Computational intelligence approach for NOx emissions minimization in a 30 MW premixed gas burner
Abstract
Artificial intelligence algorithms have become a research hotspot in attempts to reduce NOx emissions in gas burners through NOx emission modeling and optimizing operating parameters. This paper compres the predictive accuracy of NOx emission models based on LSSVM, SVR and ELM. CGA and three other GA based hybrid algorithms proposed to modify CGA were employed to optimize the operating parameters of a 30MW gas burner in order to reduce NOx emission. The results show that the NOx emission model built by LSSVM is more accurate than that of SVR and ELM. The mean relative error and correlation coefficient obtained by the LSSVM model were 0.0731% and 0.999, respectively. Among the four optimization algorithms, the novel TSGA proposed in this paper showed its superiority over the other three algorithms, excelling in its global searching ability and stability. The LSSVM plus TSGA method is a potential combination for predicting and reducing NOx emission by optimizing the operating parameters for the gas burner on-line.References
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S.R. (2015) A rigorous model to predict the amount of
Dissolved Calcium Carbonate Concentration throughout
oil eld brines: Side eect of pressure and temperature.
Fuel, 139, 154{159.
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heuristics, Springer.
R. (2015) Scatter search with path relinking for the
job shop with time lags and setup times. Computers
& Operations Research, 60, 37{54.
2.Xie, K., Li, W., and Zhao, W. (2010) Coal chemical
industry and its sustainable development in China.
Energy, 35 (11), 4349{4355.
3.Wu, S. (2002) AIR-SURROUNDING-FUEL(ASF)
PULVERIZED COAL BIAS COMBUSTION TECHNOLOGY
WITH LOW Nox EMISSION. Chinese Jour-
nal of Mechanical Engineering, 38 (01), 108.
4.BARTOSZEK, F.E., VASQUEZ, L.R., HE, W., and
CHANG, J.S. (1998) Removal of NOxfrom Flue Gas
by Reburning with Plasma Activated Natural Gas: Review
and Economics. Combustion Science and Tech-
nology, 133 (1-3), 161{190.
5.Seputro, S., and Sudarmanta, B. (2019) Numerical
Investigation of Over Fire Air (OFA) Eect on
Flow Characteristics NOx Combustion and Emission
in a 600 Mw Tangentially Fired Pulverized Coal Boiler.
IPTEK The Journal of Engineering, 5 (2).
6.Hiramatsu, T., Najafabadi, H.M., Kobayashi, N.,
and Itaya, Y. (2012) Non-Catalytic Decomposition
of NO by Microwave Plasma Induced at Atmospheric
Pressure in an Activated Coke Bed. KAGAKU KO-
GAKU RONBUNSHU, 38 (6), 397{402.
7.ZHANG, X. (2009) Eects of Stereo-staged Combustion
Technique on NOx Emmision Charactisctics.
Chinese Journal of Mechanical Engineering, 45 (02),
199.
8.Zheng, L., Zhou, H., Wang, C., and Cen, K. (2008)
Combining Support Vector Regression and Ant Colony
Optimization to Reduce NOx Emissions in Coal-Fired
Utility Boilers. Energy & Fuels, 22 (2), 1034{1040.
9.Kowalczyk, B. (2014) Model of an ANSALDO
V94.2 gas turbine from Lublin Wrotkow Combined
Heat and Power Plant using GateCycle™ software.
Journal of Power Technologies, 94.
10.Isaza, C.V., Sarmiento, H.O., Kempowsky-Hamon,
T., and LeLann, M.-V. (2014) Situation prediction
based on fuzzy clustering for industrial complex processes.
Information Sciences, 279, 785{804.
11.Li, G., Niu, P., Wang, H., and Liu, Y. (2014) Least
Square Fast Learning Network for modeling the combustion
eciency of a 300WM coal-red boiler. Neu-
ral Networks, 51, 57{66.
12.Li, G., Niu, P., Ma, Y., Wang, H., and Zhang,
W. (2014) Tuning extreme learning machine by an
improved articial bee colony to model and optimize
the boiler eciency. Knowledge-Based Systems, 67,
278{289.
13.Sainlez, M., and Heyen, G. (2013) Comparison of
supervised learning techniques for atmospheric pollutant
monitoring in a Kraft pulp mill. Journal of Com-
putational and Applied Mathematics, 246, 329{334.
14.Li, G., Niu, P., Liu, C., and Zhang, W. (2012) Enhanced
combination modeling method for combustion
eciency in coal-red boilers. Applied Soft Comput-
ing, 12 (10), 3132{3140.
15.Rusinowski, H., and Stanek, W. (2010) Hybrid
model of steam boiler. Energy, 35 (2), 1107{1113.
16.Kalogirou, S.A. (2003) Articial intelligence for the
modeling and control of combustion processes: a review.
Progress in Energy and Combustion Science,
29 (6), 515{566.
17.Lee, J.W., Park, Y.J., Kim, I.T., and Lee, K.W.
(2008) Clinical Results After Application of Bevacizumab
in Recurrent Pterygium. Journal of the Ko-
rean Ophthalmological Society, 49 (12), 1901.
18.Yin, C., Rosendahl, L., Clausen, S., and Hvid, S.L.
(2012) Characterizing and modeling of an 88 MW
grate-red boiler burning wheat straw: Experience
and lessons. Energy, 41 (1), 473{482.
19.Lopes, C., and Perdig~ao, F. (2008) Event Detection
by HMM SVM and ANN: A Comparative Study,
in Lecture Notes in Computer Science, Springer Berlin
Heidelberg, pp. 1{10.
20.Smrekar, J., Potocnik, P., and Senegacnik, A.
(2013) Multi-step-ahead prediction of NOx emissions
for a coal-based boiler. Applied Energy, 106, 89{99.
21.Guo, M.L., Li, D.J., Du, C.B., Jia, Z.H., Qin, X.Z.,
Chen, L., Sheng, L., and Li, H. (2012) Prediction of
the Busy Trac in Holidays Based on GA-SVR, in
Advances in Intelligent and Soft Computing, Springer
Berlin Heidelberg, pp. 577{582.
22.Lu, Y., and Roychowdhury, V. (2007) Parallel randomized
sampling for support vector machine (SVM)
and support vector regression (SVR). Knowledge and
Information Systems, 14 (2), 233{247.
23.Hong, W.-C., Dong, Y., Chen, L.-Y., and Wei, S.-
Y. (2011) SVR with hybrid chaotic genetic algorithms
for tourism demand forecasting. Applied Soft Com-
puting, 11 (2), 1881{1890.
24.Wei, Z., Li, X., Xu, L., and Cheng, Y. (2013)
Comparative study of computational intelligence approaches
for NOx reduction of coal-red boiler. En-
ergy, 55, 683{692.
25.Chamkalani, A., Zendehboudi, S., Bahadori, A.,
Kharrat, R., Chamkalani, R., James, L., and Chatzis,
I. (2014) Integration of LSSVM technique with PSO
to determine asphaltene deposition. Journal of
Petroleum Science and Engineering, 124, 243{253.
26.Wu, J., Shen, J., Krug, M., Nguang, S.K., and
Li, Y. (2011) GA-based nonlinear predictive switching
control for a boiler-turbine system. Journal of Control
Theory and Applications, 10 (1), 100{106.
27.Zhou, H., Lu, J., Cao, Z., Shi, J., Pan, M., Li, W.,
and Jiang, Q. (2011) Modeling and optimization of
an industrial hydrocracking unit to improve the yield
of diesel or kerosene. Fuel, 90 (12), 3521{3530.
28.Ahmadi, M.A., and Ebadi, M. (2014) Evolving
smart approach for determination dew point pressure
through condensate gas reservoirs. Fuel, 117,
1074{1084.
29.Ahmadi, M.-A., Bahadori, A., and Shadizadeh,
S.R. (2015) A rigorous model to predict the amount of
Dissolved Calcium Carbonate Concentration throughout
oil eld brines: Side eect of pressure and temperature.
Fuel, 139, 154{159.
30.Ahmadi, M.A., Zahedzadeh, M., Shadizadeh, S.R.,
and Abbassi, R. (2015) Connectionist model for predicting
minimum gas miscibility pressure: Application
to gas injection process. Fuel, 148, 202{211.
31.Sels, V., Coelho, J., Dias, A.M., and Vanhoucke,
M. (2015) Hybrid tabu search and a truncated branchand-
bound for the unrelated parallel machine scheduling
problem. Computers & Operations Research, 53,
107{117.
32.Glover F, L.M. (1997) Tabu search, Kluwer Academic
Publishers.
33.Gendreau M, P.J.-Y. (2010) Handbook of meta-
heuristics, Springer.
Published
2020-03-25
How to Cite
CHEN, Weibo; LIU, Guixiong.
Computational intelligence approach for NOx emissions minimization in a 30 MW premixed gas burner.
Journal of Power Technologies, [S.l.], v. 100, n. 1, p. 21-31, mar. 2020.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/700>. Date accessed: 21 nov. 2024.
Issue
Section
Combustion and Fuel Processing
Keywords
NOx emission modeling, Operating parameters optimization, TSGA, Hybrid algorithm, Gas burner
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