Simultaneous robust data reconciliation and gross error detection through particle swarm optimization for an industrial polypropylene reactor

Chemical Engineering Science

Volumn 65, Issue 17, 2010, Pages 4943-4954

  Martinez Prata, Diego ^a   Schwaab, Marcio ^a   Luis Lima, Enrique ^a   Carlos Pinto, José ^a  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

Nonlinear dynamic data reconciliation; Parameter estimation; Particle swarm optimization; Real industrial data; Robust estimator

Indexed keywords

DIFFERENTIAL EQUATIONS; ERRORS; ITERATIVE METHODS; NONLINEAR ANALYSIS; NUMERICAL METHODS; PARAMETER ESTIMATION; POLYPROPYLENES;

ALGEBRAIC DIFFERENTIAL EQUATION; DYNAMIC OPTIMIZATION PROBLEM (DOP); INDUSTRIAL DATUM; NONLINEAR DYNAMIC DATA RECONCILIATION; PARTICLE SWARM OPTIMIZATION METHOD (PSO); PHENOMENOLOGICAL MODELING; PROPYLENE POLYMERIZATION; ROBUST ESTIMATORS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 77954622444     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2010.05.017     Document Type: Article

References (64)

1. Abul-el-Zeet Z.H., Becerra V.M., Roberts P.D. Combined bias and outlier identification in dynamic data reconciliation. Computers and Chemical Engineering 2002, 26:921-935.
2. Albuquerque J.S., Biegler L.T. Data reconciliation and gross-error detection for dynamic systems. AIChE Journal 1996, 42:2841-2856.
3. Alhaj-Dibo M., Maquin D., Ragot J. Data reconciliation: a robust approach using a contaminated distribution. Control Engineering Practice 2008, 16:159-170.
4. Arora N., Biegler L.T. Redescending estimators for data reconciliation and parameter estimation. Computers and Chemical Engineering 2001, 25:1585-1599.
5. Baselga S. Global optimization solution of robust estimation. Journal of Surveying Engineering 2007, 133:123-128.
6. Basu, A., Paliwal, K. K., 1989. Robust M-estimates and generalized M-estimates for autoregressive parameter estimation. In: Proceedings of TENCON 89, Fourth IEEE Region 10 International Conference, Bombay.
7. Bourouis M., Pibouleau L., Floquet P., Domenech S., Al-Gobaisi D.M.K. Simulation and data validation in multistage flash desalination plants. Desalination 1998, 115:1-14.
8. Chen J., Bandoni A., Romagnoli J.A. Robust estimation of measurement error variance/covariance from process sampling data. Computers and Chemical Engineering 1997, 21:593-600.
9. Chen J., Romagnoli J.A. A strategy for simultaneous dynamic data reconciliation and outlier detection. Computers and Chemical Engineering 1998, 22:559-562.
10. Chen X., Pike R.W., Hertwing T.A., Hopper J.R. Optimal implementation of on-line optimization. Computers and Chemical Engineering 1998, 22:S435-S442.
11. Crowder M.J. Maximum likelihood estimation with dependent observations. Journal of the Royal Statistical Society Series B 1976, 38:45-53.
12. Crowe C.M., Garcia Campos Y.A., Hrymak A. Reconciliation of process flow rates by matrix projection. Part I: Linear case. AIChE Journal 1983, 29:881-888.
13. Crowe C.M. Data reconciliation-progress and challenges. Journal Process Control 1996, 6:89-98.
14. Crowe C.M. Formulation of linear data reconciliation using information theory. Chemical Engineering Science 1996, 51:3359-3366.
15. Crowe C.M. Test of maximum power for detection of gross errors in process constraints. AIChE Journal 1989, 35:563-572.
16. Embiruçu M., Prata D.M., Lima E.L., Pinto J.C. Continuous soluble Ziegler-Natta ethylene polymerizations in reactor trains II. Estimation of kinetic parameters from industrial data. Macromolecular Reaction Engineering 2008, 2:142-160.
17. Faber R., Arellano-Garcia H., Li P., Wozny G. An optimization framework for parameter estimation of large-scale systems. Chemical Engineering and Processing 2007, 46:1085-1095.
18. Faber R., Li B., Li P., Wozny G. Data reconciliation for real-time optimization of an industrial coke-oven-gas purification process. Simulation Modelling Practice and Theory 2006, 14:1121-1134.
19. Field C., Wiens D. One step M-estimators in the linear model with dependent errors. The Canadian Journal of Statistics 1994, 22:219-231.
20. Field C., Zhou J. Confidence intervals based on robust regression. Journal of Statistical Planning and Inference 2003, 115:425-439.
21. Hampel F.R. The influence curve and its role in estimation. Journal of the American Statistical Association 1974, 69:383-393.
22. Hampel F.R., Ronchetti E.M., Rousseeuw P.J., Stahel W.A. Robust Statistics: The Approach Based on Influence Functions 1986, Wiley, New York.
23. Huber P.J. Robust Statistics 1981, John Wiley, New York.
24. Jiang Q., Sanchez M., Bagajewicz M. On the performance of principal component analysis in multiple gross error identification. Industrial and Engineering Chemistry Research 1999, 38:2005-2012.
25. Johnson L.P.M., Kramer M.A. Maximum likelihood data rectification: steady-state systems. AIChE Journal 1995, 41:2415-2416.
26. Kennedy, J., Eberhart, R., 1995. Particle swarm optimization, In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942-1948.
27. Li B., Chen B., Duan D., Kong M. Correction coefficient method for gross error detection based on temporal redundancy. Qinghua Daxue Xuebao, Ziran Kexueban 2000, 40:65-69.
28. Lid T., Skogestad S. Data reconciliation and optimal operation of a catalytic naphtha reformer. Journal of Process Control 2008, 18:320-331.
29. Lid T., Skogestad S. Scaled steady state models for effective on-line applications. Computers and Chemical Engineering 2008, 32:990-999.
30. Mah R.S.H., Stanley G.M., Downing D.M. Reconciliation and ratification of process flow and inventory data. Industrial Engineering and Chemical Process Design Development 1976, 15:175-183.
31. Mah R.S.H., Tamhane A.C. Detection of gross errors in process data. AIChE Journal 1982, 28:828-830.
32. Maronna R., Arcas J. Data reconciliation and gross error diagnosis based on regression. Computers and Chemical Engineering 2009, 33:65-71.
33. Maronna R., Martin D., Yohai V. Robust Statistics-Theory and Methods 2006, John Wiley, New York.
34. Mattos Neto A.G., Pinto J.C. Steady-state modeling of slurry and bulk propylene polymerization. Chemical Engineering Science 2001, 56:4043-4057.
35. McBrayer K.F., Edgar T.F. Bias detection and estimation in dynamic data reconciliation. Journal of Process Control 1995, 5:285-289.
36. Narasimhan S., Jordache C. Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data 2000, Gulf Professional Publishing, Houston-TX.
37. Narasimhan S., Mah R.S.H. Generalized likelihood ratio method for gross error detection. AIChE Journal 1987, 33:1514-1521.
38. Özyurt D.B., Pike R.W. Theory and practice of simultaneous data reconciliation and gross error detection for chemical process. Computers and Chemical Engineering 2004, 28:381-402.
39. Peña D., Prieto F.J. Multivariate outlier detection and robust covariance matrix estimation. Technometrics 2001, 43:286-310.
40. Petzold, L.R., 1989. DASSL code, version 1989, Computing and Mathematics Research Division, Lawrence Livermore National Laboratory, L316, PO Box 808, Livermore, CA 94559.
41. Portnoy S.L. Robust estimation in dependent situation. The Annals of Statistics 1977, 5:22-43.
42. Prata D.M., Lima E.L., Pinto J.C. Simultaneous data reconciliation and parameter estimation in bulk polypropylene polymerizations in real time. Macromolecular Symposia 2006, 243:91-103.
43. Prata D.M., Lima E.L., Pinto J.C. In-line monitoring of bulk polypropylene reactors based on data reconciliation procedures. Macromolecular Symposia 2008, 71:26-37.
44. Prata D.M., Pinto J.C., Lima E.L. Comparative analysis of robust estimators on nonlinear dynamic data reconciliation. Computer-aided Chemical Engineering 2008, 25:501-506.
45. Prata D.M., Schwaab M., Lima E.L., Pinto J.C. Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: application for an industrial polypropylene reactor. Chemical Engineering Science 2009, 64:3953-3967.
46. Ragot J., Chadli M., Maquin D. Mass balance equilibration: a robust approach using contaminated distribution. AIChE Journal 2005, 51:1569-1575.
47. Reilly, P., Carpani, R., 1963. Application of statistical theory of adjustment to material balances. In: Proceedings of the 13th Canadian Chemical Engineering Conference, Montreal, Quebec.
48. Rey W.J.J. Introduction to Robust and Quasi-robust Statistical Methods 1983, Springer-Verlang, Berlin/New York.
49. Ripps D.L. Adjustment of experimental data. Chemical Engineering Progress Symposium Series 1965, 61:8-13.
50. Rollins D.K., Davis J.F. Unbiased estimation of gross error in process measurements. AIChE Journal 1992, 38:563-572.
51. Romagnoli J.A., Sánchez M.C. Data Processing and Reconciliation for Chemical Process Operations 2000, Academic Press, San Diego, CA.
52. Schladt M., Hu B. Soft sensor based on nonlinear steady-state data reconciliation in the process industry. Chemical Engineering and Processing 2007, 46:1107-1115.
53. Schwaab M., Biscaia E.C., Monteiro J.L., Pinto J.C. Nonlinear parameter estimation through particle swarm optimization. Chemical Engineering Science 2008, 63:1542-1552.
54. Tjoa I.B., Biegler L.T. Simultaneous strategy for data reconciliation and gross error detection of nonlinear systems. Computers and Chemical Engineering 1991, 15:679-690.
55. Tong H., Crowe C.M. Detection of gross errors in data reconciliation by principal component analysis. AIChE Journal 1995, 41:1712-1722.
56. Vacchani P., Rengaswamy R., Venkatasubramanian V. A framework for integrating diagnostic knowledge with nonlinear optimization for data reconciliation and parameter estimation in dynamic systems. Chemical Engineering Science 2001, 56:2133-2148.
57. Wang D.J., Romagnoli J.A. A framework for robust data reconciliation based on a generalized objective function. Industrial and Engineering Chemistry Research 2003, 42:3075-3084.
58. Wang F., Jia X.P., Zheng S.Q., Yue J.C. An improved MT-NT method for gross error detection and data reconciliation. Computers and Chemical Engineering 2004, 28:2189-2192.
59. Wongrat M., Srinophakun T., Srinophakun P. Modified genetic algorithm for nonlinear data reconciliation. Computers and Chemical Engineering 2005, 29:1059-1067.
60. Yang Y., Ten R., Jao L. A study of gross error detection and data reconciliation in process industries. Computers and Chemical Engineering 1995, 19:217-222.
61. Yang Y., Song L., Xu T. Robust estimator for correlated observations based on bifactor equivalent weights. Journal of Geodesy 2002, 76:353-358.
62. Zhang Z., Pike R.W., Herting T. Source reduction from chemical plants using on-line optimization. Waste Management 1995, 15:183-191.
63. Zhang P., Rong G., Wang Y. A new method of redundancy analysis in data reconciliation and its application. Computers and Chemical Engineering 2001, 25:941-949.
64. Zhou L., Su H., Chu J. A new method to solve robust data reconciliation in nonlinear process. Chinese Journal of Chemical Engineering 2006, 14:357-363.