Optimal state and parameter identification. An application to batch fermentation

Abstract

Simultaneous state and parameter identification is a problem that has not been addressed very often nor successfully in the past. In this work I present a general algorithm for coupling state variable and model parameter identification. A specific system that requires the simultaneous identification of process states and model parameters is that of batch beer fermentation. Results show the advantages in coupling a sequential parameter identification algorithm with the Kalman filter state identification algorithm. Such a combined algorithm has the capability of accurately estimating the entire state of the process even when some model parameters are uncertain. This strategy of using the best deterministic process information coupled with sequential updating can be successfully applied to many chemical and biochemical processing problems including batch beer fermentation.