@article { author = {Farzi, Ali and Mehrabani-Zeinabad, Arjomand and Bozorgmehry Boozarjomehry, Ramin}, title = {On-Line Nonlinear Dynamic Data Reconciliation Using Extended Kalman Filtering: Application to a Distillation Column and a CSTR}, journal = {Iranian Journal of Chemistry and Chemical Engineering}, volume = {28}, number = {3}, pages = {1-14}, year = {2009}, publisher = {Iranian Institute of Research and Development in Chemical Industries (IRDCI)-ACECR}, issn = {1021-9986}, eissn = {}, doi = {10.30492/ijcce.2009.6841}, 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 = {Data reconciliation,Nonlinear dynamic data reconciliation,Extended kalman filtering,Distillation column,CSTR,Object-oriented programming}, url = {https://ijcce.ac.ir/article_6841.html}, eprint = {https://ijcce.ac.ir/article_6841_09e918c087540f60b39086839eb026a4.pdf} }