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Multi-period synthesis of optimally-integrated biomass and bioenergy supply
networks
Lidija Čučeka,*, Mariano Martínb, Ignacio E. Grossmannc, Zdravko Kravanjaa
a
Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia
b
Department of Chemical Engineering, University of Salamanca, 37008 Salamanca, Spain
c
Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
[email protected]
Published in Computers & Chemical Engineering (2014), Volume 66, Pages 57-70
Summary
This contribution addresses the multi-period synthesis of an optimally integrated regional biomass and
bioenergy supply network through a mixed-integer linear programing (MILP) approach. The
production processes from different sources of biomass include first, second, and third generations of
biofuels like bioethanol, biodiesel, hydrogen, Fischer Tropsch diesel, and green gasoline. The aim is to
maximize the sustainable utilization of resources by accounting for the competition between fuels and
food production. An MILP model for efficient bioenergy network optimization based on four layers is
extended to include several features, such as seasonality and availability of resources, enabling
recycles of products and total site heat integration in order to address real-world applications with a
systematic decision-making approach. The multi-period optimization of a heat-integrated biorefinery's
supply network is performed through maximization of the economic performance. Economically
efficient solutions are obtained with optimal selection of raw materials, technologies, intermediate and
final product flows, and reduced greenhouse-gas (GHG) emissions.
Background
Nowadays shortages of energy and water resources, and increasing fuel prices, demand and
dependency on fossil resources, as well as growing environmental consciousness, have become major
priorities for our society. These concerns have led to the considerations of alternative and renewable
energy sources, and policies in European Union and several other countries (European Commission,
2009). Biomass is one of the promising renewable energy sources. However, the competiveness of
biomass as an energy source faces some challenges and strongly depends on the biomass supply chain
(Yu et al., 2009). Biomass is usually seasonally and locally available, has low energy density, high
moisture content, and degrades during storage. It requires extensive infrastructure for harvesting,
transportation, storage, and processing, and significant land area.
There has been large amount of work on modeling and optimization of the supply chains in several
kinds of the process industries (Laínez & Puigjaner, 2012). However, to the best of our knowledge,
there have been no studies in the literature to date which have addressed the generic (independent from
Suggested citation: Čuček, L., Martín, M., Grossmann, I. E., & Kravanja, Z. (2014). Multi-period
Synthesis of Optimally-Integrated Biomass and Bioenergy Supply Network. Computers and Chemical
Engineering, 66, 57-70.
data) multi-period synthesis of regional biomass and waste supply chain networks which include
combined first, second, and third generations of biofuels, enabling the recycling of products, and
Total-Site (TS) heat-integration within the network, amongst other features.
Aims
The goal of this study is to perform comprehensive multi-period synthesis of the regional biorefinery's
supply networks including first, second, and third generations of biofuels (Martín & Grossmann,
2013), and different sources of biomass and waste. An integrated mixed-integer linear programming
(MILP) model is developed for efficient bioenergy network optimization on a regional scale (see
Appendix B in Supplementary material by Čuček et al., 2014). The proposed model employs a four
layer structure: agricultural (layer 1 – L1), collection, preprocessing and intermediate storage (layer 2
– L2), core processing and storage of products (layer 3 – L3), and usage (layer 4 – L4) layers,
including connection links between these layers and recycling within the layers.
This model is capable of accounting for different biomass types, optimizing the locations, types and
capacities of the processing plants and the connecting logistics network. It accounts for seasonality
and the availability of resources, optimal time periods and capacities when the facilities are operating,
harvesting loss, biomass and bioproducts’ degradation, and loss with time related to storage,
distribution and usage, optimal selection of areas during the year for each year-round biomass
resource, and optimal harvesting period(s) for seasonal biomass resources. It includes the possibility of
purchasing additionally required resources, not produced within the region at L2 and L3. It enables
some of the produced products to be recycled and used within other technologies at L2 and L3 that use
them as raw materials. Furthermore, the energy is considered as another raw material or by-product
that can be reused within the network at L2 and L3 (heat and TS integration). Figure 1 shows an
example of several options related to handling the produced products and utilities. They could be
either sent to users (sold), or used within another plant at the same of different location.
qnm,t,T,L3,L4
, j , pp ,tp
Plant n,
technology t
Plant nn,
technology tt
,net,T,L3,L3
qnm, nn
, t , tt , poutpin, tp
,T,L3,L3
qnm, nn
, t , tt , poutpin, tp
Plant m,
technology t
qnm,m,net,T,L3,L2
,tt ,t , poutpin ,tp
qnm,m,T,L3,L2
,tt ,t , poutpin ,tp
Figure 1: Sale and/or reuse of products and energy
This model also considers the competition between fuels and food production. Multi-period synthesis
of a heat-integrated biorefinery's supply network is performed, with maximization of the economic
performance. Finally, the GHG emissions are evaluated for the optimal design.
Suggested citation: Čuček, L., Martín, M., Grossmann, I. E., & Kravanja, Z. (2014). Multi-period
Synthesis of Optimally-Integrated Biomass and Bioenergy Supply Network. Computers and Chemical
Engineering, 66, 57-70.
Methods
First an initial pre-screening of technologies is performed, and after the detailed synthesis of
individual technology’s flowsheet (see contribution by Martín & Grossmann, 2013). The surrogate
models are developed based on conversion factors and cost correlations. The data from the detailed
models are inserted as black-boxes into the renewable supply and demand network (developed multiperiod MILP mathematical model of a heat-integrated biorefinery’s supply network). The model
includes mass and energy balances, production and conversion constraints, transportation, operating,
storage and investment cost constraints, and the main objective of maximizing the profit.
A set of biomass feedstock that can be converted to bioethanol, biodiesel, FT-diesel, hydrogen, or
green gasoline is defined. It includes food-based crops, agricultural residues, energy crops, wood
residues, waste cooking oil and algae. A number of transformation technologies are considered
(Martín & Grossmann, 2013), biochemical, thermo-biochemical and thermochemical. For short
description of process technologies see Appendix C in Supplementary material by Čuček et al., 2014.
The advantages of this work are in the systematic analysis of using optimal raw material(s), production
process(es); and product(s), and in developing a generic mathematical model that can be used for
decision-making, and for real-world applications within any supply chain network within any region,
state or country. The multi-period synthesis model is especially powerful when designing new plants
and Total Sites.
Results
Smaller (16 zones, total area of 32,000 km2) and medium-sized illustrative supply chain networks (36
zones, total area of 72,000 km2) are evaluated. The results show that the model provides a powerful
tool for synthesizing any biomass energy supply chain network in any region, and is useful for realworld applications through systematic decision-making. From the testing examples several
conclusions could be drawn, such as i) biomass and waste, especially switchgrass and algae are
promising raw materials for producing biofuels; ii) it is feasible to economically produce second and
third generation biofuels with significant profit; iii) even higher profits can be obtained by variable
usage of agricultural areas (for around 40 %) because of lower storage costs and avoiding the
limitations of capacities within the various technologies; iv) biorefinery's supply networks could
exhibit significant unburdening capabilities in terms of GHG emissions; v) producing biofuels is
economically sounder that producing food; vi) using 20 % of the land area enables the satisfying of
demand for food and transportation fuels, except for the US average demand for transportation fuels
where the area required to satisfy the demand for food and transportation fuels increases to 60 %.
Acknowledgments
The authors acknowledge the financial support from the Slovenia-US bilateral project (BI-US/09-12027). Prof Kravanja and Dr Čuček are grateful to the Slovenian Research Agency for its financial
support (Program P2-0032 and PhD research fellowship Contract No. 1000-08-310074) and EC FP7
project ENER/FP7/296003 ‘Efficient Energy Integrated Solutions for Manufacturing Industries –
EFENIS’. Prof Grossmann and Prof Martín acknowledge NSF Grant CBET0966524, and the Center
for Advanced Process Decision-making.
Suggested citation: Čuček, L., Martín, M., Grossmann, I. E., & Kravanja, Z. (2014). Multi-period
Synthesis of Optimally-Integrated Biomass and Bioenergy Supply Network. Computers and Chemical
Engineering, 66, 57-70.
References
Čuček, L., Martín, M., Grossmann, I. E., & Kravanja, Z. (2014). Multi-period Synthesis of OptimallyIntegrated Biomass and Bioenergy Supply Network. Computers and Chemical Engineering, 66,
57-70.
European Commission, 2009. Directive 2009/28/EC of the European Parliament and of the Council of
23 April 2009 on the promotion of the use of energy from renewable sources and amending and
subsequently repealing Directives 2001/77/EC and 2003/30/EC. Official Journal of the European
Union, Brussels, Belgium.
Laínez, J. M., & Puigjaner, L. (2012). Prospective and perspective review in integrated supply chain
modelling for the chemical process industry. Current Opinion in Chemical Engineering, 1, 430445.
Martín, M., & Grossmann, I. E. (2013). On the systematic synthesis of sustainable biorefineries.
Industrial & Engineering Chemistry Research, 52, 3044-3064.
Yu, Y., Bartle, J., Li, C.-Z., & Wu, H. (2009). Mallee Biomass as a Key Bioenergy Source in Western
Australia: Importance of Biomass Supply Chain. Energy & Fuels, 23, 3290-3299.
Suggested citation: Čuček, L., Martín, M., Grossmann, I. E., & Kravanja, Z. (2014). Multi-period
Synthesis of Optimally-Integrated Biomass and Bioenergy Supply Network. Computers and Chemical
Engineering, 66, 57-70.