RBF neural network inferential sensor for process emission monitoring

Control Engineering Practice

Volumn 21, Issue 7, 2013, Pages 962-970

  Iliyas, Surajdeen A ^a   Elshafei, Moustafa ^a   Habib, Mohamed A ^a   Adeniran, Ahmed A ^a  

^a KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS   (Saudi Arabia)

Author keywords

Boilers; CFD simulation; Combustion; Inferential sensor; Neural networks; NOx emission

Indexed keywords

3D TEMPERATURE DISTRIBUTIONS; CFD SIMULATIONS; CONTINUOUS EMISSION MONITORING SYSTEM; CONTINUOUS EMISSION MONITORS; INFERENTIAL SENSORS; NEURAL NETWORK STRUCTURES; PERFORMANCE PREDICTION; SUBSTANTIAL REDUCTION;

BOILERS; COMBUSTION; COMPUTATIONAL FLUID DYNAMICS; NEURAL NETWORKS; SENSORS; THREE DIMENSIONAL; THREE DIMENSIONAL COMPUTER GRAPHICS;

INDUSTRIAL EMISSIONS;

EID: 84877134135     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2013.01.007     Document Type: Article

References (24)

1. Binchini M., Frasconi P., Gori M. Learning without local minimum in radial basis function networks. IEEE Transactions on Neural Networks 1995, 6:749-755.
2. Chen S., Cowan C.F.N., Grant PM Orthogonal least square learning algorithm for radial basis function network. IEEE Transactions on Neural Networks 1991, 2:302-309.
3. Coelho P.J., Carvalho M.G. Evaluation of a three-dimensional mathematical model of a power station boiler. Journal for Gas Turbines and Power 1996, 118:887-895.
4. Darken C., Moody J. Fast adaptive K-means clustering: some empirical results. IEEE International Journal Conference on Neural Networks 1990, vol. II:233.
5. Dong D., McAvoy T.J., Chang L.J. Emission monitoring using multivariate soft sensors. Proceedings of the American Control Conference 1995, 1:761-765.
6. x formation in natural gas burner flames. Fuel 1993, 72(4):497-503.
7. x predictive emissions monitoring system at a TMP Mill. Process Control News 2000, 20:1-4.
8. Graziani, S., Pitronel, N., Xibilia, M. G., amp; Barbalace, N. (2004), Improving monitoring of NO, emissions in refineries. In: LMTC 2004-Instrumentation and measurement technology conference. pp. 594-597.
9. Habib, M. A., & Elshafei, M. (2006), Computer simulation of NOx formation in boilers. In: The 2006 international conference on modeling, simulation and visualization methods, MSV'2006. pp. 94-100.
10. Habib, M. A., Ben-Mansour, R., & Antar, M. A. (2004), Flow field and thermal characteristics in a model of a tangentially fired furnace under different conditions of burner tripping. Journal Heat and Mass Transfer, paper no. 10.1007/s00231-004-0593-6.
11. Hagan M.T., Menhaj M. Training feed-forward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 1999, 5(6):989-993. 1994.
12. Hanson R.K., Salimian S. Survey of rate constants in H/N/O systems. Combustion chemistry 1984, 361-421. Springer-Verlag. W.C. Gardiner (Ed.).
13. Haykin S. Neural networks and learning machines 2009, Prentice Hall. (3rd ed.).
14. Kibrya, G., & Botros, K. K. (2002), A neural network based predictive emission monitoring model for NOx emission from a gas turbine combustor. In: Engineering technology conference on energy ETCE'2002, Houston, TX.
15. Li, K., & Thompson, S (2000), A cascaded neural network and its application to modeling power plants pollutants emission. In: Proceedings of the third world congress on intelligent control and automation. Vol. 2, pp. 992-997.
16. Liang N.Y., Huang G.B., Saratchandran P., Sundararajan N. A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks 2006, 17(6):1411-1423.
17. Liu B.L., Xuan Z. Soft-sensing technology in the combustion optimization for power plant. Journal of Advanced Materials Research 2011, 179-180:859-864.
18. Moody J., Darken C. Learning with localized receptive fields. Connectionist Models Summer School 1988, 133-143.
19. x emissions using neural networks. Journal of the Air & Waste Management Association 1998, 48:74-185.
20. x abatement in kiln plants in the cement industry. ZKG International 1999, 52:304-321.
21. x emission using dynamic neural network. Journal of Computer and Electrical Engineering 2009, 35(4):527-608.
22. Tronci S., Baratti R., Servida A. Monitoring pollutant emissions in a 4.8MW power plant through neural network. Neurocomputing 2002, 43:3-15.
23. Yang, Y., & Chai, T. (1997), Soft sensing based on artificial neural network. In: Proceedings of the American control conference (pp. 674-678). New Mexico.
24. x emission characteristics of a tangentially fired boiler with artificial neural networks. Energy 2004, 29:167-183.