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Application of Data Mining in Energy Industry: A Case Study of NOx Prediction
Jongsawas Chongwatpol†
NIDA Business School, National Institute of Development Administration
118 Seri Thai Road, Bangkapi, Bangkok, 10240 Thailand,
Email: [email protected], Phone: +6686-776-9686
Abstract
The incorporation of environmental control systems in coal-fired power plants has become increasingly
standard. However, power producers are still looking for ways to proactively monitor plant operation so
that the level of toxic substances emissions of NOx is complied with the environmental regulations.
Particularly under alarm conditions, data mining techniques can be applied to provide plant-wide signals
of any unusual operational and coal-quality factors that impact the level of NOx, which is deviated from its
traditional standard. This study demonstrates a step-by-step guidance on how to conduct data mining
project to explain and predict the leading causes of variation of emission of NOx and in the combustion
process. Corrective action and preventive maintenance on those unusual factors are regularly evaluated
and monitored.
Introduction
The use of electricity has been an essential part of economy. Coal power, an established electricity source
that provides a vast quantity of inexpensive and reliable power, has become more important as supplies of
oil and natural gas. Coal-fired power plants currently fuel 41% of global electricity. In fact, a higher
percentage of electricity produced by coal-fired power plants can be expected in some countries. Although
the incorporation of environmental control systems in coal-fired power plants has become increasingly
standard, power producers are still looking for ways to proactively monitor plant operation so that the
level of toxic substances emissions of NOx is complied with the environmental regulations. Particularly
under alarm conditions, data mining techniques can be applied to provide plant-wide signals of any
unusual operational and coal-quality factors that impact the level of NOx, which is deviated from its
traditional standard. This study demonstrates a step-by-step guidance on how to conduct data mining
project to explain and predict the leading causes of variation of emission of NO x in the combustion
process.
Coal-Fired Power Plants
A case study of a coal-fired power plant in Thailand has been conducted to explore the application of data
mining techniques in the energy industry. The flow of the electricity generation process in a coal-fired
power plant starts when coal is crushed into a fine power in the coal bunker to increase the surface area
for the burning process. The powdered coal is then burned at a high temperature in the combustion
chamber of a boiler. The hot gases and heat energy produced convert water in tubes lining the boiler into
steam, which is used to spin turbines to generate electricity. The byproduct of this combustion process is
the flue gas, which is discharged into the air. This flue gas contains water vapor, fly ash, and many toxic
substances such as carbon dioxide, especially NOx, and SOx. To comply with the environmental
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regulations, this flue gas must be treated appropriately by the installed protected system equipment.
Figure 1 presents the scatter plots of NOx and SOx emissions from July 2009 to June 2014. For SOx, any
emission level that is greater than 262 ppm requires immediate attention. Similarly, corrective action is
needed for NOx Emission greater than 241 ppm. Additionally, any emission level that is greatly deviated
from the mean is worth mentioning. Promoting preventive maintenance on the factors that affect the
stack emission of NOx and SOx helps improve the performance of the plant accordingly.
Average of
150 ppm
Average of
152 ppm
Figure 1: NOx and SOx Emission from July 2009 to June 2014
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Currently, the plant employs traditional excel-based regression analysis to monitor the power plant
performance. Figure 2 presents an example of the overall reaction equations, currently employed at the
plants:
----------------------------------------------------------------------------------------------------------------------------- ------CaHbOcNdSe + wH2O + g(O2+3.76N2)  x1CO + x2CO2 + x3H2O + x4NO + x5NO2 + x6SO2 + x7SO3 +
x8O2 + x9N2 + x10C
Where the primary reaction form of Sulphur Oxide  S(s) + O2(g) = SO2 (g) and
The secondary reaction SO2 (g) + 1/2O2(g) = SO3
Where the primary reaction form of Nitrogen Oxide 1/2N(s) + 1/2O2(g) = NO(g)
(Temperature is above 1600 C or 2900 F)
The secondary reaction NO(g) + 1/2O2(g) = NO2(g)
-----------------------------------------------------------------------------------------------------------------------------------Figure2: The Overall Reaction Equations
This study seeks to develop predictive modeling to support management decision making. Since NOx is
one of the key contributors to Thailand’s pollution, the focus of this study is on the following research
question “What are the most important factors that influence the stack emission of NOx and SOx in the
combustion process?” To answer this research question, the researcher examine whether more complex
analytical models using several data mining methodologies and algorithms can be predict and explanation.
In this study, we follow the five steps of the SEMMA methodology– Sample, Explore, Modify, Model,
and Assess
Research Framework:
In this study, we provide the in-depth analysis on how data mining approach can be a great help to
improve the overall plant performance and to reduce the potential plant pollution. For the preliminary
analysis, the data set contains a total of 30,000 records with 117 variables, which is derived from
approximately 11 months of power plant operations from 2013 to 2014. Figure 3 and Table 1 present the
research framework and the examples of variables used in this study.
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Data Partition
Data
Data
Data Preparation
-Select Data
Collect initial process
-Describe Data
-Explore Data
-Verify Data Quality
-Clean Data
-Construct Data
-Intregrate Data
Model Building
Decision Trees
Neural network
Regression
Model Testing
Training
Data Set
Validating
Data Set
Model Evaluation
Model Deployment
Reseach Question
“What factors have a great impact on stack
emission when the coal qualities are changed
in the combustion process?”
Figure 3: The Research Framework for Stack NOx and SOx Prediction Model
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Table 1: An Example of Variables Used in this Study
Variable
Stack NOx Emission
Stack SOx Emission
Date
Operation control process
Model
Role
Target
Target
ID
Input
Measureme
nt Level
Nominal
Nominal
Nominal
Nominal
Description
The flue gas exhaust NOx emissions (ppm)
The flue gas exhaust SOx emissions (ppm)
Date and time : hourly average
Operation control conditions:
For Stack NOx and SOx prediction:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
Fuel qualities
Input
Nominal
3RY_SH_OUTLET MAIN STEAM PRESS (A)
3RY_SH_OUTLET MAIN STEAM PRESS (B)
3RY_RH_OUTLET STEAM PRESS
3RY RH OUTLET STEAM PRESS
3RY SH OUTLET MAIN STEAM TEMP-A
3RY SH OUTLET MAIN STEAM TEMP-B
HOT REHEAT STEAM TEMP (ST INLET) (RH-2)
HOT REHEAT STEAM TEMP (ST INLET) (LH)
HOT REHEAT STEAM TEMP (ST INLET) (RH-1)
ECO OUTLET GAS O2-A
ECO OUTLET GAS O2-B
ECO OUTLET GAS O2 (LOW SELECT)
COAL FLOW-A
COAL FLOW-B
COAL FLOW-C
COAL FLOW-D
COAL FLOW-E
COAL FLOW-F
BURNER TILT
ADJUSTABLE CONTROL DRIVE _AA_A DEM-1 RB
ADJUSTABLE CONTROL DRIVE _AA_A DEM-2 RB
ADJUSTABLE CONTROL DRIVE _AA_B DEM-2 RB
ADJUSTABLE CONTROL DRIVE _AA_C DEM-1 RB
ADJUSTABLE CONTROL DRIVE _AA_D DEM-1 RB
ADJUSTABLE CONTROL DRIVE _AA_C DEM-2 RB
ADJUSTABLE CONTROL DRIVE _AA_D DEM-2 RB
AUX DAMPER _U_AA_A DEM-1 RB
AUX DAMPER _U_AA_B DEM-1 RB
AUX DAMPER _L_AA_A DEM-1 RB
AUX DAMPER _L_AA_B DEM-1 RB
AUX DAMPER _U_AA_A DEM-2 RB
AUX DAMPER _U_AA_B DEM-2 RB
AUX DAMPER _L_AA_A DEM-2 RB
AUX DAMPER _L_AA_B DEM-2 RB
AUX DAMPER _U_AA_C DEM-1 RB
AUX DAMPER _U_AA_D DEM-1 RB
AUX DAMPER _L_AA_C DEM-1 RB
AUX DAMPER _L_AA_D DEM-1 RB
AUX DAMPER _U_AA_C DEM-2 RB
AUX DAMPER _U_AA_D DEM-2 RB
AUX DAMPER _L_AA_C DEM-2 RB
AUX DAMPER _L_AA_D DEM-2 RB
AUX DAMPER _U_AA_C DEM-2 RB
AUX DAMPER _U_AA_D DEM-2 RB
AUX DAMPER _L_AA_C DEM-2 RB
AUX DAMPER _L_AA_D DEM-2 RB
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
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87
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90
91
UNIT_GROSS_MW
MAIN_STEAM_FLOW
MAIN STEAM PRESS
MAIN STEAM TEMP
RH STEAM TEMP
FDF-A INLET VANE CD FB
FDF-B INLET VANE CD FB
IDF-A INLET VANE CD FB
IDF-B INLET VANE CD FB
BOOSTER FAN-A BLADE PICH TR SIGNAL
BOOSTER FAN-B BLADE PICH TR SIGNAL
BUF-A MOTOR CURRENT
BUF-B MOTOR CURRENT
FDF-A MOTOR CURRENT
FDF-B MOTOR CURRENT
IDF-A MOTOR CURRENT
IDF-B MOTOR CURRENT
AH_A INLET GAS TEMP (2)
AH_B INLET GAS TEMP (2)
AH_A INLET GAS TEMP (1)
AH_B INLET GAS TEMP (1)
AH_A OUTLET FLUE GAS TEMP (1)
AH_A OUTLET FLUE GAS TEMP (2)
AH_A OUTLET FLUE GAS TEMP (3)
AH_B OUTLET FLUE GAS TEMP (1)
AH_B OUTLET FLUE GAS TEMP (2)
AH_B OUTLET FLUE GAS TEMP (3)
IDF-A INLET FLUE GAS TEMP
IDF-B INLET FLUE GAS TEMP
FGD GAS DUCT INLET GAS TEMP
STACK INLET FLUE GAS TEMPERATURE
PULVERIZER-A PRIMARY AIR FLOW
PULVERIZER-B PRIMARY AIR FLOW
PULVERIZER-C PRIMARY AIR FLOW
PULVERIZER-D PRIMARY AIR FLOW
PULVERIZER-E PRIMARY AIR FLOW
PULVERIZER-F PRIMARY AIR FLOW
TOTAL AIR FLOW TON PER HOUR
AH_A OUTLET SECONDARY AIR FLOW
AH_B OUTLET SECONDARY AIR FLOW
FGD BOOSTER FAN-A FLUE GAS FLOW
FGD BOOSTER FAN-B FLUE GAS FLOW
FGD BOOSTER FAN FLUE GAS FLOW
STACK INLET FLUE GAS FLOW
FGD BYPASS DUCT FLUE GAS DIFF PRESS
14
15
16
17
18
19
20
21
22
23
24
25
26
Weight average K2O Input to Bolier
Weight average MgO Input to Bolier
Weight average Mn3O4 Input to Bolier
Weight average Na2O Input to Bolier
Weight average Nitrogen Input to Bolier
Weight average P2O5 Input to Bolier
Weight average SiO2 Input to Bolier
Weight average SO3 Input to Bolier
Weight average Ti2O Input to Bolier
Weight average Total Mositure Input to Boiler
Weight average Total Sulphur Input to Bolier
Weight average Volatile Matter Input to Boiler
Ash analysis percent Unburnt Carbon
Coal qualities and chemical composition in coal
1
2
3
4
5
6
7
8
9
10
11
12
13
Weight average Al2O3 Input to Bolier
Weight average Ash Input to Boiler
Weight average CaO Input to Bolier
Weight average Carbon Input to Boiler
Weight average Chloride Input to Boiler
Weight average ESP K-Factor Input to Bolier
Weight average F.T. Input to Bolier
Weight average Fe2O3 Input to Bolier
Weight average Fixed Carbon Input to Bolier
Weight average fuel ratio Input to Bolier
Weight average Total Heat Input to Boiler
Weight average HGI Input to Boiler
Weight average Hydrogen Input to Bolier
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Preliminary Result and Discuss: NOx - The Flue Gas Exhausts Emission Prediction
Data Exploration
The first task in data exploration is to get some senses of the potential causes of increasing NO x level in
the power generation process. Based on the Pearson Correlation results in Figure 4, increasing Motor
Current, Unit_Gross_MW, and AH_A_Outlet_Secondary_Air_Flow mean higher NOx level. Similarly, we
observe a decrease in the NOx level, when increasing AUX_DAMPER_U_AA_CDEM and
Weight_average_Nitrogen_Input. According to the variable worth in Figure 4, the top five variables
which have a great impact on NOx emission include AH_Outlet_Secondary_Air_Flow,
FDF_Motor_Current, ECO_Outlet_O2, Unit_Gross_MW, AH_inlet_Gas_temp, Main_steam_flow;
meanwhile, the lowest variable worth is Ambient_Pressure
Figure 4: NOx – Variable Worth and Pearson Correlation
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Model Development
The dataset is partitioned into 70% for training and 30% for validation. Six predictive models are
developed and compared: (1) a decision tree with a maximum of 2 branches, (2) a decision tree with a
maximum of 3 branches, (3) a neural network with 1 hidden layer and 3 hidden units, (4) an autoneural
network, (5) a stepwise regression, and (6) a polynomial regression.
Model Comparison
The training dataset is used to build predictive models. The next step is to find out the accuracy of the
model fit on the validation dataset. We measure the performance of our models based on the ASE
(average squared error). ASE is evaluated among the three models built on the validation dataset. The
lower the ASE, the better the model is predicted. The results of models developed to predict NOx level
are presented in Figure 5. Stepwise regression generates the best overall prediction accuracy with the
lowest ASE, 48.889, followed by polynomial regression (ASE = 50.780) and a 3-way decision tree (ASE =
67.312).
A 3-way Decision Tree Model
Figure 6 summarizes the variable importance selected for constructing the 3-way decision tree model. The
top three important factors include (1) FDF_A_Motor_Current, (2)
Weight_average_F_T_Input_to_Boiler, and (3) AH_B Outlet Secondary Air Flow. A total of 29 “IfThen” rule-based predictions from the decision tree model is generated with the maximal tree of 41 leafs.
Stepwise Regression Model
The stepwise regression equation is presented below. The power plant may start by monitoring the
Weight_average_Mn3O4_Input_to_Boi and Weight_average_fuel_ratio_Input_to_Boi in the combustion process.
The power plant may try to improve operations to reduce the ECO_Outlet_Gas_O2_Low_Select and
FDF_A_Motor_Current or increase Aux_Damper_U_AA_A_RB, Coal_Flow_C, and Coal_Flow_C to reduce
the NOx emission.
Stack NOx Emission = - 251.8 - 0.7626(Adjust_Contorl_Drive_AA_C_D)
+ 0.8761(AH_A_inlet_Gas_Temp) +0.6903(AH_A_outet_Gas_Temp)
- 1.3539(AH_B_inlet_Gas_Temp) – 0.5702(Aux_Damper_U_AA_A_RB)
– 0.8045(Coal_Flow_A) – 0.2535(Coal_Flow_B) – 0.3303(Coal_Flow_C)
+ 15.4966(ECO_Outlet_Gas_O2_Low_Select) + 2.2013
(FDF_A_Motor_Current)
+ 1.0684(Main_Steam_Press) + 0.4897(Pulverizer_A_Primary_Air_flow)
+ 0.2240(Pulverizer_C_Primary_Air_flow)
– 175.6(Weight_average_Mn3O4_Input_to_Boi)
– 13.0318(Weight_average_Na2O_Input_to_Boi)
+ 30.5430(Weight_average_fuel_ratio_Input_to_Boi)
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Figure 5: Fit Statistics for NOx Prediction
Figure 6: A 3-Way Decision Tree Model
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Conclusion
This finding helps the power plant prioritize the important factors associated with the NO x emission; thus
closer attention to those factors can be promptly initiated. The results of this study show that data mining
approaches are capable of predicting the NOx level, given sufficient data with the proper input variables.
Power plant managers can use their existing databases along with advanced analytics through data mining
approaches to accurately predict any other toxic substances to monitor plant performance and especially
to comply with environmental regulations.
Contact Information
Your comments and questions are valued and encouraged. Contact the authors at:
Name: Jongsawas Chongwatpol, Ph.D.
Enterprise: NIDA Business School, National Institute of Development Administration
Address: 118 Seri Thai Road, Bangkapi, Bangkok, 10240 Thailand
Email: [email protected], [email protected]
Jongsawas Chongwatpol is a lecturer in NIDA Business School at National Institute of Development
Administration. He received his BE in industrial engineering from Thammasat University, Bangkok,
Thailand, and two MS degrees (in risk control management and management technology) from University
of Wisconsin - Stout, and PhD in management science and information systems from Oklahoma State
University. His research has recently been published in major journals such as Decision Support Systems,
Decision Sciences, European Journal of Operational Research, and Journal of Business Ethics. His major
research interests include decision support systems, RFID, manufacturing management, data mining, and
supply chain management.
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