1Department of Chemical Engineering, Faculty of Chemistry, University of Tabriz, Tabriz, I.R. IRAN
2Department of Chemical Engineering, Isfahan University of Technology, 84156-83111 Isfahan, I.R. IRAN
3Department of Chemical Engineering and Petroleum, Sharif University of Technology, Tehran, I.R. IRAN
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.
 Almasy, G. A., Principles of Dynamic Balancing, AIChE Journal, 36, p. 1321 (1991).
 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).
 Bai, S., Thibault, J. and McLean, D.D., Dynamic Data Reconciliation: Alternative to Kalman Filter, Journal of Process Control, 16 (9), p. 938 (2006).
 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).
 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).
 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).
 Meert, K., A Real-Time Recurrent Learning Network Structure for Data Reconciliation, Artificial Intelligence in Engineering, 12, p. 213 (1998).
 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).
 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).
 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).
 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).
 Grewal, M. S. and Andrews, A. P., “Kalman Filtering: Theory and Practice Using MATLAB”, Second Edition, John Wiley and Sons Inc., (2001).
 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).
 Mehrabni, A. Z., “Non-linear Parameter Estimation of Distillation Column”, M.Sc. Thesis, University of Wales, Department of Chemical Engineering, Nov. (1986).
 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).
 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).