State and Parameter Estimation for Biochemical Processes

In order to improve the productivity of a fermentation process, it is necessary to obtain reliable on-line data for all the important variables involved. Some of these are obtained by direct measurement and others are estimated. In the estimated variables, the model used to predict its behavior is usually known with a certain degree of precision. However, the parameters of the model may vary with time. In our work, we propose a general methodology to process all on-line real time information plus off-line data for estimation of system states and uncertain parameters. The addition of parameter estimation will increase the order of the computational matrix and memory requirements, so an optional multichannel scheme to decouple parameter from state estimation is presented. This methodology is applied to a fermentation process and the results of several experiments are presented.