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

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