On-Line Nonlinear Dynamic Data Reconciliation Using Extended Kalman Filtering: Application to a Distillation Column and a CSTR

Document Type : Research Article

Authors

1 Department of Chemical Engineering, Faculty of Chemistry, University of Tabriz, Tabriz, I.R. IRAN

2 Department of Chemical Engineering, Isfahan University of Technology, 84156-83111 Isfahan, I.R. IRAN

3 Department of Chemical Engineering and Petroleum, Sharif University of Technology, Tehran, I.R. IRAN

Abstract

Extended Kalman Filtering (EKF) is a nonlinear dynamic data reconciliation (NDDR) method. One of its main advantages is its suitability for on-line applications. This paper presents an on-line NDDR method using EKF. It is implemented for two case studies, temperature measurements of a distillation column and concentration measurements of a CSTR. In each time step, random numbers with zero mean and specified variance were added to simulated results by a random number generator. The generated data are transferred on-line to a developed data reconciliation software. The software performs NDDR on received data using EKF method. Comparison of data reconciliation results with simulated measurements and true values demonstrates a high reduction in measurement errors, while benefits high speed data reconciliation process.

Keywords

Main Subjects


[1] Almasy, G. A., Principles of Dynamic Balancing, AIChE Journal, 36, p. 1321 (1991).
[2] Liebman, M. J., Edgar, T. F. and Lasdon, L. S., Efficient Data Reconciliation and Estimation for Dynamic Processes using Nonlinear Programming Techniques, Computers Chem. Engng., 16 (10/11), p. 963 (1992).
[3] Bai, S., Thibault, J. and McLean, D.D., Dynamic Data Reconciliation: Alternative to Kalman Filter, Journal of Process Control, 16 (9), p. 938 (2006).
[4] Abu-el-zeet, Z. H., Becerra, V.M., Roberts, P.D., Combined Bias and Outlier Identification in Dynamic Data Reconciliation, Computers Chem. Engng., 26, p. 921 (2002).
[5] Barbosa Jr, V. P., Wolf, M. R. M., Maciel Fo, R., Development of Data Reconciliation for Dynamic Nonlinear System: Application to the Polymerization Reactor, Computers Chem. Engng., 24, p. 501 (2000).
[6] McBrayer, K. F., Soderstorm, T. A., Edgar, T. F. and Young, R. E., The Application of Nonlinear Dynamic Data Reconciliation to Plant Data, Computers Chem. Engng., 22 (12), p. 1907 (1998).
[7] Meert, K., A Real-Time Recurrent Learning Network Structure for Data Reconciliation, Artificial Intelligence in Engineering, 12, p. 213 (1998).
[8] Chen, J., Romagnoli, J. A., A Strategy for Simul-taneous Dynamic Data Reconciliation and Outlier Detection, Computers Chem. Engng., 22 (4/5), p. 559 (1998).
[9] Karjala, T. W., Himmelblau, D. M., Dynamic Rectification of Data via Recurrent Neural Network and the Extended Kalman Filter, AIChE Journal, 42, p. 2225 (1996).
[10] Islam, K. A., Weiss, G. H. and Romagnoli, J. A., Nonlinear Data Reconciliation for an Industrial Pyrolysis Reactor, 4th European Symposium on Computer Aided Process Engineering, p. 218 (1994).
[11] Chiari, M., Bussani, G., Grottoli, M. G. and Pierucci, S., On-Line Data Reconciliation and Optimization: Refinery Applications, 7th European Symposium on Computer Aided Process Engineering, p. 1185 (1997).
[12] Grewal,  M. S.  and  Andrews,  A. P., “Kalman Filtering: Theory and Practice Using MATLAB”, Second Edition, John Wiley and Sons Inc., (2001).
[13] Narasimhan, S. and Jordache, C., “Data Recon-ciliation and Gross Error Detection: An Intelligent Use of Process Data”, Gulf Professional Publishing, Houston, Texas, Nov. (1999).
[14] Mehrabni, A. Z., “Non-linear Parameter Estimation of Distillation Column”, M.Sc. Thesis, University of Wales, Department of Chemical Engineering, Nov. (1986).
[15] Farzi, A., Mehrabani, A.Z. and Bozorgmehry, R. B., Data Reconciliation: Development of an Object-Oriented Software Tool, Korean Journal of Chemical Engineering, 25 (5), p. 955 (2008).
[16] Jang, S. S., Joseph, B. and Mukai, H., Comparison of Two Approaches to On-Line Parameter and State Estimation of Nonlinear Systems, Ind. Engng. Chem. Process. Des. Dev., 25, p. 809 (1986).