Process control of a laboratory combustor using artificial neural networks

Computers and Chemical Engineering

Volumn 27, Issue 11, 2003, Pages 1605-1616

  Slanvetpan, T ^a,c   Barat, R B ^a   Stevens, J G ^b  

^a NEW JERSEY INSTITUTE OF TECHNOLOGY   (United States)

^b MONTCLAIR STATE UNIVERSITY   (United States)

^c Thai Shell   (Thailand)

Author keywords

Combustion system; Feed forward neural networks; Model based control; Process control; Time delay compensation

Indexed keywords

BACKPROPAGATION; COMBUSTORS; GAS EMISSIONS; NEURAL NETWORKS; THREE TERM CONTROL SYSTEMS;

INTERFACE CONTROL;

PROCESS CONTROL;

DATA; MODEL; NEURAL NETWORK; NITRIC OXIDE; PROCESS CONTROL; PROCESSING TECHNIQUE;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMBUSTION; COMPUTER PROGRAM; COMPUTER SIMULATION; CONTROL SYSTEM; ELECTRIC POWER PLANT; LABORATORY; MATHEMATICAL ANALYSIS;

EID: 0142043495     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0098-1354(03)00100-5     Document Type: Article

References (30)

1. Allen, M. G., Butler, C. T., Johnson, S. A., Lo, E. Y., & Russo, F. (1993). An imaging neural network combustion control system for utility boiler applications. Combustion and Flame 94, 205-214.
2. Bhat, N., & McAvoy, T. J. (1990). Use of neural nets for dynamic modeling and control of chemical process systems. Computers and Chemical Engineering 14 (4/5), 573-583.
3. x emissions while improving performance. Proceedings of the American Power Conference, 60(2), 667-672.
4. x emissions while improving performance. Proceedings of the International Joint Power Generation Conference, 1, 541-550.
5. Chovan, T., Catfolis, T., & Meert, K. (1996). Neural network architecture for process control based on the RTRL algorithm. AIChE Journal 42 (2), 493-502.
6. Gomm, J. B., Williams, D., Evans, J. T., & Doherty, S. K. (1997). Neural network applications in process modeling and predictive control. Transactions of the Institute of Measurement and Control 19 (4), 175-184.
7. Hecht-Nielsen, R. (1990). Neurocomputing. Addison-Wesley Publishing Company.
8. Hernández, E., & Arkun, Y. (1992). Study of the control-relevant properties of backpropagation neural network models of nonlinear dynamical systems. Computers and Chemical Engineering 16 (4), 227-240.
9. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward neural networks are universal approximators. Neural Network 2.
10. Hoskins, J. C., & Himmelblau, D. M. (1992). Process control via artificial neural networks and reinforcement learning. Computers and Chemical Engineering 16 (4), 241-251.
11. Kavchak, M., & Budman, H. (1999). Adaptive neural network structures for non-linear process estimation and control. Computers and Chemical Engineering 23, 1209-1228.
12. Mao, F. (1995). 'Combustion of methyl chloride, monomethyl amine, and their mixtures in a two stage turbulent flow reactor,' Doctoral Dissertation. New Jersey Institute of Technology.
13. 2. Combustion and Flame 105, 557-568.
14. Martins, F. G., & Coelho, M. A. N. (2000). Application of feedforward artificial neural networks to improve process control of PID-based control algorithms. Computers and Chemical Engineering 24, 853-858.
15. Milanic, S., Sel, D., Hvala, N., Strmcnik, S., & Karba, R. (1997). Applying artificial neural network models to control a time variant chemical plant. Computers and Chemical Engineering 21, S637-S642.
16. Miller, C. A., & Lemieux, P. M. (1998). Application of intelligent controls in EPA's combustion technology research and development. In US EPA. Research Triangle Park, NC: Nation Risk Management Research Laboratory.
17. Nascimento, C. A. O., Giudici, R., & Guardani, R. (2000). Neural network based approach for optimization of industrial chemical processes. Computers and Chemical Engineering 24, 2303-2314.
18. Nikravesh, M., Farell, A. E., & Stanford, T. G. (2000). Control of nonisothermal CSTR with time varying parameters via dynamic neural network control (DNNC). Chemical Engineering Journal 76, 1-16.
19. Olsson, Jan-Ola, Per Tunestål, & Bengt Johansson (2001). Closed-Loop Control of an HCCI Engine, SAE, vol. 1(1031), 111-120.
20. Palancar, M. C., Aragón, J. M., & Torrecilla, J. S. (1998). pH-control system based on artificial neural networks. Industrial and Engineering Chemistry Research 37, 2729-2740.
21. x reduction. Advance Instrumental Control 50, 405-414.
22. x control, In Proceedings of 1994 Annual Meeting of the American Power Conference (pp. 354-359).
23. Seborg, D. E., Edgar, T. F., & Mellichamp, D. A. (1989). Process Dynamics and Control. New York: Wiley.
24. Slanvetpan, T. (2002). Process control of a laboratory combustor using neural networks, In Doctoral Dissertation. Chemical Engineering Department, New Jersey Institute of Technology.
25. x and heat rate in utility boilers, In Proceedings of the Air & Waste Management Association Annual Conference (pp. 5385-5395).
26. Stephanopoulos, G. (1984). Chemical process control an introduction to theory and practice. Englewood Cliffs, New Jersey: PTR Prentice Hall.
27. Syu, M.-J., & Chen, B.-C. (1998). Back-propagation neural network adaptive control of a continuous wastewater treatment process. Industrial and Engineering Chemistry Research 37, 3625-3630.
28. Te Braake, H. A. B., van Can, E. J. L., Scherpen, J. M. A., & Verbruggen, H. B. (1998). Control of nonlinear chemical processes using neural models and feedback linearization. Computers and Chemical Engineering 22 (7-8), 1113-1127.
29. Tendulkar, S. B., Tambe, S. S., Chandra, I., Rao, P. V., Naik, R. V., & Kulkarni, B. D. (1998). Hydroxylation of phenol to dihydroxybenzenes: development of artificial neural-network-based process identification and model predictive control strategies for a pilot plant scale reactor. Industrial and Engineering Chemistry Research 37, 2081-2085.
30. Zurada, J. M. (1992). Introduction to artificial neural systems. West Publishing Company.