USE OF TENDENCY MODELS AND THEIR UNCERTAINTY IN THE DESIGN OF STATE ESTIMATORS FOR BATCH REACTORS

Tendency models have been successful in the modeling and optimization of batch reactor processes where a detailed understanding based on fun damental principles and detailed kinetic studies is not available. The evolutionary nature of the Tendency modeling algorithm has proven use ful in updating the process model between batches, as new process data or insight become available. But optimization is not the only task th at can be undertaken with a Tendency model. In this work, the use of T endency models in the design of state estimators to estimate reactor c oncentrations is investigated. The primary goal is to use the knowledg e of the uncertainty in the Tendency model (which, by its nature, is a n approximate model) to tune an extended Kalman filter. Two examples a re presented to illustrate that even though Tendency models can featur e a significant amount of uncertainty, they can be used successfully i n state estimators. (C) 1998 Elsevier Science S.A. All rights reserved .