σ G)k and the total flowrate of solute- free water that
remained constant at the targeted freshwater value, (ΣL)k is calculated in the next
column. (ΣL)k for this case stud y is maintained at 2688.8 kmol/h.
Next, a mass balance equation on SO2 for each interval is utilised to calculate
the SO2 composition difference, ∆Xk and is expressed as follows:
∆X k =
∆ Yk ( Σ G k )
( Σ Lk )
(5.2)
Column 6 shows the direction of FFW flowrate. Note that X which is located
in column 7 starts at zero as FFW is supplied. These values increase over each
interval k by the value of ∆Xk. It is observed from Table 5.4 that X increases as Y
decreases. This is due to the countercurrent operation of the column. Note also that
the final composition of 0.000503 kmol SO2 /kmol water for the freshwater used is
also shown in the same table.
86
With the data presented from in Table 5.4, the number of equilibrium stages
for each interval, Nk is directly achieved through equation 2.6 as shown in the last
column of the table. Table 5.4 also shows the pinch location obtained from WCA
and illustrates how it divides the problem into regions above and below the pinch.
However, the number of stages from each composition interval does not
represent the total number of stages target.
Each interval consists of a certain
number of streams. This should be considered during the estimation of the number
of stages for the system. Therefore, the target for the total number of stages is
determined by summing the contributions from each gas stream across the
composition intervals in which the stream exists (Hallale and Fraser, 1998):
Ni =
βi
∑N
k =αi
k
(5.3)
where ai is the interval where gas stream i starts and ßi is the interval where it ends.
To prevent any inaccurate capital cost target, this is performed separately for regions
above and below the pinch.
The results from equation 5.3 represent the number of equilibrium stages for
regions above and below the pinch and have to be converted to the number of actual
stages. Thus, by dividing the number of equilibrium stages with an overall column
efficiency, Eoc the number of real stages contributed by gas streams, Nr, i can be
determined. The equation is presented as follows:
N r ,i =
Ni
Eoc
(5.4)
where Eoc is approximately 20% of the system efficiency for case study 1
(O’Connell’s correlation in Treybal, 1981).
87
Then, the actual number of stages for regions above and below the pinch is
rounded up to the nearest integer. The target for the total number of stages, Nr, tot is
as follows:
N r, tot =
∑[ N
r,i
]
(5.5)
i
Finally, the number of stages for regions above and below the pinch is
summed up to yield the targeted minimum number of stages, Nstages for a given e
value:
N stages = ∑ [ N r, i ] AbovePinch + ∑ [N r ,i ]BelowPinch
i
(5.6)
i
Table 5.5 summarised the stage contributions for each gas stream and the
total number of stages for regions above, [Nr,i]Above Pinch and below the pinch [Nr,i]Below
Pinch
for case study 1. Hallale and Fraser (1998) concluded that the targeted number
of stages for regions above and below the pinch have to be computed separately to
ensure that the capital cost target achieved in the network design section is consistent
with the minimum water target. They also prove that a slight underestimation of
Nstages will result for cases where the pinch regions are ignored.
Table 5.5: Summary of stage contributions for each gas stream and the total number
of stages regions above and below the pinch for case study 1
Gas stream
[Nr, i ]Below Pinch
[Nr, i ]Above Pinch
1
6
0
2
5
0
3
5
5
4
0
3
Total
16
8
88
5.1.3.3 Nstages versus FFW Plot
By repeating the targeting for FFW, min and Nstages over a range of e value, an
Nstages versus FFW, min plot can be construc ted. Figure 5.3 illustrates the Nstages versus
FFW, min plot for case study 1.
The next step is to estimate the achievable FFW savings for various levels of
capital investments. As aforementioned, the retrofitted network is predicted to use
stages as efficient as the existing network. The analogy of HEN retrofit approach is
used as the basic framework for this approach.
140
120
Optimum
grassroots
design
Nstages
100
80
60
40
20
0
1500
2000
2500
3000
3500
F FW,min (kmol/hr)
Figure 5.3: Nstages versus FFW, min plot for case study 1
Following the analogy of HEN retrofit, two retrofit profiles which starts from
the existing utility consumption is presented in Figure 5.4. The curve that moves
towards the reduction of utility with an incremental Nstages is typically the best retrofit
curve. Note that capital investment is determined by Nstages; and the reduction of FFW
consumption (represented by x-axis) also presenting the simultaneous reduction of
wastewater, which can be directly transformed into utility cost savings.
89
100
Retrofit target
with a = 0.95
90
80
Retrofit target
with ∆a = 1
70
N stages
60
Optimum
grassroots design
50
40
30
20
10
Existing
network
0
1600
2000
2400
2800
(kmol/hr)
FFW
FW (kmol/h)
Figure 5.4: Nstages versus FFW plot for case study 1
3200
3600
90
A retrofit profile which is calculated via stage efficiency, astages is defined as
the ratio between the number of stages target for a grassroots design, Ntarget to the
number of stages for an existing network, Nexisting, for a given value of FFW ,
 N target 

α stages = 
N

 existing  FFW
(5.7)
The astages value indicated how close Nexisting as compared Ntarget . Note that in
equation 5.7, astages is conducted for a specific FFW value instead of MSA load. This
is because the utility costs for a water network can be represented by FFW
consumption and wastewater generation.
The incremental value of α stages, ∆α stages is defined as another retrofit profile
according to the analogy of ∆aArea,. ∆α is described as the ratio between an increase
in the targeted number of stages in a grassroots design, ∆Ntarget to that of an existing
network, ∆Nexisting, for a given decrease in freshwater flowrate, ∆FFW as shown in
equation 5.8:
 ∆N target 

∆α stages = 
 ∆N

existing  ∆ F

FW
(5.8)
Equations 5.7 and 5.8 represent two possible paths for retrofit of an existing
water network with mass transfer-based operations. The path selection is based on
the one that is leading to the optimum grassroots design. For case study 1, the
calculated value of α stages and ∆α stages values are 0.95 and 1.00 respectively. One will
need to choose a retrofit profile that is leading to the optimum grassroots design. A
constant α value may be too conservative (Silangwa, 1986; Shenoy, 1995; Polley,
2000). Hence retrofit path with ∆α = 1 was preferred as a better option for this case
study. This was proven through the achievement of retrofit targets during network
design in the next section.
91
As we move towards the upper left portion of Figure 5.4, FFW is reduced and
additional Nstages are added towards the left of the plot from the existing point. A
savings versus investment plot can be attained by converting the increased Nstages into
capital investment and the reduction of fresh water consumption as well as
wastewater generation into savings in operating cost for various e in the retrofit
profile.
Equations 2.16 to 2.18 in section 2.5.2 are applied to predict the column
diameter and stage spacing and ultimately, the capital investment for the increased
Nstages for every e in the retrofit profile. The results are then applied to equation 2.19
and 2.20 to obtain the capital cost for column shell and stages.
Figure 5.5 shows the savings versus investment plot with lines identifying
various payback periods for the SO2 example. It has been mentioned that a payback
less than six months is preferred for case study 1. Therefore, the point where 0.45
years (6 months) payback line crosses the savings versus investment plot was
selected as the retrofit target for case study 1. The targeted e value is 0.00021 kmol
SO2 /kmol water with $37,500/yr of savings and $16,500 of investment.
100,000
6 months
1 year
Saving ($/yr)
80,000
60,000
40,000
20,000
0
0
50,000
100,000
Investment ($)
150,000
Figure 5.5: Savings versus investment plot for case study 1
200,000
92
5.1.4
Retrofit Design
Now we will prove that the targets established in the pervious section are
achievable in network design. The method used in this section is similar to the
retrofit design for MEN proposed by Fraser and Hallale (2000) since water network
with mass transfer based processes also consists of a network of mass exchangers.
The first step in water network retrofit is to draw the existing network using
limiting water data with targeted e value, 0.00021 kmol SO2 /kmol water as
demonstrated in Figure 5.6. This diagram is similar to the network design proposed
by Wang and Smith (1994). It represents the entire network with the existing mass
exchangers.
The dotted horizontal lines represent the composition intervals of the water
network.
The water composition values shown on the left of the intervals are
obtained from the last column of X-Y table (see column 7 of Table 5.4, page 85).
The vertical arrows show the process streams (1-4) and freshwater streams (FW) in
the network while the solid horizontal and slanted lines represent mass exchangers
involved in mass transfer between the process streams and fresh water streams. Note
that the pinch point which occurs at interval 0.000298 kmol SO2 /kmol water divides
the water network into regions above and below the pinch. The cross pinch mass
exchangers are shown by the solid lines crossing the pinch interval in Figure 5.6.
93
0.000503
3 stages
0.000373
4
3
2
5 stages
5 stages
1161.3
0.000068
2 stages
5 stages
0.000298
(PINCH)
0.000106
Cross pinch
exchangers
1
LEGEND
1
Processes
Water streams
Mass exchangers
1006.6
1192.8
FW
Concentration interval boundaries
Concentration pinch point
Figure 5.6: Existing water network for case study 1
The second step is to eliminate all exchangers that are transferring mass
across the pinch. It can be seen in Figure 5.6 that there are three exchangers
transferring mass across the pinch. These exchangers have to be eliminated as they
cause additional fresh water usage in the network. The network with the cross-pinch
exchangers eliminated is shown in Figure 5.7.
94
0.000503
3 stages
0.000373
4
0.000298
(PINCH)
0.000106
3
2
0.000068
5 stages
1
LEGEND
1
Processes
Water streams
Mass exchangers
FW
Concentration interval boundaries
Concentration pinch point
Figure 5.7: Existing water network for case study 1 with eliminated cross-pinch
exchangers
Since the water network was formed by mass exchangers transferring mass
load to water streams, retrofit was performed next on the water network according to
the design rules developed by Wang and Smith (1994). According to these authors,
network design starts with fresh water from the bottom of the network where the
lowest composition interval exists. The mass exchanger matches between process
and fresh water streams were defined based on the composition intervals. In each
match, only sufficient fresh water was used to maintain the feasibility of the network.
If more water was available that required, any excess was bypassed to be mixed and
used in the later intervals. These rules were applied to ensure that the fresh water
and capital cost targets were achieved.
Figure 5.8 illustrates the water network after retrofit. Note that in Figure 5.8,
the existing mass exchanger for process 1 located between interval 1 and 2 remain
unchanged. Only sufficient water was utilised to maintain feasibility of the network.
Therefore, fresh water requirement for this exchanger was reduced to 1006.6 ton/h
with an increment of 1 stage in the unit. Process 2, which exists in interval 2 remain
unchanged. The remaining fresh water was bypassed to be used later by exchanger 3
95
in interval 2 and 3. Finally, in the last two intervals process 3 and 4 exist. The total
water flowrate was split between these two processes. As only sufficient water i.e.
671 kmol/h was fed to exchanger 4, the total number of stages had been increased
from 3 to 5 stages after retrofit.
0.000503
3 stages
0.000373
4
2+3 stages
5 stages
0.000298
(PINCH)
0.000106
274.52
5 stages
3
2
5+1 stages
1006.6
0.000068
1
LEGEND
1
Processes
Water streams
Mass exchangers
1006.6
671.0
FW
Concentration interval boundaries
Concentration pinch point
Figure 5.8: Retrofitted water network for case study 1
Finally, mass exchangers in the retrofitted design are evaluated. This is due
to the retrofit targets achieved in the targeting section that take into consideration of
reusing the existing mass exchangers. To do this, the Nstages of the new installed
mass exchangers is compared with the eliminated mass exchangers. When we
compared Figure 5.9 with Figure 5.1, note that three of the eliminated existing mass
exchangers can still be reused. An additional of 3 extra stages need to be installed in
exchanger 4 and 1 new stage must be added to exchanger 1 in the existing water
network to achieve the retrofit targets.
96
Wastewater
2684.2 kmol/h
PROCESS 3
(2+3 stages)
PROCESS 4
(3 stages)
PROCESS 1
(5+1 stages)
PROCESS 2
(5 stages)
1006.6
kmol/h
1006.6
kmol/h
PROCESS 3
(5 stages)
671.0
kmol/h
Freshwater
2684.2 kmol/h
Figure 5.9: Conventional flowsheet for the retrofitted network for case study 1
After retrofit, the total fresh water consumption for the example has been
reduced to 2684.2 kmol/h.
This corresponds to an operating cost savings of
$37,500/yr. The capital investment needed to achieve the final design is $19,000
which is slightly higher than the target. The resulting payback period from the
estimated savings and investment is 0.5 year.
5.1.5
Summary of the Developed Water Network Retrofit for Mass Transferbased Operations
This section has shown that MEN retrofit approach can be effectively applied
for retrofit of water network with mass transfer-based operations. The methodology
developed enables designer to obtain retrofit targets prior to network design. The
retrofit targets, which include fresh water flowrate and the number of stages for the
mass exchangers were achieved through WPA grassroots targeting techniques.
These targets were then used to plot Nstages versus FFW diagram where a retrofit path
was formed by comparing the existing design with the targets. With the specification
97
of an acceptable payback period or investment limit, a global e in accordance with
these economic criteria was determined. In the retrofit stage, elimination of crosspinch mass exchangers and retrofit of the existing water network through Wang and
Smith (1994) design rules were conducted to achieve efficient usage of the existing
stages. Through the example studied, this methodology has proven that the network
design was able to achieve the retrofit targets.
5.2
Retrofit of Water Network for Non-mass Transfer-based Operations
5.2.1
Problem Statement and Assumptions
The problem of retrofitting water network for non- mass transfer-based
processes can generally be stated as follows:
Given a set of non- mass transfer-based water-using processes, it is desired to
retrofit an existing water distribution network through re-structuring of
process streams to accomplish the best savings in operating costs.
The following assumptions were made in developing the retrofit procedure:
1. The system operates as a single contaminant system.
2. The system operates isothermally.
3. Regeneration reuse / recycling are allowed in the systems.
5.2.2
Case Study 2
98
Reuse/recycling of water in paper mills is considered to be a universal
practice of recovering valuable paper fibres from a paper machine’s excess water
(Wiseman and Ogden, 1996). Apart from operating cost savings and reduction in the
environmental impact of the process, water reuse and recycling can recover
expensive raw materials from the water network.
A paper making process case study produces paper from old newspapers and
magazines was utilised to demonstrate the developed methodology. Its raw water
treatment plant receives river water with high content of suspended solids and
dissolved solids with a operating cost of $0.043/m3 . The mill water and wastewater
system is served by a complex water network with a fresh water consumption of
1989.1 ton/h and wastewater generation of 1680.3 ton/h. A simplified version of the
existing water network for case study 2 is presented in Figure 5.10.
In this paper making process, the used papers are fed to the pulpers where
they are blended with dilution water and chemicals to form pulp slurry called stock.
The paper sheet formation begins when the stock from pulpers is sent to the forming
section of the paper machine. During paper sheet formation in the paper machine,
water is removed from the stock in the forming as well as the pressing sections.
As shown in Figure 5.10, a total of 986.52 ton/h of fresh water is fed to the
paper machine via streams 1 and 2 to remove any debris in the forming and pressing
sections of the paper machine. Part of these water sources (streams 5 and 6) are then
sent to the white water tank along with recycled water (stream 9) from other
processes in De- inking pulper (others).
In order to remove printing ink from the main stock, De- inking pulper (DIP)
receives 751.32 ton of fresh water (stream 3) and 398.5 ton/h of reused water from
white water tank (stream 8). 54 ton/h of this source (stream 12) is then mixed with
14.7 ton/h of freshwater (stream 11) to dilute the stock being pumped to the deculator
in the approach flow system (AF).
99
Fresh water
1989.06 ton/h
10
11
34.68 ton/h
14.7 ton/h
Paper Machine
1 155.4 ton/h
Pres
sing
Sect
5 155.4 ton/h
100 ppm
6 41.28 ton/h
White Water
Tank
8 201.84 ton/h
CP
170 ppm
230 ppm
2 831.12 ton/h
9 1264.5 ton/h
Forming
Section
230 ppm
7 398.5 ton/h
170 ppm
3 751.32 ton/h
DIP
1254 ton/h
250 ppm
4 201.84 ton/h
Others
AF
13415.8 ton/h
250 ppm
Figure 5.10: Existing water network for case study 2
Wastewater
1680.3 ton/h
100
Fresh water (stream 10) is also used to dilute de- inking chemicals (CP) sent
to DIP to assist the ink removal process. In addition, other process in DIP (others)
also receives 201.84 ton/h of fresh water (stream 4). As for the wastewater collected
from the paper machine and DIP (stream 7 and 13), it is sent to the effluent treatment
plant before being discharged into the river. The effluent treatment plant operating
cost is $0.295/m3 as specifiied by the plant.
Total suspended solids (TSS) is taken as the main water quality parameter in
considering water reuse and recycling for this process. Table 5.6 summarises the
water demand and source data for this case study. Water demand refers to the water
requirement of a water-using operation while water source refers to effluent stream
leaving a water-using operation that can be considered for reuse and recycle.
Table 5.6: Water demands and sources for case study 2
Water
Water
Process
Flowrate, Concentration,
Flowrate, Concentration,
C (ppm)
C (ppm)
Description Demand F (ton/h)
Source F (ton/h)
Pressing
Showers
1
155.40
20
1
155.40
100
Forming
Showers
2
831.12
80
2
1305.78
230
Other
Processes in
DIP
(Others)
3
201.84
100
3
201.84
170
DIP
4
1149.84
200
4
469.80
250
Chemical
Preparation
(CP)
5
34.68
20
Approach
Flow (AF)
6
68.70
200
101
5.2.3
Retrofit Design
The methodology for retrofit of water network consists of three ma in steps.
The first step is to diagnose the existing water network using a new tool, called
Concentration Block Diagram (CBD). CBD provides a clear view of the existing
water network in the context of contaminant concentration.
It consists of
concentration- interval boundaries, water using operations, water streams with their
flowrate in ton per hour and contaminant concentration in ppm. CBD divides the
existing water network into regions above and below the pinch. Therefore, the
minimum targets should be established before CBD can be developed. With the
limiting data, the minimum water targets can be obtained easily by using water
cascade analysis (WCA) technique developed by Manan et al. (2004a). Table 5.7
shows the results obtained from WCA with the water pinch at 170 ppm. CBD for
case study 2 is presented in Figure 5.11.
102
Table 5.7: Water cascade table (WCT) for case study 2
Interval Concentration
n
Cn (ppm)
Purity,
Pn
ΣFD, j
(ton/h)
ΣFS, I
(ton/h)
ΣFD, j + ΣFS, i
(ton/h)
FC ,
(ton/h)
Pure water surplus
(ton/h)
Cumulative pure
water surplus (ton/h)
848.12
1
0
1
2
20
0.99998
3
4
5
6
80
100
170
200
0.99992
0.99990
0
-190.08
-831.12
-201.84
0.99983
0.99980
848.12
0.016962
658.04
0.039482
-173.08
-0.003462
-219.52
-0.015366
-17.68
-0.000530
-1236.22
-0.037087
-190.08
0.016962
-831.12
155.40
201.84
-1218.54
0.056445
-46.44
0.052983
201.84
0.037617
-1218.54
7
230
0.99977
1305.78
1305.78
8
250
0.99975
469.80
469.80
0.037087
0
69.56
0.001391
539.36
539.226030
0.001391
539.227420
103
C(ppm) 0
20
155.4 (0)
80
Pressing Showers
100
170
200
230
250
1264.5
(230)
155.4 (100)
831.12 (0)
Forming Showers
201.84(0)
Other
41.28
(230)
201.84(170)
White Water Tank
398.52 (170)
751.32 (0)
DIP
14.7 (0)
54 (250)
34.68 (0)
415.8
(250)
AF
Chemical Preparation
LEGEND
Water-using operation
number Stream flowrate (ton/hr)
Water streams
(number) Stream Concentration
Concentration pinch point
(ppm)
Concentration interval boundaries
Figure 5.11: Existing water network in CBD form for case study 2
The next part of network diagnosis is to identify and eliminate the crosspinch streams. A cross-pinch stream is any stream that crosses the water pinch in the
existing water network thereby resulting in excessive use of fresh water. For case
study 2, there is only one cross-pinch stream. This is the wastewater stream leaving
DIP that is fed to AF (Figure 5.12). Next, the cross-pinch streams are eliminated
because water sources above the pinch (including fresh water) should not be feed to
demands below the pinch (Hallale, 2002). Therefore, the wastewater stream leaving
DIP that is fed to AF is eliminated.
104
C(ppm) 0
20
155.4 (0)
80
Pressing Showers
100
170
200
230
250
1264.5
(230)
155.4 (100)
831.12 (0)
Forming Showers
201.84(0)
Other
41.28
(230)
201.84(170)
White Water Tank
398.52 (170)
DIP
751.32 (0)
54 (250)
14.7 (0)
34.68 (0)
415.8
(250)
AF
Chemical Preparation
LEGEND
Water-using operation
number Stream flowrate (ton/hr)
Water streams
(number) Stream Concentration
Concentration pinch point
(ppm)
Concentration interval boundaries
Figure 5.12: Identified cross-pinch stream for case study 2
The second step of this technique is network retrofit. At this stage, the
existing water network was completed using the design rules developed by Hallale
(2002) presented in chapter 2. The preliminary retrofit scheme of case study 2 is
presented in Figure 5.13. Due to the eliminated cross-pinch stream, the demand for
AF was satisfied by the wastewater from Forming Showers. The wastewater leaving
Forming Showers can be fed to regions above and below the pinch as it was at the
pinch concentration.
105
C(ppm) 0
20
155.4 (0)
80
Pressing Showers
100
170
200
230
250
1210.5
(230)
155.4 (100)
831.12 (0)
Forming Showers
201.84(0)
Other
41.28
(230)
201.84
(170)
White Water Tank
398.52 (170)
DIP
751.32 (0)
469.8
(250)
54 (230)
14.7 (0)
34.68 (0)
AF
Chemical Preparation
LEGEND
Water-using operation
number Stream flowrate (ton/hr)
Water streams
(number) Stream Concentration
Concentration pinch point
(ppm)
Concentration interval boundaries
Figure 5.13: Preliminary retrofit design for case study 2
In the final step, the preliminary retrofit design of the existing water network
was optimised to reduce the total cost required to retrofit the existing water network.
This was done by reusing the wastewater from Forming Showers in the preliminary
retrofit design. As the wastewater stream for case study 2 appears at the water pinch,
the wastewater can be reused in the regions above and below the pinch (Hallale,
2002). Figure 5.14 illustrates the final retrofit design for case study 2 after reusing
the wastewater from Forming Showers while Figure 5.15 presents the conventional
flowsheet for the retrofitted network for case study 2.
106
C(ppm) 0
20
80
100
141.89 (0) Pressing Showers
542.03 (0)
13.51 (230)
170
200
230
250
106.9
(250)
155.4 (100)
Forming Showers
289.09 (230)
114.08(0)
Other
87.76 (230)
41.28
(230)
201.84
(170)
White Water Tank
398.52 (170)
705.3 (230)
46.02 (0)
3.01
(230)
8.96 (0)
31.67 (0)
469.8
(250)
DIP
59.74 (230)
AF
Chemical Preparation
LEGEND
Water-using operation
number Stream flowrate (ton/hr)
Water streams
(number) Stream Concentration
Concentration pinch point
(ppm)
Concentration interval boundaries
Figure 5.14: Final retrofit design for case study 2
After retrofit of case study 2, the total consumption of fresh water and
generation of wastewater was reduced 55.5% and 65.7% respectively. Table 5.8
represents the comparison of fresh water usage and wastewater generation before and
after retrofit for case study 2 with the total cost savings.
Table 5.8: Comparison of fresh water consumption and wastewater generation
before and after retrofit.
Fresh water
consumption
Wastewater
generation
Before
retrofit
(ton/h)
After
retrofit
(ton/h)
Flowrate
reduction
(ton/h)
% savings
Total
savings
($/yr)
1989.06
884.65
1104.41
55.5 %
376,000
1680.30
575.89
1104.41
65.7 %
2,593,000
107
Fresh water
884.65 ton/h
10
11
31.67 ton/h
8.96 ton/h
Paper Machine
1 141.89 ton/h
5 155.4 ton/h
Pres
sing
Sect
R1 13.51 ton/h
230 ppm
230 ppm
2 542.03 ton/h
8 201.84 ton/h
White Water
Tank
100 ppm
6 147.37 ton/h
CP
170 ppm
7 398.52 ton/h
R6 3.01 ton/h
9 106.09 ton/h
170 ppm
Forming
Section
230 ppm
230 ppm
R2 289.09
ton/h
R3 705.3 ton/h
R4 87.76
230 ppm
230 ppm
ton/h
R5 59.74. ton/h
230 ppm
230 ppm
3 46.02 ton/h
DIP
1254 ton/h
250 ppm
4 114.08 ton/h
Others
AF
13469.8 ton/h
250 ppm
Figure 5.15: Conventional flowsheet for the retrofitted network for case study 2
Wastewater
575.89 ton/h
108
5.2.4
Summary of the Developed Water Network Retrofit Technique for Nonmass Transfer-based Water–using Operations
A retrofit design procedure for an existing non- mass transfer-based water
network has been developed. In this section, it has been shown that this method is
quick and simple to use for retrofit purposes and is able to reduce the total fresh
water consumption and wastewater generation in an existing water network. This
technique consists of three steps: Network Diagnosis, Network Retrofit and Network
Evolution. In the diagnosis stage, CBD was introduced to represent the existing
water network. Next, modifications based on a set of rules were performed on the
existing water network to obtain the preliminary retrofit scheme. The final part of
retrofit was network evolution, which involved wastewater reuse to achieve the final
retrofit scheme.
Case study 2 was utilised to demonstrate the impact of this
procedure. It was that the methodology developed has managed to reduce 55.5% of
fresh water consumption and 65.7% wastewater generation for case study 2.
5.3
Retrofit of Water Network with Regeneration Units Optimisation
5.3.1
Problem Statement and Basic Assumptions
The problem of retrofitting water network with regeneration units
optimisation can generally be stated as follows:
Given a set of mass transfer-based and non-mass transfer-based water- using
processes with a set of regeneration units, it is desired to retrofit an existing
water distribution network by optimising one or more treatment process(es)
to restructure process streams and more effective use of existing process units
to accomplish the best savings in operating costs, subject to a minimum
payback period or/and a maximum capital expenditure.
109
The following assumptions were made in developing the retrofit procedure:
1. The system operates as a single contaminant system.
2. The system operates isothermally.
3. Regeneration reuse / recycling are allowed in the system.
5.3.2
Case Study 3
Pulp and paper industry is one of the many process industries that is currently
under regulatory pressure to reduce the volume and toxicity of its wastewater.
Efforts have been made to reduce water utility through wastewater reuse, recycling
and regeneration (Tripathi, 1996; Jacob et al., 2002; Parthasarathy and
Krishnagopalan, 2001). A paper making process with existing regeneration units is
now utilised to illustrate the newly proposed methodology. A simplified diagram of
the existing water network for case study 3 is shown in Figure 5.16. The mill
currently treats river water with high content of suspended solids and dissolved
solids to fulfil its fresh water demand. As shown in Figure 5.16, the existing water
network is highly integrated and the mill is currently consuming 435.6 ton/h of fresh
water and generating an equal amount of wastewater.
In the paper mill, the waste papers are blended with dilution water and
chemicals in the pulpers to form pulp slurry called stock. The stock is then sent to
the forming section of the paper machine for paper sheet formation. The paper
machine receives 180 ton/h of fresh water via streams 1 and 2 as well as recycle
water from second saveall disc filter, SDF2 (via stream 10) and clarified water tower,
CWT (streams 3 and 4). These water sources are used to eliminate any debris in the
forming and pressing sections of the paper machine. Besides, water is removed from
the stock in the forming and pressing sections of the paper machine.
110
Fresh Water
15 18 ton/h
435.6 ton/h
Paper Machine
1 54 ton/h
6 68.4 ton/h
For
min
g
Saveall Disc Filter 1
2800ppm
7 169.2 ton/h
2800ppm
2 126 ton/h
12 68.4 ton/h
Pressing
Section
8 1130.4 ton/h
4
154.8 ton/h
676.8
160ppm
ton/h
18
201.6 ton/h
36 ton/h
250ppm
Saveall Disc Filter 2
19
16
190.8 ton/h
104.4 ton/h
177ppm
160ppm
13 1130.4 ton/h
Saveall Disc Filter 3
Clarified
Water Tower
150ppm
2800ppm
3
Deculator
17
10 169.2 ton/h
100ppm
160ppm
11 396 ton/h
5
1245.6 ton/h
730ppm
160ppm
9 813.6 ton/h
1063ppm
Chemical
Preparation
DIP
14 244.8 ton/h
1063ppm
Figure 5.16: Existing water network for case study 3
RCF
DAF
Wastewater
20 435.6 ton/h
150 ppm
111
Water leaving the paper machine is mixed with recycled water (stream 5)
from de- inking pulper (DIP) before it is sent to a series of SDFs for fibers recovery
(streams 6, 7 and 8). Part of the mixed water is also sent to the recycled fiber plant
(RCF) for cleaner stock production (via stream 14) while the rest is recycled to the
DIP for main stock ink removal (stream 9). Recovered fiber from SDF and RCF is
sent to the paper machine for reuse (not shown in Figure 5.16).
Effluent from SDF1 and CWT (streams 12 and 16) are sent for reuse in the
deculator, while the remaining water required by the deculator is supplemented with
fresh water (stream 15).
Water fed to the deculator is used to flush away
contaminants that are heavier than fibers. Upon the completion of deculation process,
its effluent is sent to the RCF unit (stream 19). CWT on the other hand receives
filtered water from SDF3 via stream 13 and freshwater via stream 17. The huge
quantity of water in the clarifier prevents fibers from settling to the bottom of the
tower. Fresh water is used to dilute deinking chemicals during chemical preparation
(stream 18). In addition, DIP received water discharged from CWT via stream 11.
Finally, effluent from the RCF unit is sent to a dissolved air flotation (DAF)
unit for suspended solids removal. Solids from the DAF unit are returned to slud ge
tanks whereas clear water is sent the effluent treatment plant before being discharged
to the environment (stream 20).
A closer observation indicates that there are four process equipment that are
functioning as regeneration units in the existing water network, i.e. the DAF unit and
three SDFs.
DAF is a commonly used water purification technique to remove
suspended solids (TSS) from the process streams.
This separation process is
operated by introducing fine gas (usually air) bubbles into the wastewater to attach
and lift the particles to the water surface for TSS removal. Hence, wastewater leaves
the unit with higher purity. During operation, a portion of the DAF tank effluent is
normally recycled, pressurised and semi-saturated with air before re-entering the tank.
The efficiency of a DAF tank can be improved by adding flocculating chemicals into
the effluent before it is mixed with pressurised recycle (Eckenfelder, 2000).
112
On the other hand, SDF is widely used as thickening device in the pulp and
paper industry to remove solids from wastewater. It is operated by passing the
wastewater stream through filter mediums supported by disks. The solid content of
the wastewater will be trapped by the filter mediums and finally disposed off. Prior
to the development of polymer-type flocculants, disc filters operate in a 100%
submergence mode (Rousseau, 1987). Therefore, the efficiency of SDF can be
improved by the addition of polymer-type flocculants into its effluent.
From the above description, it is clear that that both DAF and SDFs units are
functioning as water regeneration units that partially purify water streams for reuse
and recycle in the water network.
These existing regeneration units should be
optimised to achieve further water utility saving before any new regeneration unit is
installed during any water retrofit work.
Table 5.9 summarise the limiting data of the water demands and sources for
the case study. Water demand refers to the water requirement in a water- using
operation while water source refers to the effluent streams from a water- using
operation. The most significant water quality factor in a paper mill, i.e. the total
suspended solid (TSS) was taken as the main factor considering water reuse and
recycle in this work. The economic data associated with regeneration units of DAF
tank and SDFs for optimisation study are summarised in Table 5.10. These data are
extracted from various literature sources (Arundel, 2000; Koppol et al., 2003; Perry
and Green, 1997; Peter and Timmerhaus, 1980; Intelligen, 2000; Tchobanoglaus and
Burton, 1991; Wiseman and Ogden, 1996).
113
Table 5.9: Limiting water data for case study 3
Process Description
Water
Demand
Flowrate, F
(ton/h)
Concentration, C
(ppm)
Pressing 1
1
126.0
20
Forming 1
2
54.0
20
Deculator 1
3
18.0
20
CP
4
36.0
20
CWT 1
7
201.6
20
Pressing 2
8
169.2
100
CWT 2
9
1130.4
150
Pressing 3
10
676.8
160
Forming 2
11
154.8
160
Deculator 2
12
104.4
160
DIP
13
396.0
160
Deculator 3
14
68.4
250
Process Description
Water
Source
Flowrate, F
(ton/h)
Concentration, C
(ppm)
SDF 2
1
169.2
100
DAF
2
435.6
150
SDF 3
3
1130.4
150
CWT
4
1332.0
160
SDF 1
5
68.4
250
Table 5.10: Economic data for regeneration units
Dissolved air flotation
(DAF)
Saveall disc filter
(SDF)
Cout,min
30 ppm
30 ppm
Hydraulic loading rate
20 m3 /m2 /day
6 m3 /m2 /day
Existing operating cost
$ 0.131 /ton
$ 0.174 /ton
Operating cost (30 ppm)
$ 0.150 /ton
$ 0.179 /ton
Costing equation
C = 2,310.6A + 260,292
C = 63,300*(A/9.3)0.48
Maximum area per unit
400 m2
140 m2
Recycle flowrate
50 %
-
Piping estimation
16% of capital investment
16% of capital investment
114
5.3.3
Selection of Optimisation Parameter for Existing Regeneration Units
When more than one regeneration units are found in a water network, like in
case study 3, one will have to choose to start optimisation with either one of these
units. However, a better means to start the optimisation study is to determine the
process parameter that will have greater impact on the overall network utility
consumption. Feng and Chu (2004) pointed out that two process parameters for
optimising a regeneration unit are the regeneration flowrate and the regenerator
outlet concentration.
Later in this chapter, it will be shown that the choice of
optimisation parameters is governed by the purity of the regeneration units outlet
streams (source) relative to the pinch purity.
In order to optimise an existing regeneration unit in a water network, we
begin by establishing the utility targets and the pinch point for the problem. This
will allow us to identify the source locations of these units relative to the pinch, and
enable determination of the optimisation parameters leading to the overall utility
reduction of the network. Identification of the regio ns above and below the pinch
through WCA is important to assess the integration of existing regeneration units for
the case study. Since a regeneration unit functions to purify a process stream, its
outlet (source) concentration must be lower than its inlet (demand) concentration.
Hence a regeneration unit which draws water from above the pinch can discharge its
source to the same region but at a lower concentration level. On the other hand, a
regeneration unit which draws water demands from below the pinch can discharge its
water source back to the same region (at a lower concentration), or at the pinch
purity or to the region above the pinch.
Referring to the WCT (Table 5.11) for this case study, SDF2 draws its water
demand and return its source to the same region, i.e. to the region above the pinch.
On the other hand, SDF1, SDF3 and DAF units draw their demands from region
below the pinch, SDF3 and DAF return their sources at the pinch purity, while SDF1
sends its source to the region below the pinch.
115
Table 5.11: WCT for case study 3 in grassroots design mode
Cumulativ
Level
k
Concentration Ck
Purity,
(ppm)
Pk
Σ FD, j
(ton/h)
Σ FS, i
(ton/h)
ΣFD, j + ΣFS, i
(ton/h)
Pure
e flowrate, FC , water surplus
(ton/h)
(ton/h)
Cumulative
pure water surplus
(ton/h)
377.52
0
1
0
1.000000
2
20
0.999980
-435.6
3
100
0.999900
-169.2
169.2
0
4
150
0.999850
-1130.4
1566
435.6
5
160
0.999840
-1332
1332
0
6
250
0.999750
-68.4
68.4
0
7
1000000
0
377.52
0.00755
-435.6
0.0075504
-58.08
-0.004646
0.002904
-58.08
-0.002904
0
377.52
0.003775
0.0037752
377.52
0.033977
0.037752
377.52
377.4256
377.463372
116
For regeneration units that produce sources in the region above the pinch (i.e.
SDF2), optimisation should be preformed by increasing its regeneration flowrate
and/or by upgrading its purity. This is due to the region above the pinch being the
most constrained region terms of water purity (Hallale, 2002). Thus larger amount of
water source (with purity higher than the pinch purity) is needed in this region. In
other words, the ability to produce purer water source can lead to significant
reduction in the amount of fresh water needed in the network. Therefore, SDF2 in
this case study can be optimised by increasing regeneration flowrate and/or
upgrading the outlet water purity.
On the other hand, one should only optimise existing regeneration units that
generate water sources in the region below the pinch (SDF1) by upgrading its purity.
To ensure water utility reduction, the source from these units should be purified (or
upgraded) at least to the pinch purity, or preferable higher than the pinch purity.
Note that if the water source of a regeneration unit is purified to below the pinch
purity, the overall water utility requirement will not be affected as water is taken
from a region with surplus of water and is returned to the same region (Hallale,
2002). Note also that it would be point lass to increase the regeneration flowrate for
regeneration units located below the pinch since this does not reduce the utility
requirement.
In conclusion, it would be only beneficial to optimise SDF1 by
upgrading its outlet concentration to 150 ppm or lower.
For regeneration units with water sources located at the pinch purity (the
pinch-causing sources) such as for DAF and SDF3, a portion of these sources
belongs to the region above the pinch, while the rest in the region below the pinch.
The source location of these regeneration units was identified and optimised
independently depending on where they exist relative to the pinch. For the portion
above the pinch, optimisation in terms of quality (purity) and quantity (flowrate) can
be carried out. However for the portion of the source which exists below the pinch,
optimisation by upgrading the outlet purity should be carried out. Note from Table
5.11 that most of the flowrate of the source for DAF exist below the pinch while the
source from SDF3 has been directly used to satisfy the demand at the pinch purity.
117
Thus optimisation should only be focused on upgrading the outlet purity of DAF
source.
5.3.4
Retrofit Targeting
This section presents the technique to integrate optimisation of regeneration
units into a water network retrofit project. Although water regeneration is a common
practice, however, most regeneration units are usually not operating at its maximum
capacity and/or minimum outlet concentration. There are often rooms for optimising
the process parameters to further reduce the utility consumption during water
network retrofit. For instance, a regeneration unit (with a source stream located in
the region above the pinch) that is operated at 80% of its maximum capacity can
consider flowrate increment.
This generates a larger amount of water at higher
purity for reuse/recycle in the network. Consequently, further utility savings can be
achieved. Similarly, regeneration units should always be considered to operate at the
lowest possible (minimum) outlet concentration, Cout,min to produce water of higher
quality and include further savings in utility consumption.
For case study 3, all existing regeneration units (DAF tank and SDFs) were
observed to operate at 80% their maximum capacity. Besides, these units were also
observed to operate at a lower outlet concentration as opposed to their minimum
outlet concentration of 30 ppm (Table 5.10). Note that, all retrofit targets were
determined based on a limit on payback period of 2 years.
5.3.4.1 Comparison of Estimated Incremental Cost
Optimisation of a regeneration unit involves the search for the unit’s optimum
regeneration flowrate (Freg) and outlet concentration (Cout ). Often, deviations exist
between the design and the operating parameters of a regeneration unit.
For
regeneration, a flowrate deviation of ∆Freg is the difference between the optimised
118
regeneration flowrate and the existing flowrate. Similarly, a deviation of ∆Cout is the
difference between the optimised and the existing outlet concentration of the
regeneration unit. For ease of analysis, an initial comparison was made between the
estimated incremental costs for these process variables. The estimated incremental
cost refers to the additional operating cost requirement when optimisation is
performed on the existing regeneration units. It plays an important role in assisting a
designer to make decision on which process parameter to be optimised first and
provide quick cost estimation associated with these process changes.
When an existing regeneration unit purify a larger quantity of water at a fixed
outlet concentration, Cout,existing more water treatment chemicals are required.
Therefore, the estimated cost increment is attributed to the increase in operating cost
due to higher regeneration flowrate. The total operating cost for all regeneration
units ∆Cost Freg was calculated as the sum of the individual regeneration unit’s
incremental cost, as follows:
∆Cost Freg = ∑ (Cost F , i ∆Freg,max, i )
(5.9)
i
where Cost F, i ($/ton) is the operating cost of regeneration unit i with Cout,existing and
∆Freg,max,i (ton/h) is the maximum regeneration flowrate increment for regeneration
unit i.
On the other hand, upgrading the outlet purity (Cout ) of an existing
regeneration unit requires a higher amount of water treatment chemicals to remove
additional mass load, ∆M.
The resulting incremental cost for optimisation by
upgrading the outlet purity, for all regeneration units was defined as the sum of
incremental operating cost for all the individual regeneration units, as follows,
∆Cost C o u t = ∑ (Cost M , i ∆M reg, max, i )
i
(5.10)
119
where Cost M, i is the operating cost of regeneration unit i based on total mass load
removed ($/kg), and ∆Mreg,max,i is the maximum additional mass load removed by
regeneration unit i (kg/h).
Comparison between the estimated cost increment calculated from equations
5.9 and 5.10 enables a designer to begin an optimisation study based on the cheaper
retrofit option. Recall that, SDF2 was optimised by increasing Freg and lowering Cout ,
while SDF1 and DAF were optimised by lowering Cout . The maximum regeneration
flowrate ∆Freg,max,i for SDF2 was 42.3 ton/h, based on an existing regeneration
capacity of 80% (169.2 ton/h; Table 5.9).
On the other hand, the maximum
additional mass load removal (∆Mreg,max,i ) for SDF1, SDF2 and DAF were 15.05
kg/h, 11.84 kg/h and 52.27 kg/h respectively (based on a maximum Cout of 30 ppm).
Using equations 5.9 and 5.10, ∆Cost Freg and ∆Cost Cout for case study 3 were $
18,314/yr and $ 623,030/yr respectively. Therefore, optimisation of regeneration
units for this case study should first consider the option of increasing Freg, due to the
lower incremental cost. Once the Freg option has been considered, one can then
explore the option of lowering Cout .
5.3.4.2 Optimisation of SDF2 with Increased Freg
Utility consumption of a water network si expected to reduce due to the
increased flowrate in a regeneration unit. However, for case study 3 the optimum
regeneration flowrate, i.e. ∆Freg,optimum for SDF2 should only fall within the range of
0 = ∆Freg = 42.3 ton/h.
Figure 5.17 shows the FFW (fresh water flowrate) target versus ∆Freg plot for
the grassroots water network obtained using the WCA technique (Manan et al.,
2004a). This plot shows a constant reduction in fresh water consumption with the
increase of ∆Freg value for optimum grassroots design. Note however, that due to the
various constraints present in an existing process, the grassroots target may not be
achievable during retrofit.
120
450
Existing network
Retrofit profile
with ∆a ∆F = 1
Doubtful Economic
F FW (ton/h)
430
Retrofit profile
with a ∆F = 0.8713
410
Economical Project
Optimum
grassroots
design
390
370
Infeasible
350
0
5
10
15
20
25
30
35
40
45
∆F reg (ton/h)
Figure 5.17: FFW versus ∆Freg plot for optimisation of SDF2 through increasing Freg
For optimisation of regeneration units, a new retrofit parameter called the
“fresh water efficiency”, a ∆F is defined as follows:
 F

α ∆F =  FW, target 
 FFW,existing  ∆Freg
(5.11)
For a fixed ∆Freg, the FFW, target and FFW, existing refer to the fresh water target
for grassroots design and the existing network respectively. a ∆F value is a measure of
the proximity of an existing fresh water consumption to the targeted fresh water in a
grassroots design. An α ∆F value of unity means that the existing water network has
matched the utility targets in a grassroots design. For case study 3, an α ∆F = 0.8713
was obtained.
Following the analogy of ∆aArea (Silangwa, 1986; Shenoy, 1995; Polley,
2000), an alternative definition of fresh water efficiency for a given value of flowrate
increment of existing regeneration unit, ∆(∆Freg) can be defined.
α ∆F can be
121
alternatively defined as the ratio between the fresh water target reduction in a
grassroots design, ∆FFW, target to that for an existing network, ∆FFW, existing, as follow:
 ∆FFW, target 

∆α ∆F = 

∆
F
 FW, existing ∆ (∆Freg )
(5.12)
Figure 5.17 also shows a retrofit profile that was plotted using the calculated
α∆F value of 0.8713 and ∆α ∆F value of 1.0000, based on an existing utility
consumption of 435.6 ton/h. As shown, retrofit path with the constant α F value of
0.4264 proven to be a better choice, since it approaches the utility targets of an ideal
grassroots design. The plot also shows constant utility reduction with ∆Freg until an
Freg,max value of 42.3 ton/h. Note that the utility cost savings is based on the equal
reduction of fresh water (represented by y-axis) as well as wastewater. Meanwhile,
capital investment was based on cost estimation (see data given in Table 5.10).
Figure 5.17 resembles the retrofit profiles for heat integration (Tjoe and
Linnhoff, 1986) and mass integration (Fraser and Hallale, 2000) problems. The
retrofit profile (Figure 5.17) consists of three main regions. For an α ∆F value of
0.8713, the region below the optimum grassroots design curve is an infeasible region
since it is impossible to achieve a utility consumption lower than that of the optimum
grassroots design.
The region between the optimum grassroots design and the
retrofit profile is term as the economical design region where the desired retrofit
targets may exist. Finally, it is uneconomical to achieve retrofit targets in the region
above the retrofit profile as no savings can be achieved.
The economics of the retrofit option is assessed next. This includes cost
savings in fresh water and wastewater utilities as well as capital investment during
retrofit. The next utility savings during water network retrofit was calculated based
on the difference between the total savings from reduction in water utilities and the
operating cost increment from ∆Freg.
On the other hand, the required capital
investment covers the cost of piping investment for network modifications, which
was estimated from the capital investment of a newly installed regeneration unit(s).
122
For most regeneration units, capital investment is estimated based on the size of
regeneration unit as follows:
Size of regenerati on unit =
Maximum regenerati on flowrate
Hydraulic loading rate
(5.13)
The retrofit economics can be better assessed through the savings versus
investment plot which shows the potential utility savings for a given capital
investment. Figure 5.18 shows such plot for the optimisation of SDF2 with increased
Freg. As shown, the plot appears as a vertical straight line with a fixed capital
investment as a result of the independence on piping investment. With the total
savings of $ 4,400/yr and an investment of $ 152,200, the payback period for the
project was calculated at 34.5 years. Note that the high cost of piping investment
was due to high capital cost of the existing SDF2. The long payback period achieved
obviously does not fulfil the 2 years economic criteria for this case study. Hence,
optimisation of SDF2 by increasing Freg had proven to be an uneconomical retrofit
option and would not be implemented for this case study. The option of lowering
Cout was explored next.
34.5 years
4,000
2,000
Savings ($/yr)
0
0
-2,000
20,000
40,000
60,000
80,000 100,000 120,000 140,000 160,000 180,000
Investment ($)
-4,000
-6,000
-8,000
-10,000
Figure 5.18: Savings versus investment plot for optimisation of SDF 2 through
increasing Freg
123
Note that the poor economics in this case study may not necessary apply for
other retrofit cases. Cases exists where Freg increment may be economical and
fulfilling the desired retrofit targets. When this happens, one may either choose to
proceed with optimisation of existing regeneration units by lowering Cout or proceed
to the network design stage.
5.3.4.3 Optimisation of DAF with Lowered Cout
Since it is uneconomical to retrofit case study 3 through Freg, we will now
focus on lowering Cout to produce water for reuse/recycle in the network. For the
existing regeneration unit, it is also necessary to target the optimum outlet
concentration reduction, ∆Cout, optimum that lies within the range of the existing and the
minimum outlet concentration reduction, i.e. 0 = ∆Cout,optimum = ∆Cout,max .
Options for optimisation exist due to the existence of multiple units in a
network. Choosing to optimise either a single regeneration unit or a combination of
units will result in different outcomes. Individual optimisation of the regeneration
units to an outlet (minimum) concentration of 30 ppm yields grassroots fresh water
targets of 322.8 ton/h, 298.56 ton/h and 145.2 ton/h, for the optimisation of SDF1,
SDF2 and DAF units respectively. Note that the source for SDF3 has been used to
satisfy another water demand at the pinch purity and hence, not considered in the
optimisation study.
The preceding results show that a regeneration unit with higher regeneration
flowrate (i.e. DAF) enable bigger utility savings in a water network. However, as
shown by Koppol et al. (2003) water utility of a network will remain unchanged after
a portion of regenerated water is reused and/or recycled in a network. This is due to
the water network reaching its reuse/recycling bottleneck. Hence, it is important to
locate the minimum upgrade flowrate, Fupgrade,min that defined the boundary for
optimisation.
124
To determine the value of Fupgrade,min , a FFW versus Fupgrade plot was
constructed in Figure 5.19. Figure 5.19 shows that for a grassroots water network,
fresh water utility reduces steadily with the reuse/recycle of regenerated water until
the Fupgrade,min value of 290.4 ton/h is reached. This corresponds to the minimum
fresh water flowrate of 145.2 ton/h (equals the fresh water target with DAF tank
optimisation).
This means that the minimum limit for optimisation of the
regeneration unit is at 290.4 ton/h to enable maximum utility savings. Out of the
three regeneration units of SDF1, SDF2 and DAF, the only unit which can handle
this amount of regeneration flowrate is the DAF unit. Hence, optimisation is only
needed for DAF as it process as water flowrate higher than the Fupgrade,min value
(435.6 ton/h). Next, we consider the option to lower Cout .
400
350
300
F FW (ton/h)
250
200
150
Fupgrade,min
100
50
0
0
50
100
150
200
250
300
350
400
F upgrade (ton/h)
Figure 5.19: FFW versus Fupgrade for optimisation of SDF1 and DAF through
upgrading Cout
To determine the value of ∆Cout,optimum another tool called FFW,
min
versus
∆Cout plot is needed. Such a plot is shown in Figure 5.20, where various FFW, min
targets were determined for a grassroots water network when DAF was optimised for
various ∆Cout value, in the range of ∆Cout,max . The ∆Cout,max value which was the
difference between the existing outlet concentration Cout,existing (150 ppm) and Cout,min
125
(30 ppm) for DAF unit was calculated as 120 ppm. Note that less fresh water is
consumed due to higher purity of regenerated water in the water network. Note also
that the curved shape of the plot in Figure 5.20 is due to the existence of multiple
pinches in the water network (refer to the WCT in Table 5.12).
450
Doubtful Economic
Retrofit profile
with a ∆C = 0.7410
400
Economic Project
350
F FW (ton/h)
300
Optimum
grassroots design
250
200
150
Infeasible
100
50
0
0
20
40
60
80
100
120
∆ C out (ppm )
Figure 5.20: FFW versus ∆Cout for optimisation of DAF through upgrading Cout
As in the case of Freg optimisation, for a fixed value of ∆Cout , the “fresh water
efficiency, a∆C” is defined to establish the capital investment target. Equation 5.14 is
a new a∆C value defined as the ratio between fresh water targets in a grassroots
design, FFW, target to that in an existing network, FFW, existing , for a fixed ∆Cout .
 FFW,target 

α?C = 
F

 FW,existing  ∆C o u t
(5.14)
a ∆C value is a measure of the proximity of an existing fresh water consumption to the
targeted fresh water in a grassroots design. An a∆C equal of unity means that the
existing water network has matched the utility targets in a grassroots design. For
case study 3, an a∆C = 0.7410 was attained.
126
Table 5.12: WCT for case study 3 with F upgrade of 290.4 ton/h
Level
k
Concentration
Ck (ppm)
Purity,
Pk
Σ FD, j
(ton/h)
Σ FS, i
(ton/h)
ΣFD, j + ΣFS, i
(ton/h)
Cumulativ
Pure
Cumulative
e flowrate, FC
water surplus
pure water surplus
(ton/h)
(ton/h)
(ton/h)
145.2
1
0
1.000000
2
20
0.999980
3
30
0.999970
4
100
0.999900
5
150
6
0
145.2
-435.6
0.002904
-435.6
0.0029040
-290.4
290.4
290.4
-169.2
169.2
0
0.999850
-1130.4
1566
213.6
160
0.999840
-1332
1332
0
7
250
0.999750
-68.4
8
1000000
0
-0.002904
0
0
0
0
0
0
0
213.6
0.002136
0.0021360
213.6
0.019224
-68.4
0.0213600
145.2
145.1637
145.185060
127
A retrofit profile for DAF optimisation is shown in Figure 5.20 based on the
calculated α ∆C value of 0.7410. As shown, fresh water consumption is reduced with
the increase of ∆Cout value.
The retrofit path with a constant α ∆C value is a
reasonable and conservative retrofit choice. As in the case of Figure 5.17, Figure
5.20 also consists of three main regions: the infeasible design region, the region for
economical designs and the region with doubtful economics.
Next, a savings versus investment plot for optimisation of DAF by lowering
Cout plotted in Figure 5.21. Note that due to the independent of piping cost, Figure
5.21 appears to be a vertically straight line with a fixed capital cost investment. The
point where 0.57 years payback line touches the savings versus investment plot with
$ 0.37M capital investment and $ 0.66M savings determines the retrofit target for
optimisation of DAF by lowering Cout . Cout,optimum corresponds to this optimum point
is determined as 30 ppm. Hence it can be concluded that the economically feasible
optimisation option for this case study is achieved via the optimisation by lowering
Cout .
0.57 year
700,000
600,000
Savings ($/yr)
500,000
1 year
400,000
300,000
1.5 years
200,000
100,000
0
0
100,000
200,000
300,000
400,000
Investment ($)
Figure 5.21: Savings versus investment for optimisation of DAF through upgrading
Cout
128
5.3.4.4 Discussion
It should also be noted that selection of optimisation parameter (regeneration
flowrate and/or its outlet concentration) is strongly depending on the existing
operating range of regeneration units.
If the existing regeneration units are not
operating at their maximum capacity, flowrate optimisation shall be conducted. For
regeneration units that do not produce water at its minimum outlet concentration
Cout,min , lowering of Cout is needed. For regeneration units that do not operate at their
maximum capacity and minimum outlet concentration, optimisation of both process
parameters can be considered.
Note also that in water network retrofit, besides the straight line retrofit path
(Figure 5.22a), a curved retrofit path (Figure 5.22b) similar to those found in
conventional heat and mass integration problems may also exist particularly for
water network problems involving multiple pinches.
Existing
design
Existing
design
Targeted
retrofit design
with ∆a = 1
Targeted
retrofit design
with ∆a = 1
Targeted retrofit
design with
constant a
Targeted retrofit
design with
constant a
FFW
FFW
Optimum
grassroots
design
Optimum
grassroots
design
∆ Freg
∆ Freg
(a)
(b)
Figure 5.22: Two kinds of retrofit profiles (a) curve paths (b) straight paths
129
5.3.5
Retrofit Design
Prior to retrofit of water network to achieve the established utility targets, the
pinch location(s) for an existing water network must be identified.
The pinch
location divides water network into regions above and below the pinch. Various
network design procedures can be applied independently in these regions to achieve
the retrofit targets.
To obtain the pinch point(s), the limiting data with optimised parameter (Cout
of 30 ppm) for DAF were incorporated into WCA targeting technique (Manan et al.,
2004a). Table 5.13 shows the WCT for the case study. The water pinch for this
retrofitted network occurs at the third purity level of the WCT i.e. at 30 ppm. Hence,
retrofit design will be carried out independently in both regions above and below the
pinch purity of 30 ppm.
Concentration block diagram (CBD) presented in section 5.2.3 was used to
represent the existing water network of case study 3 which involved non- mass
transfer-based processes. CBD enables the existing water network to be view in the
context of the water flowrate as well as contaminant concentration. Figure 5.23
shows the CBD for case study 3.
The concentration interval boundaries are
represented by the vertical dashed lines. These lines correspond to a fixed limiting
inlet or outlet concentration. The boxes represent water-using operations according
to their inlet and outlet concentrations. The arrows in the diagram indicate the water
streams of the existing water network with streams flowrate in ton/h and contaminant
concentration in ppm (shown in parathesis).
130
Table 5.13: WCT for case study 3 with F upgrade of 435.6 ton/h
Level
k
Concentration
Ck (ppm)
Purity,
Pk
Σ FD, j
(ton/h)
Σ FS, i
(ton/h)
ΣFD, j + ΣFS, i
(ton/h)
Cumulati
Pure
ve flowrate, FC water surplus
(ton/h)
(ton/h)
Cumulative
pure water surplus
(ton/h)
145.2
1
0
1.000000
2
20
0.999980
3
30
0.999970
4
100
0.999900
0
145.2
-435.6
0.002904
-290.4
5
6
7
8
150
160
250
1000000
0.999850
0.999840
0.999750
0
-169.2
-1130.4
-1332
-68.4
0.002904
-435.6
435.6
435.6
169.2
0
1130.4
1332
68.4
-0.002904
0
145.2
0.010164
145.2
0.00726
145.2
0.002136
145.2
0.013068
145.2
145.1637
0.010164
0
0.017424
0
0.018876
0
0.031944
145.195644
131
C(ppm)
0
126(0)
20
100
150
160
177
250
730
2800
Pressing 1
Pressing 2
169.2
(100)
Pressing 3
54(0)
Forming 1
Forming 2
SDF 2
676.8
(160)
154.8
(160)
SDF 1
SDF 3
18(0)
Deculator 1
1130.4
(150)
6
8
.
Deculator 2
104.4 (160)
CWT 2
Deculator 3
201.6(0)
CWT 1
192.4 (160)
201.6 (160)
36(0)
DIP
CP
435.6 (150)
DAF
LEGEND
Water-using operation
number
Stream flowrate (ton/h)
Water streams
Concentration pinch point
Concentration interval boundaries
(number)
Stream Concentration (ppm)
Figure 5.23: Existing water network for case study 3 in CBD
Next, regeneration units identified for optimisation during the targeting stage
was re-designed according to the retrofit targets. Cout value for DAF was lowered to
the targeted outlet concentration of 30 ppm (Figure 5.24). After optimisation, the
water sources for DAF unit exist at the pinch point. This means that these sources
can be used in the regions above as well as below the pinch.
132
C(ppm)
0
126(0)
20
30
100
150
160
177
250
730
2800
Pressing 1
Pressing 2
169.2
(100)
Pressing 3
676.8 (160)
Forming 1
5
4
Forming 2
154.8 (160)
SDF 2
SDF 1
SDF 3
1
8
Deculator 1
1130.4
(150)
Deculator 2
CWT 2
104.4 (160)
Deculator 3
201.6(0)
CWT 1
201.6 (160)
36(0)
6
8
.
192.4 (160)
DIP
CP
435.6
(30)
DAF
LEGEND
Water-using operation
Water streams
number
Stream flowrate (ton/h)
(number) Stream Concentration (ppm)
Concentration pinch point
Concentration interval boundaries
Figure 5.24: Existing water network in CBD with optimised regeneration units for
case study 3
Then, one can begin retrofit of the existing water network to accomplish the
preliminary retrofit design. Firstly, cross-pinch streams in the water network were
eliminated. Note from Figure 5.24 that there is no cross-pinch stream in the network.
Therefore, the existing network was maintained.
In the final stage of this design, reuse/recycle of water network was
conducted to reduce the existing water utility. The final retrofit design of case study
3 after reusing wastewater from DAF is shown in Figure 5.25. Note that, the cleaner
sources from DAF were fed to 4 process units, i.e. Pressing1, Forming1, Deculator1,
CWT1 and the remaining to CP (Figure 5.25). These flowrates agree with the water
133
allocation targets identified in WCT.
Figure 5.26 presents the conventional
flowsheet for the retrofitted network for case study 3.
After retrofit, the utility
consumption of the existing water network has been reduced by 66.7% to 145.2 ton/h.
In this case study, the water utility after retrofit has achieved the grassroots targets.
However, this is a rare situation as most retrofit projects do not achieve the
grassroots targets (Tjoe and Linnhoff, 1986).
C(ppm)
0
42(0)
20
30
100
150
160
177
250
730
2800
Pressing 1
Pressing 2
84
(30)
169.2
(100)
Pressing 3
676.8 (160)
Forming 1
1
8
Forming 2
154.8 (160)
SDF 2
36
(30)
SDF 1
SDF 3
6
(
Deculator 1
1130.4
(150)
12
(30)
104.4 (160)
Deculator 3
CWT 1
CP
2
4
Deculator 2
CWT 2
67.2(0)
12(0)
6
8
.
134.4
(30)
201.6 (160)
192.4 (160)
DIP
DAF
145.2 (30)
LEGEND
Water-using operation
Water streams
Concentration pinch point
number
Stream flowrate (ton/h)
(number) Stream Concentration (ppm)
Concentration interval boundaries
Figure 5.25: Final retrofit design for case study 3
134
Fresh Water
15 6 ton/h
145.2 ton/h
Paper Machine
1 18 ton/h
6 68.4 ton/h
For
min
g
12 68.4 ton/h
Saveall Disc Filter 1
2800ppm
7 169.2 ton/h
Pressing
Section
8 1130.4 ton/h
Saveall Disc Filter 2
4
154.8 ton/h
676.8 ton/h
160ppm
19
16
190.8 ton/h
104.4 ton/h
177ppm
160ppm
13 1130.4 ton/h
Saveall Disc Filter 3
150ppm
2800ppm
3
18
67.2 ton/h
12 ton/h
250ppm
2800ppm
2 42 ton/h
Deculator
17
R4
10 169.2 ton/h
Clarified
Water Tower
12 ton/h
100ppm
R5
30ppm
160ppm
134.4 ton/h
11 396 ton/h
5
1245.6 ton/h
730ppm
R1
36 ton/h
30ppm
R2
84 ton/h
30ppm
30ppm
160ppm
9 813.6 ton/h
1063ppm
Chemical
Preparation
DIP
14 244.8 ton/h
1063ppm
RCF
R3
24 ton/h 30ppm
Figure 5.26: Conventional flowsheet for the retrofitted network for case study 3
DAF
Wastewater
20 145.2 ton/h
150 ppm
135
A total partial savings of $ 0.72M is achievable with an investment of $
0.37M for optimisation of DAF to 30 ppm in the existing water network. The
resulting payback period is 0.53 year which is slightly lower than the target (0.57
year). Note that the final retrofit design shown is one of the few possible retrofit
solutions that can be generated using this methodology. One may also include other
process constraints to achieve different network designs.
5.3.6
Summary of the Developed Water Network Retrofit with Regeneration
Units Optimisation
Optimisation of existing regeneration unit(s) provide opportunity to further
reduce utility saving before the installation of new regeneration unit. Hence, this
retrofit option possesses the advantage of low capital investment and minor process
changes over other retrofit approaches. A new two-stage approach based on pinch
analysis for retrofit of water network with the integration of existing regeneration
unit(s) optimisation has been presented. In the first stage, retrofit targets (utility
savings and capital investment) were determined for a range of total flowrate and/or
outlet concentration of the regeneration unit. Given a fixed payback period or capital
expenditure, the retrofit targets were determined from the saving versus investment
diagram. Once the retrofit targets were identified, the existing water network was redesigned to meet the established targets.
This methodology has successfully
achieved the retrofit targets prior to design and further minimise fresh water
consumption and wastewater generation in an existing water network.
136
5.4
Retrofit of Water Network with the Additional of New Regeneration
Units
5.4.1
Problem Statement and Basic Assumptions
The problem statement of retrofitting water network with regeneration can
generally be stated as follows:
Given a set of mass transfer-based and non-mass transfer-based water- using
processes, it is desired to retrofit an existing water distribution network by
adding one or more treatment process(es) to restructure process streams and
make more effective use of existing process units to accomplish the best
savings in operating costs, subject to a minimum payback period or/and a
maximum capital expenditure.
The following assumptions were made in developing the retrofit procedure:
1. The system operates as a single contaminant system.
2. The system operates isothermally.
3. Regeneration reuse / recycling are allowed in the system.
4. Single type of regeneration treatment.
5.4.2
Case Study 4
Reuse/recycling of water in paper mills is considered to be a universal
practice of recovering valuable paper fibres from a paper machine’s excess water
(Wiseman and Ogden, 1996). Water reuse and recycling can recover expensive raw
materials from the water network as well as operating cost savings and reduction in
the environmental impact of the process.
137
Case study 2 from section 5.3.2 is utilised to demonstrate the newly
developed methodology. A simplified version of the existing water network for this
case study is illustrated according to flowrate and contaminant concentration in
Figure 5.10.
Total suspended solid (TSS) is also taken as the main factor in
considering water reuse and recycling in this case study. Table 4.6 summarises the
water demand and source data for this case study.
5.4.3
Retrofit Targeting
In this section, a new technique to incorporate regeneration units into water
network retrofit is presented. Two typical physical treatments that were suggested to
purify water (by recovering fibre from the excess water of paper machines) are
dissolved air flotation (DAF) tank and saveall disc filter (SDF). Table 5.14 shows
the economic data associated with DAF tank and SDF that is extracted from various
literature sources (Arundel, 2000; Koppol et al., 2003; Perry and Green, 1997; Peter
and Timmerhaus, 1980; Intelligen, 2000; Tchobanoglaus & Burton, 1991; Wiseman
& Ogden, 1996).
DAF tank is a unit operation that removes suspended solids (TSS) from
wastewater and other industrial process streams. DAF tank is commonly used as
wastewater pre-treatment, product recovery and thickening of biological solids in
industries i.e. food processing, pulp and papers and petrochemicals. This separation
process is operated by introducing fine gas (usually air) bubbles into the wastewater
to attach and lift the particles to the water surface to be removed. Hence, wastewater
leaves the unit at higher purity. A portion of the DAF tank effluent is recycled,
pressurised and semi-saturated with air before re-entering the tank.
138
Table 5.14: Economic data for regeneration units
Dissolved air flotation
(DAF)
Saveall disc filter
(SDF)
Cout,min
30 ppm
30 ppm
Hydraulic loading rate
20 m3 /m2 /day
6 m3 /m2 /day
Existing operating cost
$ 0.131 /ton
$ 0.174 /ton
Operating cost (30 ppm)
$ 0.150 /ton
$ 0.179 /ton
Costing equation
C = 2,310.6A + 260,292
C = 63,300*(A/9.3)0.48
Maximum area per unit
400 m2
140 m2
Recycle flowrate
50 %
-
Piping estimation
16% of capital investment
16% of capital investment
On the other hand, SDF is widely used as thickening device in the pulp and
paper industry to remove solids from wastewater. It is operated by passing the
wastewater stream through filter mediums supported by disks. The solid content of
the wastewater will be trapped by the filter mediums and finally disposed off. This
leaves the wastewater in higher quality. Both regeneration units will be assessed in
the retrofit situation.
Feng and Chu (2004) stated that the capital and operating costs of a
regeneration unit are normally a function of regeneration flowrate (Freg) and the
outlet concentration (Cout ). By studying the effects of these variables on the added
regeneration unit, several retrofit cases can be explored in combination to target the
optimum retrofit design with the addition of regeneration unit.
Three cases
considered in this work include:
(i)
Vary Freg with fixed Cout
(ii)
Vary Cout with fixed Freg
(iii)
Vary both Freg and Cout
Each case is applicable for a specific situation. Case (i) involves a situation
where a fixed Cout is required from a regeneration unit.
Case (ii) applies for
situations where a fixed amount of regenerated water is needed in certain processes.
Case (iii) applies when there are no preferable Cout and Freg values during the retrofit
139
situation. To yield a specific retrofit target, it is necessary to select a reasonable
payback period for investment for each case.
For case study 4, the minimum achievable outlet composition, Cout,min for both
types of regeneration units (DAF tank and SDF) was set at 30 ppm (Wiseman and
Ogden, 1996). On the other hand, there is virtually no limitation for the value of Freg
for each regeneration unit, since this value will only affect the number of newly
installed regeneration unit(s) in the network. On the economic criterion, maximum
saving is highly preferable, while the maximum payback period was set for 2 years.
Application of the above-mentioned cases on case study 4 is described next.
5.4.3.1 Case 1: Vary Freg with Fixed Cout
For Case 1, the minimum outlet concentration for the regeneration unit,
Cout,min was fixed at 30 ppm while the regeneration flowrate, Freg was varied. The
objective of this case was to search for the optimum regeneration flowrate, Freg,
optimum
for the newly added regene ration unit(s). When a new regeneration unit was
installed in an existing water network, Freg amount of water at lower quality was
regenerated to a higher quality for reuse/recycle, thereby reducing the utility targets
of the network. Consequently, a bigger regeneration unit with a higher Freg enabled
further utility savings. However, as will be shown in the later section, utility savings
for the network tends to level off after a certain amount of regenerated water has
been reused or recycled in the network.
We term this maximum regeneration
flowrate to be Freg,max . Hence, the optimum regeneration flowrate, Freg,optimum will fall
in the range of 0 = Freg,optimum = Freg,max .
To acquire the value of Freg,max, a plot of fresh water flowrate (FFW ) target
versus Freg was generated for a grassroots design in Figure 5.27, utilising the WCA
targeting technique developed by Manan et al. (2004a). An example of the results
with Freg of 620.27 ton/h at Cout,min = 30 ppm is shown in water cascade table (WCT)
in Table 5.15. As shown in Figure 5.27, the fresh water target starts to level off at
140
the Freg,max of 620.27 ton/h. This is due to the water network having reached its
limitation in terms of reusing/recycling of the regenerated water.
2000
Retrofit profile with
∆αF = 1.0000
1800
1600
F FW (ton/h)
1400
1200
Best retrofit with
α F = 0.4264
1000
Various
retrofit
paths
800
600
Optimum
grassroots
design
400
200
Freg, max = 620.27 ton/h
0
0
100
200
300
400
500
600
700
800
900
1000
F reg (ton/h)
Figure 5.27: FFW versus Freg (Case 1)
Ideally, it is desirable to retrofit an existing network to achieve the minimum
utility targets identified in a grassroots design.
Nevertheless, this is always not
possible as various process constraints in an existing network may exist during
retrofit. Hence, we make use of the analogy of HEN and MEN retrofit approaches as
the basic framework for this newly proposed water network retrofit approach.
However, instead of adding more exchanger areas/stages (such as in the case of HEN
and MEN retrofit works), capital investments were allocated on new regeneration
units installation to further reduce the utility targets, apart from modifications of
existing network structure.
141
Table 5.15: WCT with 620.27 ton/hr of Freg with 30 ppm Cout
Interval
n
Concentration
Cn (ppm)
0
1
20
0.99998
30
0.99997
Purity,
Pn
Σ FD, j
(ton/h)
Σ FS, i
(ton/h)
ΣFD, j + ΣFS, i
(ton/h)
FC , (ton/h)
Pure water
surplus
(ton/h)
Cumulative pure
water surplus
(ton/h)
308.76
0
0.00002
308.76
-190.08
0.00001
620.27
0.99992
-831.12
0.9999
-201.84
155.40
0.99983
201.84
0.9998
-1218.5
0.99977
1155.31
0.99977
0.03695
-92.17
-0.00180
-138.61
-0.00970
63.23
0.00190
-1155.30
-0.03470
0
0
0.00736
0.04431
0.04247
0.03276
-1218.50
0.00003
230
738.95
201.84
0.00003
200
0.00119
-46.44
0.00007
170
118.68
-831.12
0.00002
100
0.00618
620.27
0.00005
80
0.00618
-190.08
0.03466
1155.31
0
0
142
Figure 5.28 shows a few possible water network retrofit profiles for this case.
It is obvious that the retrofit profile for water network was not exactly similar to the
retrofit profiles in HEN and MEN problems. In the case of water network retrofit,
the profiles originate from the utility consumption (fresh water flowrate) of the
existing network at the upper left portion of the FFW versus Freg plot and moves
towards the lower right portion of the plot. The best retrofit curve is typically the
curve that moves towards the utility targets of the grassroots network. Note that the
reduction of fresh water consumption (represented by y-axis) also leads to the
simultaneous reduction of wastewater flowrate.
Existing
design
Targeted retrofit
design with ∆a F = 1
Targeted retrofit
design with
constant a F
FFW
Existing
design
Targeted retrofit
design with ∆a F = 1
Targeted retrofit
design with
constant a F
FFW
Optimum
grassroots
design
Optimum
grassroots
design
Freg
(a)
Freg
(b)
Figure 5.28: Two kinds of retrofit profiles (case 1) (a) curve paths (b) straight paths
We next define a new retrofit parameter called the “fresh water
efficiency”, α F. For a fixed regeneration flowrate (Freg), α F is defined as the ratio
between fresh water target for a grassroots design (FFW,target ) to the fresh water
consumption of an existing network (FFW,existing), as given in equation 5.15:
 FFW, target 

α F = 

F
 FW, existing  Freg
5.15)
The α F value indicates how close the fresh water consumption in an existing
network as compared to that in a grassroots design. A value of unity for α F means
143
that the existing water network has achieved the utility targets in a grassroots design.
This is however almost impossible for most retrofit cases.
Following the analogy of ∆α Area (Silangwa, 1986; Shenoy, 1995; Polley,
2000), one may also define the “incremental value of fresh water efficiency”, ∆α F for
an increment of regeneration flowrate, ∆Freg.
∆α F can be defined as the ratio
between the decrease in fresh water target in grassroots design (∆FFW,target ) to that of
the decrease in fresh water consumption in an existing network (∆FFW ,existing), as
shown in equation 5.16:
 ∆FFW, target 

∆α F = 

∆
F
FW,
existing

 ∆Freg
(5.16)
Following equation 5.15 and 5.16, α F and ∆α F values for this case study were
calculated as 0.4264 and 1.0000 respectively. Two possible paths for retrofitting the
water network using the calculated α F and ∆α F values are shown in Figure 5.27. As
shown, retrofit path with the constant α F value of 0.4264 proven to be a better choice,
since it approaches the utility targets of an ideal grassroots design.
It should also be noted that two general types of retrofit profiles may exist in
water networks, i.e. a curved path which is similar to the conventional profiles for
heat and mass integration (Figure 5.28a) and a linear retrofit path (Figure 5.28b).
The curved path occurs for problems with multiple pinches while the linear profile is
most commonly found in most water network problems involving a single pinch
point, such as the one for this case study. We will limit our discussion to the linear
retrofit profile for the remaining of the text.
Figure 5.29 focuses on the left portion of Figure 5.27, i.e. portion where the
FFW versus Freg plot levels off at Freg,max = 620.27 ton/h.
For the ease of
demonstration, only the retrofit profile of α F = 0.4264 was shown. The graph can
now be divided into three main regions. The area below the optimum grassroots
design is termed as the infeasible region since it is impossible to achieve utility
reductions lower than that for the optimum grassroots design. The region in between
144
the optimum grassroots design and the retrofit profile is term as the economical
design region where the desired retrofit targets will possibly fall.
Finally, it is
uneconomical to achieve retrofit targets in the region above the retrofit profile since
no savings can be achieved.
2500
Existing
design
Targeted retrofit
design with a = 0.4264
2000
F FW (ton/h)
Doubtful Economics
1500
Optimum
grassroots
design
1000
Economic Project
500
Infeasible
0
0
100
200
300
400
500
600
700
F reg (ton/h)
Figure 5.29: FFW versus Freg plot with constant a (Case 1)
We next determine the economic performance of this retrofit case, via the use
of savings versus investment plot. This includes the determination of savings for
both fresh water and wastewater utilities as well as the capital expenditure during the
retrofit work.
Utility savings achieved during water network retrofit can be
calculated based on the difference between the total savings of water utility reduction
and the increment in operating cost due to the Freg. On the other hand, the capital
investment required during network retrofit covers the capital investment of a newly
installed regeneration unit(s) and piping modifications. For most regeneration units,
an estimation of the size of a regeneration unit can be made using the following
equation (Tchobanoglaus and Burton, 1991):
145
Size of regenerati on unit =
Total regenerati on flowrate
Hydraulic loading rate
(5.17)
Due to the difference in capital and operating cost, different savings versus
investment plots are needed to assess the two proposed regeneration units, i.e. DAF
tank and SDF. Figure 5.30 and Figure 5.31 illustrate the savings versus investment
plot for DAF tank and SDF respectively. As shown in Figure 5.30, there are three
different segment s of savings versus investment plot for the DAF tank. Each of these
segments represents the desired number of units based on the size calculated at
different Freg value. Payback period lines were also identified in the diagram to
enable designer to choose the optimum retrofit targets. A targeted investment limit
of 1.68 years (below 2 years with maximum utility savings) was identified for a
capital investment $3.91M and savings of $2.30M. Three DAF tanks are therefore
required to achieve this target.
2,500,000
1.7 years
Segment 3
2 years
Savings ($/yr)
2,000,000
1,500,000
Segment 2
Payback
lines
1,000,000
Segment 1
500,000
0
0
1,000,000
2,000,000
3,000,000
4,000,000
Investment ($)
Figure 5.30: Savings versus investment plot for DAF (Case 1)
Besides, the savings versus investment plot for SDF is shown in Figure 5.31.
A targeted investment limit of 1.92 years (below 2 years with maximum utility
savings) was identified for a capital investment and savings of $4.83M and $2.52M
146
respectively. However, the installation of 18 units of SDF was required to achieve
this target.
3,000,000
2,500,000
Savings ($/yr)
2,000,000
1,500,000
2 years
1.92 years
1,000,000
500,000
Payback lines
0
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
4,000,000
4,500,000
5,000,000
Investment ($)
Figure 5.31: Savings versus investment plot for SDF (Case 1)
It is also worth to point out that Figure 5.30 and Figure 5.31 are unlike the
retrofit situation in heat and mass integration where the total area or number of stages
is the only parameter to decide the retrofit option. However, there are more factors
to be considered in this work. For instance, installing 18 units of SDF may not be a
practical retrofit option for the case study, since a large area is required for all of
these regeneration units. Hence, it is up to the designer to decide which regeneration
units are to be chosen during network retrofit. In any case, the value of Freg,optimum
corresponding to both retrofit options is 620.27 ton/h.
147
5.4.3.2 Case 2: Vary Cout with Fixed Freg
For the second case of network retrofit, Freg was fixed at Freg,max while the
outlet concentration of the regeneration unit, Cout was varied. The objective of this
case was to determine the optimum outlet concentration of the regeneration unit,
Cout,optimum , which lies in the range of Cout,min = Cout,optimum = Cout,max . Table 5.14
indicates that the Cout,min for both regeneration units in this case study were fixed at
30 ppm. On the other hand, Cout,max could be based on the highest concentration
among the sources available in the water network. This is a reasonable basis since
no water shall be regenerated “dirtier” than the available water sources in an existing
network. From Table 5.6, Cout,max was identified at 250 ppm, i.e. Cout for source 4.
The next step in this case involved the determination of Freg,max for the water
network, such as that performed in Case 1. Since the same case study was used,
Freg,max was fixed at 620.27 ton/h. Note also that in Case 1, this flowrate was also
determined to be the optimal regeneration flowrate (Freg,optimum ) when Cout was fixed
at Cout,min . Hence Freg in this case was fixed at 620.27 ton/h, while Cout for the unit
was varied.
Next, WCA technique (Manan et al., 2004a) was used to locate the various
FFW,min target for the grassroots water network when a regeneration unit with fixed
Freg,max and various Cout is added. Figure 5.32 shows two different kinds of FFW,min
versus Cout plot, i.e. a curve path such as with conventional heat and mass integration
profile (Figure 5.32a) or a straight retrofit path (Figure 5.32b). These plots reveal
that for an optimum grassroots design, a regeneration unit with lower Cout would
consume less fresh water as a result of cleaner regenerated water that could be
reused/recycled in the water network.
148
Targeted
retrofit
design with
∆a = 1
Existing
design
Targeted retrofit
design with
constant a
Targeted retrofit
design with
constant a
FFW
Existing
design
Targeted retrofit
design with ∆a = 1
FFW
Optimum
grassroots
design
Optimum
grassroots
design
Cout
Cout
(a)
(b)
Figure 5.32: Two kinds of retrofit profiles (case 2) (a) curve paths (b) straight paths
One would expect a similar trend for an existing water network. However,
during retrofit, some capital investment would be needed for newly installed
regeneration unit(s) as well as for existing network modifications. We hence define
another retrofit parameter i.e. “fresh water efficiency”, aC as follows:
 FFW, target 

αC = 
F

FW,
existing

 Co u t
(5.18)
where FFW,target and FFW,existing are respectively the fresh water target for grassroots
design and the existing network, for a given Cout value for a regeneration unit. The α
c
value provides a comparison of the existing fresh water consumption to the
minimum fresh water targets in a grassroots design. As in the case of equation 5.15,
an α c?of unity means that the existing water network has achieved the grassroots
utility targets. Again, this is impractical for most retrofit cases.
Alternatively, an “incremental value of fresh water efficiency”, ∆α c may also
be defined as the ratio between a decrease of fresh water target in grassroots design,
∆FFW,target to that of the decrease in fresh water consumption in an existing network,
∆FFW,existing, for a given decrease of outlet concentration of regeneration unit, ∆Cout ,
as shown in equation 5.19:
149
 ∆FFW,target 

∆α C 
 ∆F

 FW, existing ∆C o u t
(5.19)
Figure 5.33 represents the two retrofit profiles for Case 2, which was plotted
using the fresh water efficiency values calculated using equation 5.18 and 5.19. This
corresponds to the values of α c = 0.4264 and ∆α c = 1.0000. Note from Figure 5.33
that these retrofit profiles originated from the utility target of the existing network at
the upper right portion of the FFW,min versus Cout plot. Fresh water usage was reduced
with a decrease in outlet concentration of the regeneration unit. A feasible retrofit
path is the one leading to the optimum grassroots design, i.e. towards the lower left
portion of the graph. As shown in Figure 5.33, a retrofit path with a constant α value
would be a better choice. This resembles the situation in Case 1. Figure 5.33 also
shows that this retrofit diagram can be partitioned into three main regions, based on
the retrofit profile of α C = 0.4264, i.e. an infeasible design region, an economical
design region and an uneconomic design region.
Targeted retrofit design
with α = 0.4264
Retrofit profile
with
∆α = 1.0000
2000
Doubtful Economic
Existing
design
1500
F FW (ton/h)
Doubtful Economic
Optimum
grassroots
design
1000
Economic Project
500
Infeasible
Cout,min
0
0
50
100
150
200
C out (ppm)
Figure 5.33: FFW versus Cout plot with constant a (Case 2)
250
300
150
Next, the savings achieved and the capital investment (due to new
regeneration unit placement) at each point in Figure 5.33 was calculated based on the
economics data in Table 5.14. The savings versus investment plot for DAF tank and
SDF are shown in Figure 5.34 and Figure 5.35 respectively. As shown in Figure
5.34, an investment of $3.91M was needed for the installation of three new DAF
tanks to achieve an annual saving of $ 2.30M. This corresponds to the payback
period of 1.7 years. On the other hand, capital investment of $4.83M is needed for
the installation of 18 new SDF units. Annual saving achieved in this alternative was
targeted at $2.52M. This leads to a payback period of 1.92 years. Note also that due
to the fixed value of Freg in this case, both savings versus investment diagrams in
Figure 5.34 and Figure 5.35 appear to be a vertically straight line with a fixed capital
cost investment. Finally, Cout,optimum corresponds to both regeneration units is
determined at 30 ppm.
2,500,000
1.7 years
2 years
2,000,000
Savings ($/yr)
Payback lines
1,500,000
1,000,000
500,000
0
0
1,000,000
2,000,000
3,000,000
Investment ($)
Figure 5.34: Savings versus investment plot for DAF (Case 2)
4,000,000
151
3,000,000
1.92 years
2,500,000
2 years
Payback lines
Savings ($/yr)
2,000,000
1,500,000
1,000,000
500,000
0
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
Investment ($)
Figure 5.35: Savings versus investment plot for SDF (Case 2)
5.4.3.3 Case 3: Vary Cout and Freg
For Case 3, both Freg and Cout were varied. The objective of this case was to
target both the optimum operating parameters of the newly added regeneration
unit(s), i.e. Freg,optimum and Cout,optimum . The first step in this case was to identify the
boundary for Cout,optimum of this case. Since the same case study and regeneration
units were used, Cout,min of 30 ppm and Cout,max of 250 ppm were chosen. With the
identification of the boundary of Cout,optimum , the value of Freg,max can be determined
for various Cout as done in Case 1.
Figure 5.36 shows the FFW versus Cout plot for a grassroots network,
calculated using the WCA technique (Manan et al., 2004a). As shown, fresh water
consumption remains unchanged at 308.76 ton/h when the regeneration unit produces
regenerated water lower than the Cout of 95 ppm. However, beyond the Cout of 95
ppm, a larger amount of fresh water was required in the network, due to the lower
quality of regenerated water. Note that the increase of fresh water also means the
increase of wastewater generation from the network.
152
1000
900
800
F FW, min (ton/h)
700
600
500
400
300
200
95 ppm
100
0
0
50
100
150
200
250
300
C out (ppm)
Figure 3.36: FFW, min versus Cout (Case 3)
Our objective was to target the utility reduction when regeneration unit(s)
was installed into an existing water network. It is therefore necessary to focus on the
constant fresh water region of the grassroots FFW versus Cout plot (i.e. Cout = 95 ppm)
where the minimum utility targets were achieved. As a result, the boundary of
Cout,max was shifted from 250 ppm to 95 ppm.
Figure 5.37 focuses the FFW versus Cout plot with the newly defined Cout
boundary at 95 ppm (from Figure 5.36). This plot consists of the optimum grassroots
design and a newly added retrofit profile for the existing network, with α calculated
at 0.4264 (following equation 6.18). Note also that α was selected instead of ∆α in
this case. This was due to the optimum grassroots plot being a horizontal straight
line, and hence no profile of ∆α can be plotted. Similar to the previous cases,
regions of infeasible design, economical design and an uneconomic design exist in
this case.
153
1200
Targeted retrofit
design with α = 0.4264
1000
Doubtful Economic
F FW (ton/h)
800
600
Economic Project
Optimum
grassroots
design
400
200
Infeasible
Infeasible
0
20
30
40
50
60
70
80
90
100
110
120
C out (ppm)
Figure 5.37: FFW versus Cout plot with new Cout boundary (Case 3)
The savings in the operating cost and capital investment were calculated next
to assess the economics of this case. Figure 5.38 and Figure 5.39 illustrate the
savings versus investment plot for DAF tank and SDF respectively. A targeted
investment limit of 1.7 years (below 2 years with maximum utility savings) was
identified for DAF tank for a capital investment of $3.91M and savings of $2.30M.
Three DAF tank were required to achieve this target. On the other hand, 18 units of
SDF were needed to achieve the established targets. This corresponds to the capital
investment of $4.83M, annual savings of $2.52M and a payback time of 1.92 years.
Cout,optimum and Freg,optimum corresponds to both regeneration units were 30 ppm and
620.27 ton/h.
154
2,350,000
1.7 years
2 years
2,300,000
Savings ($yr)
2,250,000
2,200,000
2,150,000
2,100,000
2,050,000
Payback lines
2,000,000
3,500,000
4,000,000
4,500,000
5,000,000
5,500,000
6,000,000
Investment ($)
Figure 5.38: Savings versus investment plot for DAF (Case 3)
2,550,000
1.92 years
2 years
2,525,000
Savings ($/yr)
2,500,000
2,475,000
2,450,000
2,425,000
Payback lines
2,400,000
4,500,000
5,000,000
5,500,000
6,000,000
6,500,000
7,000,000
Investment ($)
Figure 5.39: Savings versus investment plot for SDF (Case 3)
7,500,000
155
5.4.3.4 Discussions
It has been shown that three cases achieved the same retrofit targets
(Cout,optimum and Freg,optimum ) for this case study. This is mainly due to the selection of
fixed Cout and Freg variables in the first two cases. If a different Cout and/or Freg and
different payback period were specified for Case 1 and/or Case 2, different retrofit
targets would have resulted. Therefore, it can be concluded that retrofit targets
depend on Cout and Freg, as well as on the payback period specifications.
Although Cout,optimum and/or Freg,optimum attained to design both regeneration
units are 30 ppm and 620.27 ton/h respectively, selection of DAF tanks as new
regeneration units will be a better choice to perform the retrofit for all three cases for
this case study. This is due to the more reasonable targeted number of DAF tanks as
compared to SDF. Nevertheless, one can still consider installing SDF in this case
study if the maximum water utility savings constraint is neglected. For instance, by
installing 5 units of SDF with a total Freg of 139 ton/h and Cout of 30 ppm, a payback
time of 1.91 years can be achieved.
All the retrofit targets achieved in this section were merely based on the
problem data for the case study and is independent of any network design. In order
for these targets to be meaningful and effective, a network design technique that can
lead to the retrofit targets is needed. This is described in the next section.
5.4.4
Retrofit Design
In the design of a maximum water recovery network for grassroots design,
the pinch point(s) play an important role to ensure the established utility targets are
realised. Water network is normally divided into regions above and below the pinch
during the design stage. Network design is then carried out independently in these
regions using various network design procedures (e.g. Wang and Smith, 1994; Feng
and Seider, 2001; Prakash & Shenoy, 2004). Similarly, getting the pinch location(s)
for an existing water network is also essential before any retrofit design is carried out.
156
To determine the pinch points, we rely again on the WCA technique
developed by Manan et al., (2004a). Utility targeting was performed for a grassroots
network based on the water demands and sources data (Table 5.6) along with the
Freg,optimum and Cout,optimum of the regeneration unit(s) identified earlier. Table 5.15
shows the resulting WCT for the grassroots network.
The water pinch for this
grassroots network exists at the lowest concentration level (230 ppm) where there is
zero cumulative pure water surplus (Table 5.15).
Water pinch at this location
indicates that all sources and demands appear at the region above the pinch and
hence a zero discharge process (no wastewater generation) can be achieved.
However, in revamping an existing water network, achieving the targets as in the
grassroots design is not always possible.
For retrofitting water network involving non- mass transfer processes, such as
for this case study, many network design techniques may be used. These include the
source sink mapping diagram (El-Halwagi, 1997; Dunn and Wenzel, 2001), sinksource allocation (Prakash and Shenoy, 2004) or concentration block diagram
described in section 5.2.3. In this work, we utilise the concentration block diagram
(CBD) to represent the existing water network. CBD provides a clear representation
of the existing water network in terms of the water flowrate as well as contaminant
concentration.
CBD of case study 4 is presented in Figure 5.40. The vertical dashed lines
represent the concentration- interval boundaries which correspond to a distinct
limiting inlet or outlet concentration. The water using operations are presented by
rectangles correspond to their inlet and outlet concentration. The arrows in the
diagram indicate the water streams of the existing water network with the stream
flowrate in ton/h and contaminant concentration in ppm (figures in parenthesis).
157
C(ppm)
0
20
155.4 (0)
80
Pressing Showers
100
170
200
230
155.4 (100)
250
1264.5 (230) 150.47 (230)
831.12 (0)
Forming Showers
201.84(100)
Others
Users
41.28
(230)
201.84(170)
150.47
(230)
White Water Tank
398.52 (170)
DIP
751.32 (0)
415.8
(250)
54 (250)
14.7 (0)
AF
34.68 (0)
CP
LEGEND
Water-using operation
Water streams
Concentration interval boundaries
number
Stream flowrate (ton/hr)
(number) Stream Concentration (ppm)
Figure 5.40: Existing water network for case study 4 in CBD with identified streams
for regeneration
The next step in the retrofit design stage is to identify the streams for
regeneration. Sources at the highest concentration are always preferred as this
reduces the mass load accumulated in wastewater produced. From Figure 5.40, one
noticed that, at the highest concentration of 250 ppm, 469.8 ton/h of wastewater is
available from the source 4, i.e. the water source of DIP. However, the targeted
Freg,max that was identified in the earlier stage amounts to 620.27 ton/h. This means
that part of the wastewater discharged from Forming Showers (at second highest
concentration) was chosen to satisfy the remaining Freg amount fed to the DAF tanks.
The dotted lines in Figure 5.40 show the streams which are identified for
regeneration. 54 ton/h of water produced from DIP was originally sent to DAF tanks
instead of being reused in AF.
158
The existing water network was next redesigned to meet the established
retrofit targets. This involved sending the identified streams to the regeneration unit
(DAF tanks) and feeding the purified source to the water-using operations to reduce
fresh water intake. The preliminary retrofit design is presented in Figure 5.41. As
shown, 54 ton/h of water source is now sent to AF unit from the DAF tanks instead
of feeding from the DIP unit directly. The remaining regenerated water from the
DAF tanks are also sent to the Pressing Showers, Forming Showers and for Chemical
Preparation.
C(ppm)
0
20
51.8 (0)
30
80
100
170
200
230
250
Pressing Showers
103.6 (30)
155.4 (100)
391.57 (0)
1114.03 (230)
Forming Showers
439.55 (30)
201.84(100)
Others
41.28
(230)
201.84(170)
White Water Tank
398.52 (170)
DIP
751.32 (0)
469.8
(250)
DAF tanks
150.47
(230)
54 (30)
14.7 (0)
AF
11.56 (0)
CP
23.12 (30)
LEGEND
Water-using operation
number
Water streams
Concentration interval boundaries
(number) Stream Concentration (ppm)
Stream flowrate (ton/hr)
Figure 5.41: Preliminary retrofit design for case study 4
The final step of the retrofit design stage involves optimisatio n of the
preliminary retrofitted network for further utility reduction.
To achieve this,
opportunity for further reuse and/or recycle of wastewater sources in the preliminary
159
retrofit design was explored. One option in this effort is to reuse and recycle the
effluent from Forming Showers back to its own water demands as well as to other
water-using processes. The final retrofit design and the conventional flowsheet for
the retrofitted network for case study 4 are shown in Figure 5.42 and Figure 5.43
respectively. It is shown that after retrofit, the fresh water consumption has been
reduced by 80% to 401.82 ton/h while the wastewater generation has been reduced
by 95% to 92.52 ton/h. Utility savings achieved through this methodology are much
higher as compared to utility savings attained by retrofit methodology without
regeneration in section 5.2.3 (55.5% fresh water reduction and 65.7% wastewater
elimination). This has proven that water network retrofit with regeneration strategy
enable larger water savings for existing processes.
C(ppm)
0
20
51.8 (0)
30
80
Pressing
Showers
103.6 (30)
100
170
200
230
250
92.52
(230)
155.4 (100)
177.82 (0)
Forming Showers
439.55 (30)
114.08 (100)
Other
213.75
(230)
87.76
(230)
White Water Tank
398.52
(170)
46.02(0)
41.28
(230)
201.84(170)
705.3
(230)
DIP
469.8
(250)
Regeneration Unit
54 (30)
11.56 (0)
14.7
(230)
AF
150.47
(230)
CP
23.12 (30)
LEGEND
Water-using operation
Water streams
number
Stream flowrate (ton/hr)
(number) Stream Concentration (ppm)
Concentration interval boundaries
Figure 5.42: Final retrofit design for case study 4
160
Fresh water
401.82 ton/h
9
Paper Machine
1 51.8 ton/h
5 155.4 ton/h
Pres
sing
Sect
11.56 ton/h
8 201.84 ton/h
White Water
Tank
100 ppm
6 41.28 ton/h
170 ppm
CP
230 ppm
2 177.82 ton/h
Forming
Section
Wastewater
R3
R4
213.75 ton/h 705.3 ton/h
R1103.6 ton/h
30 ppm
230 ppm
R2439.55 ton/h
230 ppm
R5
R7
R8
87.76 ton/h
150.47 ton/h
23.12 ton/h
230 ppm
230 ppm
30 ppm
R10
14.7 ton/h
230 ppm
30 ppm
7 398.5 ton/h
170 ppm
3 46.02 ton/h
4 114.08 ton/h
DAF Tanks
R6
469.8 ton/h
DIP
250 ppm
Others
Figure 5.43: Conventional flowsheet for the retrofitted network for case study 4
R954 ton/h
30 ppm
AF
92.52 ton/h
Economic calculations show that a total savings of $3.16M has been achieved
with this final network, with the installation of three new DAF tanks. Though the
savings had slightly surpassed the targeted value ($0.86M), however the capital
investment of $3.91M remained within target. The resulting payback period of 1.24
years is slightly better as compared to the targeted value of 1.7 years. Finally, note
that the retrofit design presented above is one of the many possible solutions that can
achieve the retrofit target. Often, different network design configurations can be
achieved with the use of different network design techniques (El-Halwagi, 1997;
Dunn and Wenzel, 2001; Prakash and Shenoy, 2004).
5.4.5
Summary of the Developed Water Network Retrofit with The Additional
of New Regeneration Units
Optimisation of existing regeneration unit(s) provide opportunity to further
reduce utility saving before the installation of new regeneration unit. Hence, this
retrofit option possesses the advantage of low capital investment and minor process
changes over other retrofit approaches. A new two-stage approach based on pinch
analysis for retrofit of water network with the integration of existing regeneration
unit(s) optimisation has been presented. In the first stage, retrofit targets (utility
savings and capital investment) were determined for a range of total flowrate and/or
outlet concentration of the regeneration unit. Given a fixed payback period or capital
expenditure, the retrofit targets were determined from the saving versus investment
diagram. Once the retrofit targets were identified, the existing water network was redesigned to meet the established targets.
This methodology has successfully
achieved the retrofit targets prior to design and further minimise fresh water
consumption and wastewater generation in an existing water network.
CHAPTER 6
CONCLUSIONS AND FUTURE WORKS
6.1
Summary and significance
The work in this thesis offers some major contributions in the area of retrofit
synthesis of water network. Four new techniques on water network retrofit have been
developed.
These include retrofit of water network with mass transfer-based
operations, retrofit of water network with non- mass transfer-based operations, retrofit
of water network with regeneration unit optimisation, and retrofit of water network
with the addition of new regeneration unit. The retrofit synthesis task was based on
the pinch analysis concept.
Retrofit technique for water network with mass transfer-based operations
involving two stages namely utility (water) targeting and network design has been
established. During the targeting stage, fresh water and wastewater savings, and
capital investment target were determined for a particular capital expenditure. Lastly
the existing network was retrofitted to meet the targets.
For water network with non- mass transfer-based operations, a retrofit design
methodology has been established. To diagnose, retrofit and evolve the existing
water network, a new graphical tool called concentration block diagram (CBD) has
been introduced.
163
A new systematic technique for retrofit of water network with integration of
existing regeneration unit(s) optimisation has been developed. In the targeting stage,
retrofit targets, where utility savings and capital investment were determined for a
range of process parameters (flowrate increment or outlet concentration reduction of
the existing regeneration unit). Next, the existing water network was re-designed to
meet the chosen targets.
To incorporate new regeneration unit(s) into water network, a new systematic
retrofit methodology has been presented. The first stage of the retrofit task was to
identify various retrofit targets (utility savings and capital investment) for a range of
process parameters (total flowrate and/or outlet concentration of the regeneration unit)
to obtain a savings versus investment curve.
Lastly the existing network was
retrofitted to achieve the targets.
6.2
Future works
Since the work on water network retrofit is relative new, three main areas for
future development can be identified:
i. Batch process system
In various industrial sectors, such as food, pharmaceutical, biochemical
manufacturing, processes are commonly operated in batch mode. To date,
there has been very few work done on water network retrofit for batch
process systems.
The development of retrofit synthesis for batch water
networks which are industrially very common as well as important is
therefore required.
ii. Multiple contaminants problem
So far, we have only dealt with water network retrofit problem involving
single contaminant. However, it is often necessary to consider cases with
164
multiple contaminants since they are the more common in industry. Wang
and Smith (1994) presented their grassroots approach in handling the multiple
contaminants problem by shifting the inlet and outlet concentration of the
processes. It is assumed that the transfer of contaminants in the processes
happens simultaneously.
iii. Simultaneous analysis of batch, semibatch and continuous processes
Kemp and Deakin (1989a) pointed out that continuous process is in fact, a
special case of batch process with only one time interval. Their work also
attempts to show that it is possible to simultaneously integrate batch,
semibatch and continuous processes together.
The strategy is to carefully
arrange these processes so that they could be efficiently integrated.
A
continuous process will take place during all time intervals, whereas batch
and semibatch processes will appear in some of the time intervals. Hence,
targeting and design of water network retrofit should be carried out
independently for each time interval.
REFERENCES
Ahmad, S. and Polley, G. T. (1990). Debottlenecking of Heat Exchange Networks.
Heat Recovery Systems & CHP. 10 (4): 369-385.
Alfadala, H. E., Sunol, A. K. and El-Halwagi, M.M. (2001). An Integrated Approach
to the Retrofitting of Mass Exchange Networks. Clean Product and Process 2.
236-247.
Alva-Argáez, A., Kokossis, A. C. and Smith, R. (1998). Wastewater Minimisation of
Industrial Systems Using An Integrated Approach. Computers and Chemical
Engineering, 22: S741-744.
Arundel, J. (2000) Sewage and Industrial Effluent Treatment. 2nd ed. Great Britain:
Blackwell Science.
Asante, N. D. K. and Zhu X. X. (1997). An Automated and Interactive Approach for
Heat Exchanger Network Retrofit. TransIChemE. 75(A): 349-359.
Bagajewicz, M. and Savelski, M. (2001). On the Use of Linear Models for the
Design of Water Utilization Systems in Process Plants with A Single
Contaminant. Transactions of the Institute of Chemical Engineers, Part A, 79:
600-610.
Benko, N., Rév, E., and Fonyó Z. (2000). The Use of Nonlinear Programming to
Optimal Water Allocation. Chemical Engineering Communication, 178: 67101.
Briones, V. and Kokossis, A. (1996). A New Approach for the Optimal Retrofit of
Heat Exchanger Networks. Com. Chem. Eng. 20: S43-S48.
Briones, V. and Kokossis, A. (1999). Hypertargets: A Conceptual Programming
Approach for the Optimisation of Industrial Heat Exchanger Networks – II.
Retrofit Design. Chemical Engineering Science. 54: 541-561.
166
Byers, W., W. Doerr, R. Krishnan, and D. Peters (1995) How to Implement
Industrial Water Reuse: A Systematic Approach, Center for Waste Reduction
Technologies (CWRT), American Institute of Chemical Engineers (AIChE),
New York.
Carlsson, A., Franck, P. A. and Berntsson, T. (1993). Design Better Heat Exchanger
Network Retrofit. Chem. Eng. Sci. 54: 541-561.
Castro, P., Matos, H., Fernandes, M. C., Nunes, C. P. (1999). Improvements for
Mass-exchange Networks Design. Chemical Engineering Science, 54: 16491665.
Ciric, A. R. and Floudas, C. A. (1989). A Retrofit Approach for Heat Exchanger
Networks. Com. Chem. Eng. 13 (6): 703-715.
Coulson, J. M., Richardson, J. F. and Sinnott, R. K. (1993). Chemical Engineering.
Vol. 6, 2nd ed. U. K.: Pergamon Press.
Dhole, V. R., Ramachandi, N., Tainsh, R. A. and Wasileski, M. (1996). Make Your
Process Water Pay for Itself. Chem. Eng. 103 (1): 100-103.
Dunn, R. F., and Wenzel, H. (2001). Process Integration Design Methods for Water
Conservation and Wastewater Reduction in Industry.
Part 1: Design for
Single Contaminant. Cleaner Production Processes, 3: 307-318.
Eckenfelder, W. W. (2000). Industrial Water and Pollution Control. 3rd ed. New
York: McGraw Hill.
El-Halwagi, M. M. (1997). Pollution Prevention through Process Integration:
Systematic Design Tools. San Diego: Academic Press.
El-Halwagi, M. M. and Manousiouthakis, V. (1989). Synthesis of Mass Exchange
Networks. AIChE Journal. 35 (8). 1233-1244.
El-Halwagi, M. M. and Manousiouthakis, V. (1990a). Automatic Synthesis of Mass
Excha nge Networks with Single Component Targets. Chemical Engineering
Science. 45 (9). 2813-2831.
El-Halwagi, M. M. and Manousiouthakis, V. (1990b). Simultaneous Synthesis of
Mass Exchange and Regeneration Networks. AIChE Journal. 36 (8). 12091219.
El-Halwagi, M. M. and Srinivas, B. K. (1992). Synthesis of Reactive Mass Exchange
Networks. Chemical Engineering Science. 47 (8): 2113-2119.
167
Feng, X. and Chu, K. H. (2004). Cost Optimisation of Industrial Wastewater Reuse
Systems. Trans IChemE, Part B, Process Safety and Environment Protection.
82 (B3): 249-255.
Feng, X. and Seider, W. D. (2001). New Structure and Design Method for Water
Networks. Industrial and Engineering Chemistry Research. 40: 6140-6146.
Fraser, D. N. and Halale, N. (2000). Retrofit of Mass Exc hange Network Using
Pinch Technology. AIChE Journal. 46 (10): 2112-2117.
Hallale, N. (1998). Capital Cost Targets for the Optimum Synthesis of Mass
Exchange Networks. Department of Chemical Engineering, University of
Cape Town, South Africa: PhD Thesis.
Hallale, N. (2002). A New Graphical Targeting Method for Water Minimisation.
Advances in Environmental Research. 6 (3): 377-390.
Hallale, N. and Fraser, D.M. (1998). Capital Cost Targets for Mass Exchange
Networks A Special Case: Water Minimisation. Chemical Engineering
Science. 53 (2): 293-313.
Huang, C.-H., Chang, C.-T., Ling, H.-C., and Chang, C.-C. (1999). A Mathematical
Programming Model for Water Usage and Treatment Network Design.
Industrial and Engineering Chemistry Research, 38: 2666-2679.
Intelligen, Inc. (1991). SuperPro Designer® (Academic Site Ed.). Version 5, Build 9.
Jacob, J., Kaipe, H. Couderc, F., and Paris, J. (2002). Water Network Analysis in
Pulp and Paper Processes by Pinch and Linear Programming Techniques.
Chemical Engineering Communication. 189(2): 184-206.
Jödicke, G., Fisher, U., and Hungerbühler, K. (2001). Wastewater Reuse: A New
Approach to Screen for Designs with Minimal Total Costs. Computer and
Chemical Engineering. 25: 203-215.
Koppol, A.P.R., Bagajewicz, M.J., Dericks, B.J., and Savelski, M.J. (2003). On Zero
Water Discharge Solutions in the Process Industry. Advances in
Environmental Research. 8: 151-171.
Kuo, W. C. J., Smith, R. (1998). Design of Water-using Systems Involving
Regeneration. Transactions of the Institute of Chemical Engineers. Part B, 76:
94-114.
168
Linnhoff, B., Townsend, D. W., Boland, D., Hewitt, G. F., Thomas, B. E. A., Guy, A.
R. and Marshall, R. H. (1982). A User Guide on Process Integration for the
Efficient Use of Energy. Rugby: IChemE.
Manan, Z. A., Foo, D. C. Y., Tan, Y. L. (2004a). Targeting the Minimum Water
Flowrate Using Water Cascade Analysis Technique, AIChE Journal. 50(12):
3169-3183.
Manan, Z. A., Ooi, B. L., Lin, F. Y. and Foo, C. Y. (2003). Heat-MATRIX – A
Computer Software For The Reduction Of Energy And Water In Process
Plants. 31st Int. Exhibition of Invention, New Techniques and Products of
Geneva, Switzerland.
Manan, Z. A., Wan Alwi, S. R. and Ujang, Z. (2004b). Water Pinch Analysis for
Urban Systems: A Paradigm for Water Minimisation. Water 21 (to be
published in February 2005).
Mann, J. G. and Liu, Y. A. (1999). Industrial Water Reuse and Wastewater
Minimisation. New York: McGraw-Hill.
Marcone, L. S. and Roger, J. Z. (2000). Retrofit of Pressure Drop Constrained Heat
Exchanger Networks. Applied Thermal Engineering. 20: 1469-1480.
Nie X.R. and Zhu, X. X. (1999). Heat Exchanger Network Retrofit Considering
Pressure Drop and Heat- Transfer Enhancement. AIChE Journal. 45 (6):
1239-1254.
Olesen, S. G., Polley, G. T. (1997). A Simple Methodology for the Design of Water
Networks Handling Single Contaminants. Transactions of the Institute of
Chemical Engineers. Part A, 75: 420-426.
Papalexandri, K. P., Pistikopoulos, E. N. and Floudas, A. (1994). Mass Exchange
Networks for Waste Minimisation: A Simultaneous Approach. TransIChemE.
72: 279-294.
Parthasarathy, G., and Krishnagopalan, G. (2001). Systematic Reallocation of
Aqueous Resources Using Mass Integration in A Typical Pulp Mill.
Advances in Environmental Research. 5: 61-79.
Perry, R. H., and Green, D.W. (1997) Perry’s Chemical Engineer’s Handbook. 7th ed.
New York: McGraw Hill.
Peter, M. S., and Timmerhaus, K.D. (1980). Plant Design and Economics for
Chemical Engineers. 3rd. ed. New York: McGraw Hill.
169
Polley, G. T. and Heggs, P. J. (1999). Don’t Let the Pinch Pinch You. Chem. Eng.
Prog. December. 35-45.
Polley, G. T. and Majid A. (2000). Don’t Let the Retrofit Pinch You. Chem. Eng.
Prog. November. 43-48.
Polley, G. T. and Panjeh S. (1990). Process Integration Retrofit subject to Pressure
Drop Constraint. Bussemaker, H. Th. and Iedema, P. D. ed. Computer
Application in Chemical Engineering. May 7-9. The Hague, The Netherlands:
Elsevier, 31-36.
Polley, G. T. Polley, H.L. (2000). Design Better Water Networks. Chemical
Engineering Progress. 96: 47-52.
Polley, G. T., Van Reisen, J. L. B. and Verjeijen, P. J. T. (1998). Structural Trageting
for Heat Integration Retrofit. Applied Thermal Engineering. 18 (5): 283-294.
Prakash, R. and Shenoy, U. V. (2005). Targeting and Design of Water Network for
Fixed Flowrate and Fixed Contaminant Load Operations. Chemical
Engineering Science. 60(1): 255-268.
Ramachandran, L. and Rene B. (1996). A Novel Visualization Tool for Heat
Exchanger Network Retrofit. Ind. Eng. Chem. Res. 35: 4507-4522.
Reisen, V. J., Grievink, P. J. T. and Polley, G. T. (1995). The Placement of Twostream and Multi-stream Heat Exchangers in an Existing Network through
Path Analysis. Com. Chem. Eng. 19: S143-S148.
Rousseau, R. W. ed (1987). Handbook of Separation Process Technology. New York:
John Wiley and Sons.
Saboo, A. K, Morari, M. And Colberg R. D. (1986). RESHEX: An Interactive
Software Package for the Synthesis and Analysis of Resilient Heat Exchange
Networks – I Programme Description and Application. Computer and
Chemical Engineering. 10: 577-589.
Savelski, M., and Bagajewicz, M. (2001). Algorithmic Procedure to Design Water
Utilization Systems Featuring A Single Contaminant in Process Plants.
Chemical Engineering Science. 56: 1897-1911.
Shenoy, U. V. (1995) Heat Exchanger Network Synthesis: Process Optimization by
Energy and Resource Analysis. Houston Gulf: Publishing Co.
170
Shiklomanov, I. A. (1999) State Hydrological Institute (SHI, St Peterburg) and
United Nations Educational, Scientific and Cultural Organisation (UNESCO,
Paris).
Silangwa, M. (1986) Evaluation of Various Surface Area Efficiency Criteria in Heat
Exchanger Network Retrofits. University of Manchester Institute of Science
and Technology: M.Sc. Dissertation.
Smith, R. (1995). Chemical Process Design. New York: McGraw Hill.
Sorin, M. and Bedard, S. (1999). The Global Pinch Point in Water Reuse Network.
TransIChemE. 77 (B): 305-308.
Takama, N., Kuriyama, T., Shiroko, K., Umeda, T. (1980). Optimal Water
Allocation in A Petroleum Refinery. Computers and Chemical Engineering.
4: 251-258.
Tan, Y. L., and Manan, Z. A. (2003). Retrofit of Water Network in a Paper Making
Process based on Pinch Analysis Technique. 17th Symposium of Malaysia
Chemical Engineers (SOMChE 2003). December 29-30. Penang: Universiti
Sains Malays ia, 61-66.
Tan, Y.L. and Manan, Z.A. (2004). Retrofit of Water Network for Mass Transfer
based Processes, 18th Symposium of Malaysia Chemical Engineers (SOMChE
2004). December 13-14. Perak: Universiti Teknologi PETRONAS, 6:33-39.
Tan, Y. L., Manan Z. A., Foo, C. Y. (2002), Water Minimisation by Pinch
Technology – Water Cascade Table for Minimum Water and Wastewater
Targeting. Proceeding of the 9th Asia Pacific Confederation of Chemical
Engineering Congress (APCChE), New Zealand.
Tchobanoglaus, G., and Burton, F. L. (1991) Wastewater Engineering: Treatment,
Disposal and Reuse. 3rd ed. New York: McGraw Hill.
Thevendiraraj, S., Klemes, J., Paz, D., Aso, G., Cardenas, G. J. (2003). Water and
Wastewater Minimisation Study of A Citrus Plant. Resources, Conservation
and Recycling. 37: 227-250.
Tjoe, T. N. and Linnhoff, B. (1986). Using Pinch Technology for Process Retrofit.
Chemical Engineering April 28: 47-60.
Tripati, R. (1996). Pinch Technology Reduced Wastewater. Chemical Engineering.
November, 87-90.
171
Ulrich, G. D. (1984). A Guide to Chemical Engineering Process Design and
Economics. New York, U.S.A.: Wiley.
Wang, Y. P. and Smith, R. (1994). Wastewater Minimisation. Chemical Engineering
Science. 49 (7): 981-1006.
Westerberg, A. W. (1987). Process Synthesis: A Morphological View. in Liu, Y. A.,
McGee, H. A. and Epperly, W. R. Recent Developments in Chemical Process
and Plant Design. New York: John Wiley & Sons.
Wiseman, N., and Ogden, G. (1996). Zero Liquid Effluent Technologies for The
Paper Industry. Paper Technology. 37: 31-38.
Xu, D., Hu, Y., Hua, B., Wang, X. (2003). Manimization of the Flowrate of Fresh
Water and Corresponding Regenerated Water in Water-using System with
Rregeneration Reuse. Chinese Journal of Chemical Engineering. 11(3): 257263.
Yee, T. F. and Grossmann, I. E. (1991). A Screening and Optimisation Approach for
the Retrofit of the Heat Exchanger Networks. Ind. Eng. Chem. Res. 30: 146162.
Zhu, X. X. (1997). Automated Design Method for Heat Exchanger Network Using
Block Decomposition and Heuristic Rules. Com. Chem. Eng. 21 (10): 10951104.
Zhu, X. X. and Asante, D. K. (1999). Diagnosis and Optimisation Approach for Heat
Exchanger Network Retrofit. AIChE Journal. 45 (7): 1488-1503.