Nonlinear State Estimation Techniques Applied to Polymer Processes

GALDEANO RUBÉN; ASTEASUAIN MARIANO; SANCHEZ MABEL

Los Cocos, Argentina

Simposio; V Simposio Argentino-Chileno de Polímeros; 2009

Universidad Nacional de Córdoba

The control and on line optimization of polymer properties in a continuous process is an extremely important and yet largely unsolved problem. In order to improve the overall process performance, Dynamic Real Time Optimization (DRTO) closed loops have begun to be implemented. DRTO loops comprise state estimation, dynamic optimization and control tasks. It is common practice in DRTO loops to use, together with the control algorithm, a state estimator in order to obtain reasonably good estimates of variables which are not measured, are measured off-line or are measured between large periods of time, but need to be controlled. In the case of a linear process in presence of white noise both on the process model used for the estimation and on the measurements, the estimation problem has an optimal solution, known as the Kalman Filter. This solution has been extended to the nonlinear case, and is called Extended Kalman Filter (EKF). In spite that the EKF is a suboptimal solution, it is probably the most commonly used estimator (Semino et al., 1996). However, under certain situations this technique may present problems, particularly in the case of high nonlinear systems such as polymer processes. In this work, we compare the performance of two different on-line state estimations: the URNDDR and PF on line state estimation techniques applied to polymer process. Trabajo para ser presentado en modalidad de poster.