Context-based recognition of process states using neural networks

Chemical Engineering Science

Volumn 60, Issue 4, 2005, Pages 935-949

  Srinivasan, R ^a   Wang, C ^a   Ho, W K ^a   Lim, K W ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Fault diagnosis; Pattern recognition; State identification

Indexed keywords

CATALYTIC CRACKING; CHEMICAL ENGINEERING; FERMENTATION; FLUIDIZED BEDS; NEURAL NETWORKS; PROBLEM SOLVING;

CRACKING UNITS; ENGINEERING DOMAIN; PROCESS MEASUREMENTS;

PATTERN RECOGNITION;

FAULT DIAGNOSIS;

EID: 10644232347     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2004.09.061     Document Type: Article

References (29)

1. Arnold, M.W., Darius, I.H., 1989. Alarm management in batch process control. ISA Transactions 28 (3), 33-40.
2. Baughman, D.R., Liu, Y.A., 1995. Neural Networks in Bioprocessing and Chemical Engineering, Academic Press, San Diego.
3. Bhagwat, A., Srinivasan, R., Krishnaswamy, P.R., 2003. Fault detection during process transitions: a model-based approach. Chemical Engineering Science 58 (2), 309-325.
4. Brezillon, P., 1999. Context in problem solving: a survey. The Knowledge Engineering Review 14 (1), 47-80.
5. Clancey, W.J., 1983. The epistemology of a rule-based expert system: a framework for explanation. Artificial Intelligence Journal 20 (3), 197-204.
6. Gollmer, K., Posten, C., 1995. Pattern recognition for phase detection in bioprocesses. Computer Applications in Biotechnology: a Postprint Volume From the Proceeding of the Sixth International Conference, Garmisch-Partenkirchen, Germany, pp. 41-47.
7. Hecht-Nielsen, R., 1988. Theory of back-propagation neural network. Proceedings of the International Neural Network Society Annual Meeting September, 1988.
8. Hecht-Nielsen, R., 1989. Theory of back-propagation neural network. Proceedings of the International Joint Conference on Neural Networks pp. 593-608.
9. Honeywell Inc., 2000. Guide to the Fluidized Catalytic Cracking Unit Standard Model.
10. Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward neural networks are universal approximators. Neural Networks 2 (5), 359-366.
11. Hunter, A., 2001. A default logic based framework for context-dependent reasoning with lexical knowledge. Journal of Intelligent Information Systems 16, 65-87.
12. Jensen, L.D., 1997. Need for dynamic configuration and other augmentation of distributed control systems for improved alarm management. IEE Colloquium (Digest), 8/1-8/4.
13. Kettebekov, S., Sharma, R., 1999. Toward multimodal interpretation in a natural speech/gesture interface. Proceedings of the 1999 International Conference on Information Intelligence and Systems 328-335.
14. Khalilian, M., Dhib, R., 2001. Online identification and control of a fluidized catalytic cracking unit. Proceedings of the IASTED International Conference on Modeling, Identification, and Control, pp. 448-452.
15. Maskery, H., Meads, J., 1992. Context: in the eyes of users and in computer systems. SIGCHI Bulletin 24 (2), 12-21.
16. Muthuswamy, K., 2001. A feature recogntioon approach to control and supervision of fermentation processes. M.Eng. Thesis, National University of Singapore.
17. Muthuswamy, K., Srinivasan, R., 2003. Phase-based supervisory control for fermentation process development. Journal of Process Control 13 (5), 367-382.
18. Pramit, S., Raghunathan, R., 2000. Multivariable gain-scheduled fuzzy logic control of a fluidized catalytic cracker unit. Computers & Chemical Engineering 24 (2-7), 1083-1089.
19. Rosen, C., Yuan, Z., 2001. Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering. Water Science and Technology 43, 147-156.
20. Seidenberg, M.S., 1982. Automatic access of the meanings of ambiguous words in context: some limitations of knowledge-based processing. Cognitive Psychology 489-537.
21. Srinivasan, R., 2002. Efficiently managing transitions in chemical plants. Technical Report, Laboratory for Intelligent Applications in Chemical Engineering, Department of Chemical and Environmental Engineering, National University of Singapore.
22. Srinivasan, R., Wang, C., Ho, W.K., Lim, K.W., 2003a. Dynamic principal component analysis based methodology for clustering process states in agile chemical plants. Industrial and Engineering Chemistry Research 43 (9), 2123-2139.
23. Srinivasan, R., Wang, C., Ho, W.K., Lim, K.W., 2003b. Neural network systems for multi-dimensional temporal pattern classification. Computers and Chemical Engineering, in press.
24. Sundarraman, A., Srinivasan, R., 2003. Monitoring transitions in chemical plants using enhanced trend analysis. Computers & Chemical Engineering 27 (10), 1455-1472.
25. P.D.Tumey, 1993a. Exploiting context when learning to classify. Proceedings of the European Conference on Machine Learning, ECML-93, pp. 402-407.
26. Turney, P.D., 1993b. Robust classification with context-sensitive features. In: Industrial and Engineering Application of Artificial Intelligence and Expert systems, IEA/AIE-93.Gordon and Breach, Edinburgh, Scotland, pp. 268-276.
27. Turney, P., Halasz, M., 1993. Contextual normalization applied to aircraft gas turbine engine diagnosis. Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies 3 (2), 109-129.
28. Wang, C., 2003. Pattern recognition approaches to state identification in chemical plant. Ph.D. Thesis, National University of Singapore.
29. Wang, C., Srinivasan, R., Ho, W.K., Lim, K.W., 2000. Dynamic alarm configuration in chemical processes through process mode identification. The Proceedings of ASCC'2000, Shanghai, p. 1.