Use of a Novel Autoassociative Neural Network for Nonlinear Steady-State Data Reconciliation

AIChE Journal

Volumn 43, Issue 7, 1997, Pages 1785-1796

  Du, Yang Guang ^a   Hodouin, Daniel ^a   Thibault, Jules ^a  

^a UNIVERSITÉ LAVAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; FLOTATION; LEAST SQUARES APPROXIMATIONS; MANUFACTURING DATA PROCESSING; MEASUREMENT ERRORS; NONLINEAR CONTROL SYSTEMS; STATE ESTIMATION;

AUTOASSOCIATIVE NEURAL NETWORKS; NONLINEAR STEADY STATE DATA RECONCILIATION;

STATISTICAL PROCESS CONTROL;

EID: 0031185573     PISSN: 00011541     EISSN: None     Source Type: Journal    
DOI: 10.1002/aic.690430714     Document Type: Article

References (41)

1. Albuquerque, J. S., and L. T. Biegler, "Data Reconciliation and Gross-Error Detection for Dynamic Systems," AIChE J., 42(10), 2841 (1996).
2. Aynsley, M., A. Hofland, A. J. Morris, G. A. Montague and C. Di Massimo, "Artificial Intelligence and the Supervision of Bioprocesses (Real-Time Knowledge-Based Systems and Neural Networks)," Adv. Biochem. Eng. Biotechnol., 48, 1 (1993).
3. Chen, J., A. Bandoni, and J. A. Romagnoli, "Robust Statistical Process Monitoring," Comput. Chem. Eng., 20, S497 (1996).
4. Crowe, C. M., Y. A. Garcia Campos, and A. Hrymak, "Reconciliation of Process Flow Rates by Matrix Projection: I. The Linear Case," AIChE J., 29(6), 881 (1983).
5. Crowe, C. M., "Reconciliation of Process Flow Rates by Matrix Projection: II. The Nonlinear Case," AIChE J., 32(4), 616 (1986).
6. Crowe, C. M., "Observability and Redundancy of Process Data for Steady-State Reconciliation," Chem. Eng. Sci., 44(12), 2909 (1989).
7. Crowe, C. M., "Data Reconciliation - Progress and Challenges," J. Proc. Control, 6(2/3), 89 (1996).
8. Dong, D., and T. J. McAvoy, "Nonlinear Principal Component Analysis-based on Principal Curves and Neural Networks," Comput. Chem, Eng., 20(1), 65 (1995).
9. Dunia, R., S. J. Qin, T. F. Edgar, and T. J. McAvoy, "Use of Principal Component Analysis for Sensor Fault Identification," Comput. Chem. Eng., 20, S539 (1996).
10. Gertler, J., "A Constrained Minimum Variance Input-Output Estimator for Linear Dynamic Systems," Automatica, 15(3), 353 (1979).
11. Hodouin, D., C. Bazin, and S. Makni, "Dynamic Material Balance Algorithm; Application to Industrial Flotation Circuits," Proc. SME/AIME Meeting, Phoenix, AZ, p. 96 (1996).
12. Hodouin, D., C. Bazin, and S. Makni, "On-Line Reconciliation of Mineral Processing Data," Proc. AIME/SME Symp. on Emerging Computer Techniques for the Mineral Industry, Reno, NV (1993).
13. Hodouin, D., T. Gelpe, and M. D. Everell, "Sensitivity Analysis of Material Balance Calculations: An Application to a Cement Clinker Grinding Process," Powder Technol., 32, 139 (1982).
14. Hodouin, D., and M. D. Everell, "A Hierarchical Procedure for Adjustment and Material Balancing of Mineral Process Data," Int. J. Miner. Process., 7(2), 91 (1980).
15. Hoskins, J. C., K. M. Kaliyur, and D. M. Himmelblau, "Fault Diagnosis in Complex Chemical Plants Using Artificial Neural Networks," AIChE J., 37(1), 137 (1991).
16. Karjala, T. W., and D. M. Himmelblau, "Dynamic Data Reconciliation by Recurrent Neural Networks vs. Traditional Methods," AIChE J., 4(11), 1865 (1994).
17. Karjala, T. W., and D. M. Himmelblau, "Dynamic Rectification of Data via Recurrent Neural Nets and Extended Kaiman Filter," AJChE J., 42(8), 2225 (1996).
18. Kramer, M. A., "Nonlinear Principal Component Analysis Using Autoassociative Neural Networks," AIChE J., 37(2), 233 (1991).
19. Kramer, M. A., "Autoassociative Neural Networks," Comput. Chem. Eng., 16(4), 313 (1992).
20. Liebman, M. J., T. F. Edgar, and L. S. Lasdon, "Efficient Data Reconciliation and Estimation for Dynamic Processes Using Nonlinear Programming Techniques," Comput. Chem. Eng., 16(10/11), 963 (1992).
21. Lynch, A. J., Mineral Crushing and Grinding Circuits: Their Simulation, Optimization and Control, Elsevier, New York, p. 137 (1977).
22. MacGregor, J. F., "Statistical Process Control of Multivariable Processes," Control Eng. Prac., 3(3), 403 (1995).
23. Mah, R. S. H., Chemical Process Structures and Information Flows, Batterworths, Boston (1990).
24. Makni, S., D. Hodouin, and C. Bazin, "On-Line Reconciliation by Minimization of a Weighted Sum of Squared Residuals and Node Imbalances," Proc. IMPC, Publ. SME/AIME, San Francisco (1995).
25. Makni, S., and D. Hodouin, "Recursive BILMAT Algorithm: An On-Line Extension of Data Reconciliation Techniques for Steady-State Bilinear Material Balance," Min. Eng., 7(9), 1179 (1994).
26. McBrayer, K. F., and T. F. Edgar, "Detection and Estimation of Measurement Bias in Dynamic Data Reconciliation," Advanced Control of Chemical Processes (ADCHEM'94), IFAC, Pergamon Press, London (1994).
27. Meyer, M., B. Koehret, and M. Enjalbert, "Data Reconciliation on Multicomponent Network Process," Comput. Chem. Eng., 17(8), 807 (1993).
28. Nomikos, P., and J. F. MacGregor, "Monitoring Batch Processes Using Multiway Principal Component Analysis," AIChE J., 40(8), 1361 (1994).
29. Piovoso, M. J., and K. A. Kosanovich, "Application of Multivariate Statistical Methods to Process Monitoring and Controller Design," Int. J. Control, 59(3), 743 (1994).
30. Rollins, D. K., and J. F. Davis, "Unbiased Estimation of Gross Errors in Process Measurements," AIChE J., 38(4), 563 (1992).
31. Romagnoli, J. A., and G. Stephanopoulos, "Rectification of Process Measurement Data in the Presence of Gross Errors," Chem. Eng. Sci., 36(11), 1849 (1981).
32. Rumelhart, D., G. Hinton, and R. Williams, "Learning Internal Representations by Error Propagation," Parallel Distributed Processing, MTN Press, Cambridge, MA (1986).
33. Schraa, O. J., and C. M. Crowe, "The Numerical Solution of Bilinear Data Reconciliation Problems Using Unconstrained Optimization Methods," Comput. Chem. Eng., 20, S727 (1996).
34. Smith, H. W., and N. Ichiyen, "Computer Adjustment of Metallurgical Balances," Can. Inst. Mining Met. Bull., 66, 97 (1973).
35. Tan, S., and M. L. Mavrovouniotis, "Reducing Data Dimensionality through Optimizing Neural Network Inputs," AIChE J., 41(6), 1471 (1995).
36. Terry, P. A., and D. M. Himmelblau, "Data Rectification and Gross Error Detection in a Steady-State Process via Artificial Neural Networks," Ind. Eng. Chem. Res., 32, 3020 (1993).
37. Tjoa, I. B., and L. T. Biegler, "Simultaneous Strategies for Data Reconciliation and Gross Error Detection of Nonlinear Systems," Comput. Chem. Eng., 15(10), 679 (1991).
38. Tong, H., and C. M. Crowe, "Detecting Persistent Gross Errors by Sequential Analysis of Principal Components," Comput. Chem. Eng., 20, S733 (1996).
39. Wiegel, R. L., "Advances in Mineral Processing Material Balance," Can. Metall. Q., 11(2), 413 (1972).
40. Wise, B. M., and N. L. Ricker, "Recent Advances in Multivariable Statistical Process Control: Improving Robustness and Sensitivity," Proc. IFAC, ADCHEM Symp., 125 (1991).
41. Zasadzinski, M., "Contribution à l'estimation de l'état des systèmes singuliers. Application à la validation des données des systèmes dynamiques linéaires," PhD Thesis, Univ. de Nancy, France (1990).