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 fundamental principles and detailed kinetic studies is not available. The evolutionary nature of the Tendency modeling algorithm has proven useful in updating the process model between batches, as new process data or insight become available. But optimization is not the only task that can be undertaken with a Tendency model. In this work, the use of Tendency models in the design of state estimators to estimate reactor concentrations is investigated. The primary goal is to use the knowledge of the uncertainty in the Tendency model (which, by its nature, is an approximate model) to tune an extended Kalman filter. Two examples are presented to illustrate that even though Tendency models can feature a significant amount of uncertainty, they can be used successfully in state estimators.