USE OF AN AUTOASSOCIATIVE NEURAL NETWORK FOR DYNAMIC DATA RECONCILIATION

The technique of dynamic data reconciliation has been previously studied in the literature and shown to be an effective tool to better estimate the true values of process variables by using information from both measured values and process models. Real-time implementation of dynamic data reconciliation involves solving complex optimization problem, leading to large computation time. This paper presents a study on the use of a dynamic Autoassociative Neural Network (AANN) for dynamic data reconciliation. Once trained, the AANN can be directly used for online signal validation. Closed-loop performance of the AANN for both linear and nonlinear processes was evaluated using simulations of two storage tank processes. The AANN provided accurate estimates of measured values for the two processes studied in this investigation.