State Estimation and Parameter Identification in a Fed-Batch Penicillin Production Process

Abstract

This work presents an application of a recursive estimator of states and parameters in a fed-batch penicillin production process based on the use of the extended Kalman filter. The estimated state variables were the cell, substrate, product and dissolved oxygen concentrations, the fermenter volume and the oxygen transfer coefficient. A simplified model of this process was used for the filter, and the actual values for product amount and concentration of dissolved oxygen with independent random Gaussian white noise were obtained using a deterministic and nonstructured mathematical model. The influence of the filter parameters, initial deviations and presence of noise on the observed variables was analyzed. In addition, estimator performance was verified when the parameters and the structure of the process model were changed. The extended Kalman filter implemented was found to be suitable to predict the states of the system and the model parameters. Therefore, it can be used for optimization and control purposes in a fermentative process which requires some state variables that are measured with a long delay time or unmeasured parameters