Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Engineering Applications of Artificial Intelligence

Volumn 25, Issue 1, 2012, Pages 147-158

  Zhou, Hao ^a   Zhao, Jia Pei ^a   Zheng, Li Gang ^a,b   Wang, Chun Lin ^a   Cen, Ke Fa ^a  

^a ZHEJIANG UNIVERSITY   (China)

^b HENAN POLYTECHNIC UNIVERSITY   (China)

Author keywords

Ant colony optimization; Artificial neural networks; Combustion modeling; NOx emissions modeling; Support vector regression

Indexed keywords

ANT-COLONY OPTIMIZATION; ARTIFICIAL NEURAL NETWORK; COAL FIRED UTILITY BOILERS; COAL-FIRED UTILITIES; COMBUSTION MODELING; COMBUSTION OPTIMIZATION; EMISSIONS MODEL; GAUSSIAN KERNELS; GENERALIZED REGRESSION; MODELING TECHNIQUE; NEURAL NETWORKS MODEL; OPTIMAL PARAMETER; OPTIMIZATION ALGORITHMS; PREDICTIVE ACCURACY; PREDICTIVE EMISSIONS MONITORING; REAL TIME MODELING; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS; TIME RESPONSE;

AIR POLLUTION CONTROL EQUIPMENT; ALGORITHMS; COAL; COAL FIRED BOILERS; NEURAL NETWORKS; OPTIMIZATION; PULVERIZED FUEL FIRED BOILERS; REGRESSION ANALYSIS;

COAL COMBUSTION;

EID: 80855123687     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2011.08.005     Document Type: Article

References (55)

1. x and CO prediction with variable tapped delay line neural networks J. Intergrated Comput.-Aided Eng. 6 1999 27 39
2. Azid, I.A.; Ripin, Z.M.; Aris, M.S.; Ahmad, A.L.; Seetharamu, K.N.; Yusoff, R.M.; 2000. Predicting combined-cycle natural gas power plant emissions by using artificial neural networks. In: Proceedings of TENCON, vol. 3, Kuala Lumpur, Malaysia, pp. 512517.
3. Baines, G.; 1999. Neural networks for boiler emission prediction. In: Proceedings of the 16th IEEE Conference on Instrumentation and Measurement Technology, vol. 1, Venice, Italy, pp. 435439.
4. M. Barletta, A. Gisario, and S. Guarino Modelling of electrostatic fluidized bed (EFB) coating process using artificial neural networks Eng. Appl. Artif. Intell. 20 6 2007 721 733 (Pubitemid 46962282)
5. P.G. Benardos, and G.C. Vosniakos Optimizing feedforward artificial neural network architecture Eng. Appl. Artif. Intell. 20 3 2007 365 382 (Pubitemid 46366649)
6. x emissions while improving performance. In: Proceedings of the Dynamic Modeling Control Applications for Industry Workshop. IEEE Industry Applications, Vancouver, BC, Canada, pp. 16.
7. C.W. Chan, and G.H. Huang Artificial intelligence for management and control of pollution minimization and mitigation processes Eng. Appl. Artif. Intell. 16 2 2003 75 90
8. Chang, C.-C.; Lin, C.-J.; 2001. LIBSVM: A Library for Support Vector Machines. Software available from: 〈http://www.csie.ntu.edu.tw/cjlin/ libsvm〉.
9. V. Cherkassky, and Y. Ma Practical selection of SVM parameters and noise estimation for SVM regression Neural Networks 17 1 2004 113 126 (Pubitemid 38019003)
10. Chong, A.Z.S.; Wilcox, S.J.; Ward, J.; 2001. Prediction of gaseous emissions from a chain grate stoker boiler using neural networks of ARX structure. In: IEE Proceedings of Science, Measurement and Technology, vol. 148(3), pp. 95102.
11. x optimisation Fuel 81 5 2002 619 626 (Pubitemid 34181559)
12. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines and Other Kernel-based Learning Methods 2004 Cambridge University Press
13. x) emissions from a natural gas-fired burner using a simple genetic algorithm. In: Proceedings of the Fourth IEEE Conference on Control Applications, Albany, NY, USA, pp. 673678.
14. Dong, D.; McAvoy, T.J.; Chang, L.J.; 1995. Emission monitoring using multivariate soft sensors. In: Proceedings of the American Control Conference, vol. 1, Seattle, WA, USA, pp. 761765.
15. x emissions in thermal power plants using neural networks J. Eng. Gas Turbines Power 123 2 2001 465 471 (Pubitemid 32575825)
16. Frenken, R.; Rozendaal, C.; Dijk, H.; Knoester, P.; 1996. Development of an advisory system based on a neural network for the operation of a coal fired power plant. In: Proceedings of the International Conference on Artificial Neural Networks, pp. 197202.
17. E. Frias-Martinez, A. Sanchez, and J. Velez Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition Eng. Appl. Artif. Intell. 19 6 2006 693 704 (Pubitemid 44292511)
18. C. Gao, E. Bompard, R. Napoli, and H. Cheng Price forecast in the competitive electricity market by support vector machine Physica A: Stat. Mech. Appl. 382 1 2007 98 113 (Pubitemid 46856094)
19. x emissions in refineries. In: Proceedings of the lMTC 2004 - Insuummtation and Measurement Technology Conference, vol. 2, Cormo, Italy, pp. 10531056.
20. J.V. Hansen, and R.D. Meservy Learning experiments with genetic optimization of a generalized regression neural network Decision Support Syst. 18 34 1996 317 325
21. S.C. Hill, and D.L. Smoot Modeling of nitrogen oxides formation and destruction in combustion systems Prog. Energy Combust. Sci. 26 46 2000 417 458
22. Hsu, C.W.; Chang, C.C.; Lin, C.J.; 2003. A Practical Guide to Support Vector Classification. Available from: 〈http://www.csie.ntu.edu.tw/ ∼cjlin/papers/guide/guide.pdf〉.
23. Z. Hua, Y. Wang, X. Xu, B. Zhang, and L. Liang Predicting corporate financial distress based on integration of support vector machine and logistic regression Expert Syst. Appl. 33 2 2007 434 440 (Pubitemid 46150047)
24. C.-L. Huang, and C.-J. Wang A GA-based feature selection and parameters optimization for support vector machines Expert Syst. Appl. 31 2 2006 231 240 (Pubitemid 44294444)
25. E. Ikonen, K. Najim, and U. Kortela Neuro-fuzzy modelling of power plant fuelgas emissions Eng. Appl. Artif. Intell. 13 6 2000 705 717 (Pubitemid 32034101)
26. Jia, J.H.; 2007. Applying Artificial Intelligence, Advanced Control, and Optimization Technologies in Coal Fired Generating Units. 〈http://www.netl. doe.gov〉.
27. S.A. Kalogirou Artificial intelligence for the modeling and control of combustion processes: a review Prog. Energy Combust. Sci. 29 6 2003 515 566
28. Kamas, J.; Keeler, J.; 1995. Predictive emissions monitoring systems: a low-cost alternative for emissions monitoring [in cement industry]. In: Proceedings of the 37th IEEE Cement Industry Technical Conference, San Juan, Puerto Rico, pp. 497509.
29. N. Karaboga, A. Kalinli, and D. Karaboga Designing digital IIR filters using ant colony optimisation algorithm Eng. Appl. Artif. Intell. 17 3 2004 301 309
30. x emission in a coal-fired power generation plant. In: Proceedings of the Fifteenth Triennial World Congress, vol. 1, Barcelona, Spain, pp. 761765.
31. K. Li, S. Thompson, P.A. Wieringa, J. Peng, and G.R. Duan Neural networks and genetic algorithms can support human supervisory control to reduce fossil fuel power plant emissions Cognit. Technol. Work 5 2 2003 107 126 (Pubitemid 36921828)
32. Y.P. Liu, M.G. Wu, and J.X. Qian Predicting coal ash fusion temperature based on its chemical composition using ACO-BP neural network Thermochim. Acta 454 1 2007 64 68 (Pubitemid 46185500)
33. x reduction in power plants Int. J. Electr. Power Energy Syst. 20 3 1998 169 176 (Pubitemid 128391663)
34. x device control performance using a software sensor Control Eng. Pract. 6 10 1998 1267 1276 (Pubitemid 128385000)
35. x control: research to application Prog. Energy Combust. Sci. 23 3 1997 233 266 (Pubitemid 127385502)
36. H. Niska, T. Hiltunen, A. Karppinen, J. Ruuskanen, and M. Kolehmainen Evolving the neural network model for forecasting air pollution time series Eng. Appl. Artif. Intell. 17 2 2004 159 167
37. S. Osowski, and K. Garanty Forecasting of the daily meteorological pollution using wavelets and support vector machine Eng. Appl. Artif. Intell. 20 6 2007 745 755 (Pubitemid 46962287)
38. x reduction Mod. Power Syst. 20 5 2000 59 61
39. x) emissions modeling and advisory control. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 6, Orlando, FL, USA, pp. 38343839.
40. B. Samanta, K.R. Al-Balushi, and S.A. Al-Araimi Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection Eng. Appl. Artif. Intell. 16 78 2003 657 665
41. K. Socha, and C. Blum An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training Neural Comput. Appl. 16 3 2007 235 247
42. D.F. Specht A general regression neural network IEEE Trans. Neural Networks 2 6 1991 568 576
43. S. Tronci, R. Baratti, and A. Servida Monitoring pollutant emissions in a 4.8 MW power plant through neural network Neurocomputing 43 14 2002 3 15 (Pubitemid 34041223)
44. B. Ustun, W.J. Melssen, and L.M.C. Buydens Visualisation and interpretation of Support Vector Regression models Anal. Chim. Acta 595 12 2007 299 309 (Pubitemid 46990301)
45. C.-M. Vong, P.-K. Wong, and Y.-P. Li Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference Eng. Appl. Artif. Intell. 19 3 2006 277 287 (Pubitemid 43177591)
46. C.-H. Wu, G.-H. Tzeng, Y.-J. Goo, and W.-C. Fang A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy Expert Syst. Appl. 32 2 2007 397 408 (Pubitemid 44754927)
47. S.H. Yang, B.H. Chen, and X.Z. Wang Neural network based fault diagnosis using unmeasurable inputs Eng. Appl. Artif. Intell. 13 3 2000 345 356
48. S.F. Yuan, and F.L. Chu Fault diagnostics based on particle swarm optimisation and support vector machines Mech. Syst. Signal Process. 21 4 2007 1787 1798
49. Zheng, L.G.; 2004. Optimization Operation of the High Capacity Power Station Boiler and Measurement on GasSolid Multiphase Flow by OPTICS WAVE Method. Masters Thesis for Zhejiang University.
50. x emissions in coal-fired utility boiler Energy Fuels 22 2 2008 1034 1040
51. x emissions from coal fired boilers using generalized regression neural network, bioinformatics and biomedical engineering. In: Proceedings of the Second ICBBE, vol. 3, Shanghai, China, pp. 19161919.
52. x combustion modification at a coal-field utility boiler Expert Syst. Appl. 36 2 2009 2780 2793
53. x emission characteristics of a tangentially fired boiler with artificial neural networks Energy 29 1 2004 167 183 (Pubitemid 37312615)
54. H. Zhou, K.F. Cen, and J.R. Fan Multi-objective optimization of the coal combustion performance with artificial neural networks and genetic algorithms Int. J. Energy Res. 29 6 2005 499 510 (Pubitemid 40652256)
55. x pulverized coal combustion Fuel 80 15 2001 2163 2169