Recursive Bayesian Filtering for States Estimation: An Application Case in Biotechnological Processes

Abstract — In this work a state estimator for a continuous bioprocess is presented. To reach this aim a nonlinear filtering technique, based on the recursive application of the Bayes rule and Monte Carlo techniques, is used. To the best of author’s knowledge, not many applications in the biotechnological area applying such techniques have been reported. Generally, a bioprocess has strong nonlinear and non Gaussian characteristics and so this methodology becomes more attractive. Specifically, the recursive Bayesian Filters SIR (Sampling Importance Resampling) are used, including different kinds of resampling; also the uncertainties of states and measurements are modelled. The estimator behaviour and performance are illustrated for the continuous alcoholic fermentation process of Zymomonas mobilis. There is an industrial interest in the use of Zymomonas due to its capability to produce ethanol; and for the fuel ethanol industry to expand; Zymomonas mobilis has attracted the attention as a promising bacterium regarding the ethanol production improvement.