A new approach for function approximation in boiler combustion optimization based on modified structural AOSVR

Expert Systems with Applications

Volumn 36, Issue 4, 2009, Pages 8691-8704

  Si, Fengqi ^a   Romero, Carlos E ^b   Yao, Zheng ^b   Xu, Zhigao ^a   Morey, Robert L ^c   Liebowitz, Barry N ^d  

^a SOUTHEAST UNIVERSITY   (China)

^b LEHIGH UNIVERSITY   (United States)

^c AES Cayuga LLC   (United States)

^d New York State Energy Research and Development Authority   (United States)

Author keywords

Adaptive learning; Boiler combustion optimization; Function approximation; Support vector regression

Indexed keywords

BOILERS; COAL COMBUSTION; COAL DEPOSITS; COMBUSTION; FUNCTIONS; OPTIMIZATION; PROBABILITY DENSITY FUNCTION; REGRESSION ANALYSIS; SMOKE; THERMOCHEMISTRY;

ADAPTIVE LEARNING; BOILER COMBUSTION OPTIMIZATION; CHARACTERISTIC FUNCTIONS; COAL QUALITIES; COMBUSTION PRO CESS; DATA SETS; FIELD TESTS; FUNCTION APPROXIMATION; MODEL PARAMETERS; NEW APPROACHES; ON-LINE LEARNING METHODS; ON-LINE OPTIMIZATIONS; ON-LINE SUPPORTS; OPTIMAL CONTROLLERS; PLANT EQUIPMENTS; STRUCTURAL MATRICES; SUPPORT VECTOR REGRESSION; TEST CONDITIONS; THERMAL EFFICIENCIES; TRAINED SAMPLES; TRAINING DATA SETS; VARIABLE OPERATING CONDITIONS;

STRUCTURAL OPTIMIZATION;

EID: 60249096405     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.12.008     Document Type: Article

References (21)

1. Chaudhri A.B., Rashid A., and Zicari R. Xml data management: Native xml and xml-enabled database systems (2003), Addision-Wesley
2. De Angelo J.G., and Sjoberg C.E. The effect of coal quality on meeting the 1995 ozone season NOx cap at New York state electric & gas. Progress in Energy and Combustion 25 (1999) 341-352
3. 2 formation from fuel nitrogen. 15th symposium (international) on combustion (1975), Pittsburgh 1093-1102
4. Flake G.W., and Lawrence S. Efficient SVM regression training with SMO. Machine Learning 46 (2002) 271-290
5. Jung K.S., Keener T.C., and Khang S. Compositional factors affecting NOx emissions from Ohio coals. Fuel Progress Technology 74 (2001) 49-61
6. Kivinen J., Somla A.J., and Williamson R.C. Online learning with kernels. In: Dietterich T.G., Becher S., and Ghahramani Z. (Eds). Advances in neural information processing systems 14 (2002), Cambridge, MIT Press 785-792
7. Kusiak A., and Song Z. Combustion efficiency optimization and virtual testing: A data-mining approach. IEEE Transactions on Industrial Informatics 2 3 (2006) 176-184
8. Li Q., and Moon B. Indexing and querying XML data for regular path expressions. Proceedings of the 27th VLDB conference (2001), Roma 361-370
9. Li K., Thompson S., and Peng J.X. Modelling and prediction of NOx emission in a coal-fired power generation plant. Control Engineering Practice 12 (2004) 707-723
10. Ma J.S., Theiler J., and Perkins S. Accurate on-line support vector regression. Neural Computation 15 (2003) 2683-2703
11. Nørgaard M., Ravn O., Poulsen N.K., and Hansen L.K. Neural networks for modelling and control of dynamic systems (2000), Springer-Verlag, London
12. Platt J.C. Using analytic QP and sparseness to speed training of support vector machines. Proceedings of the 1998 conference on advances in neural information processing systems II (1998), MIT Press 557-563
13. Schölkopf B., Burges C., and Smola A.J. Advances in kernel methods - Support vector learning (1999), MIT Press, Cambridge, MA
14. Schölkopf B., and Smola A.J. Learning with kernels (2002), MIT Press, London, England
15. Shevade S.K., Keerthi S.S., Bhattacharyya C., and Murthy K.R.K. Improvements to the SMO algorithm for SVM regression. IEEE transactions on neural networks 11 5 (2000) 1188-1193
16. Smola A.J., and Schölkopf B. A tutorial on support vector regression. Statistics and Computing 14 4 (2004) 199-222
17. Vapnik V., and Chervonenkis A. Theory of pattern recognition (1974), Nauka, Moscow, Russia
18. Visona S.P., and Stanmore B.R. 3-D modeling of NOx formation in a 275 MW utility boiler. Journal of the Institute of Energy 69 (1996) 68-79
19. Wang L. Support vector machines: Theory and applications (2005), Springer, Berlin
20. Zhao C.Y., Zhang H.X., Zhang X.Y., Liu M.C., and Fanb B.T. Application of support vector machine (SVM) for prediction toxic activity of different data sets. Toxicology 217 2-3 (2006) 105-119
21. Zhou H., Cen K.F., and Mao J.B. Combining neural network and genetic algorithms to optimize low NOx pulverized coal combustion. Fuel 80 (2001) 2163-2169