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Received August 12, 2007
Accepted March 5, 2008
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Data reconciliation: Development of an object-oriented software tool
Department of Chemical Engineering, Isfahan University of Technology, Isfahan, Iran, Postal Code: 84156-83111 1Department of Chemical Engineering and Petroleum, Sharif University of Technology, Tehran, Iran
Korean Journal of Chemical Engineering, September 2008, 25(5), 955-965(11), 10.1007/s11814-008-0155-6
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Abstract
Object-oriented modeling methodology is used for encapsulating different methods and attributes of data reconciliation (DR) in classes. Classes which are defined for DR, cover steady-state, dynamic, linear and nonlinear DR problems. Two main classes are Constraints and DR and defined for manipulating constraints and general DR problem. The remaining classes are derived from these two classes. A class namely DDRMethod is developed for encapsulating all common attributes and methods needed for any DDR method. Developed DR software and the method of performing dynamic DR are discussed in this paper. Two illustrative examples of Extended Kalman Filtering and artificial neural networks are used for DDR and two classes of DDRByKalman and NetDDRMethod developed by inheritance from DDRMethod class for these two methods. Performance of the proposed method is investigated by DDR of temperature measurements of a distillation column.
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References
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Crowe CM, Campos YAG, Hrymak A, AIChE J., 29, 881 (1983)
Crowe CM, AIChE J., 32, 616 (1986)
Liebman MJ, Edgar TF, Lasdon LS, Comput. Chem. Eng., 16(10/11), 963 (1992)
Kim IW, Park S, Edgar TF, Korean J. Chem. Eng., 13(2), 211 (1996)
Meert K, Artificial Intelligence in Engineering, 12, 213 (1998)
Chen J, Romagnoli JA, Comput. Chem. Eng., 22(4-5), 559 (1998)
Abu-el-zeet ZH, Becerra VM, Roberts PD, Comput. Chem. Eng., 26(6), 921 (2002)
Kelly JD, Comput. Chem. Eng., 28(12), 2837 (2004)
Bai SH, Thibault J, McLean DD, J. Process Control, 16(5), 485 (2006)
O’Docherty M, Object-oriented analysis and design, understanding system development with UML 2.0, John Wiley and Sons Ltd., Chichester, England (2005)
Narasimhan S, Jordache C, Data reconciliation and gross error detection: An intelligent use of process data, Gulf Professional Publishing, Houston, Texas, November (1999)
Grewal MS, Andrews AP, Kalman filtering: Theory and practice using MATLAB, second edition, John Wiley and Sons Inc. (2001)
Yoo A, Lee TC, Yang DR, Korean J. Chem. Eng., 21(4), 753 (2004)
Brown RG, Hwang PYC, Introduction to random signals and applied Kalman filtering, 3rd ed., John Wiley & Sons Inc., New York (1997)
Himmelblau DM, Korean J. Chem. Eng., 17(4), 373 (2000)
Hagan MT, De Jesus O, Schultz R, Training Recurrent Networks for Filtering and Control, Chapter 12 in Recurrent neural networks: Design and applications, L. Medsker and L. C. Jain, Eds., CRC Press, 311-340 (1999)
Narendra KS, Parthasarathy K, IEEE Trans. Neural Networks, 2, 252 (1991)
Narendra KS, Mukhopadhyay S, IEEE Trans. Neural Networks, 8, 475 (1997)
Mehrabani AZ, Non-linear parameter estimation of distillation column, M.Sc. Thesis, University of Wales, Department of Chemical Engineering, Nov. (1986)
Shin J, Lee M, Park S, Korean J. Chem. Eng., 15(6), 667 (1998)
More JJ, The Levenberg-Marquardt Algorithm: Implementation and Theory, Numerical analysis, ed. G. A. Watson, Lecture Notes in Mathematics 630, Springer Verlag, 105-116 (1977)
