An embedded fault detection, isolation and accommodation system in a model predictive controller for an industrial benchmark process

Computers and Chemical Engineering

Volumn 32, Issue 12, 2008, Pages 2966-2985

  Kettunen, Markus ^a   Zhang, Ping ^b   Jämsä Jounela, Sirkka Liisa ^a  

^a AALTO UNIVERSITY   (Finland)

^b UNIVERSITY OF DUISBURG ESSEN   (Germany)

Author keywords

FTC; MPC; PCA; PLS; Shell control problem; SMI

Indexed keywords

CHLORINE COMPOUNDS; CURVE FITTING; FAULT DETECTION; FAULT TOLERANCE; FAULT TOLERANT COMPUTER SYSTEMS; FINANCIAL DATA PROCESSING; IDENTIFICATION (CONTROL SYSTEMS); INFORMATION SCIENCE; LEAST SQUARES APPROXIMATIONS; MODEL PREDICTIVE CONTROL; PREDICTIVE CONTROL SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; QUALITY ASSURANCE; RELIABILITY;

CONTROL PROBLEMS; CONTROL STRATEGIES; FAULT DETECTION AND ISOLATION; FAULT-TOLERANT CONTROL; FTC; INDUSTRIAL BENCHMARK; INDUSTRIAL PROCESSING; MECHANISTIC MODELING; MODEL PREDICTIVE CONTROLLER; MODEL-BASED APPROACHES; MPC; PARTIAL-LEAST-SQUARES; PCA; PI CONTROLLERS; PLS; PRINCIPAL COMPONENT ANALYSIS (PCA); PROCESS INDUSTRIES; PROCESS MEASUREMENTS; SHELL CONTROL PROBLEM; SMI; SUBSPACE MODEL IDENTIFICATION;

PROCESS CONTROL;

EID: 52949104411     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2008.03.011     Document Type: Article

References (43)

1. Bemporad A., Morari M., and Ricker N.L. Matlab model predictive control toolbox-user's guide (2006). http://www.mathworks.com/access/helpdesk/help/pdf_doc/mpc/mpc_ug.pdf http://www.mathworks.com/access/helpdesk/help/pdf_doc/mpc/mpc_ug.pdf (May 16, 2006)
2. Bordeau, M. A. (2003). Squared model predictive controller performance on the Shell standard control problem. ISA conference, Houston, USA.
3. Bristol E.H. On a new measure for multivariable process control. IEEE Transactions on Automatic Control 11 (1966) 133
4. Dong D., and McAvoy T.J. Nonlinear principal component analysis based on principal curves and neural networks. Computers & Chemical Engineering 20 (1996) 65-78
5. Camacho E.F., and Bordons C. Model predictive control. 2nd ed. (2000), Springer-Verlag, London
6. Chen G., McAvoy T.J., and Piovoso M.J. A multivariate statistical controller for online quality improvement. Journal of Process Control 8 (1998) 139-149
7. Chiang L.H., Russell E.L., and Braatz R.D. Fault detection and diagnosis in industrial systems (2001), Springer-Verlag, London
8. Cutler C.R., and Ramaker B.L. Dynamic matrix control-a computer control algorithm. Joint automatic control conference. San Francisco, CA, USA (1980)
9. Dunia R., Qin S.J., Edgar T.F., and McAvoy T.J. Identification of faulty sensors using principal component analysis. AIChE Journal 42 10 (1996) 2797-2812
10. Frank P.M., Ding S.X., and Marcu T. Model-based fault diagnosis in technical processes. Transactions of the Institute for Measurement and Control 22 (2000) 57-101
11. Gerlach R.W., Kowalski B.R., and Wold H. Partial least squares modelling with latent variables. Analytica Chimica Acta 112 (1979) 417-421
12. Hyötyniemi, H. (2001). Multivariate regression-Techniques and tools (Vol. 125). Internal report. Helsinki: Helsinki University of Technology.
13. Jackson J.E. Control procedures for residuals associated with principal component analysis. Technometrics 21 (1979) 341-349
14. Jackson J.E. User's guide to principal components (1991), Wiley Interscience, New York
15. Komulainen T., Sourander M., and Jämsä-Jounela S.-L. An online application of dynamic PLS to a dearomatization process. Computers & Chemical Engineering 28 (2004) 2611-2619
16. Ku W., Storer R.H., and Georgakis C. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems 30 (1995) 179-196
17. Larimore W. Canonical variate analysis in identification, filtering and adaptive control. Proceedings of 29th IEEE conference on decision and control. Honolulu, Hawaii (1990) 596-604
18. Lee J.H., and Cooley B. Recent advances in model predictive control and other related areas. Proceedings of the chemical process control-V, CACHE, AIChE (1997) 201-216
19. Li W., Yue H.H., Valle-Cervantes S., and Qin S.J. Recursive PCA for adaptive process monitoring. Journal of Process Control 10 (2000) 471-486
20. Maciejowski J.M. Modelling and predictive control: Enabling technologies for reconfiguration. Annual Reviews in Control 23 (1999) 13-23
21. McAvoy T., Jämsä-Jounela S.-L., Patton R., Perrier M., Weber H., and Georgakis C. Milestone report for area 7 industrial applications. Control Engineering Practice 12 (2004) 113-119
22. Patwardhan S.C., Manuja S., Narasimhan S., and Shah S.L. From data to diagnosis and control using generalized orthonormal basis filters. Part II. Model predictive and fault tolerant control. Journal of Process Control 16 (2006) 157-175
23. Prakash J., Patwardhan S.C., and Narasimhan S.A. Supervisory approach to fault-tolerant control of linear multivariable systems. Industrial & Engineering Chemistry Research 41 (2002) 2270-2281
24. Pranatyasto T.N., and Qin S.J. Sensor validation and process fault diagnosis for FCC units under MPC feedback. Control Engineering Practice 9 (2001) 877-888
25. Prett D.M., and Morari M. Shell process control workshop (1987), Butterworth Publishers, Stoneham
26. Qin S.J. Recursive PLS algorithms for adaptive data modelling. Computers & Chemical Engineering 22 (1998) 503-514
27. Qin S.J., and Badgwell T.A. A survey of industrial model predictive control technology. Control Engineering Practice 11 (2003) 733-764
28. Rawlings J.B. Tutorial overview of model predictive control. IEEE Control Systems Magazine 20 (2000) 38-52
29. Richalet J., Rault A., Testud J.L., and Papon J. Model predictive heuristic control: Applications to industrial processes. Automatica 14 (1978) 413-428
30. Rivera D.E., Morari M., and Skogestad S. Internal model control: PID controller design. Industrial & Engineering Chemistry Process Design and Development 25 (1986) 252-265
31. Staroswiecki M., and Gehin A.-L. From control to supervision. Annual Reviews in Control 25 (2001) 1-11
32. Van Overschee P., and de Moor B. N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica 30 (1994) 75-93
33. Venkatasubramanian V., Rengaswamy R., and Kavuri S.N. A review of process fault detection and diagnosis. Part I. Quantitative model-based methods. Computers & Chemical Engineering 27 (2003) 293-311
34. Venkatasubramanian V., Rengaswamy R., and Kavuri S.N. A review of process fault detection and diagnosis. Part II. Qualitative models and search strategies. Computers & Chemical Engineering 27 (2003) 313-326
35. Venkatasubramanian V., Rengaswamy R., and Kavuri S.N. A review of process fault detection and diagnosis. Part III. Process history based methods. Computers & Chemical Engineering 27 (2003) 327-346
36. Venkateswarlu Ch., and Venkat Rao K. Dynamic recurrent radial basis function network model predictive control of unstable nonlinear processes. Chemical Engineering Science 60 (2005) 6718-6732
37. Verhaegen M. Identification of the deterministic part of MIMO state space models given in innovations form from input-output data. Automatica 30 (1994) 61-74
38. Vermasvuori M., Vatanski N., and Jämsä-Jounela S.-L. Data-based fault detection of the online analysers in a dearomatisation process. Proceedings of first NeCST workshop on networked control systems & fault tolerant control (2005) 219-224
39. Vlachos C., Williams D., and Gomm J.B. Solution to the Shell standard control problem using genetically tuned PID controllers. Control Engineering Practice 10 (2002) 151-163
40. Wang J., and Qin S.J. A new subspace identification approach based on principal component analysis. Journal of Process Control 12 (2002) 841-855
41. Wold H. Path models with latent variables: The NIPALS approach. Quantitative sociology: International perspective on mathematical and statistical model building (1975) pp. 307-357
42. Yoon S., and MacGregor J.F. Principal-component analysis of multiscale data for process monitoring and fault diagnosis. AIChE Journal 50 11 (2004) 2891-2903
43. Yu Z., Lee J.H., and Morari M. State-estimation-based model predictive control applied to shell control problem-A case study. Chemical Engineering Science 49 (1994) 285-301