Successive complementary model-based experimental designs for parameter estimation of fed-batch bioreactors

When a dynamic model is used for the description of (fed-)batch bioreactors, it is typical that the model parameters are highly correlated to each other. In this case, it is important to keep the parameter correlation as small as possible to obtain a reliable set of parameter estimates. In this study, we propose an anticorrelation parameter estimation scheme that can be best utilized when a number of different batch experiments are sequentially processed. The scheme iteratively performs parameter estimation and model-based design of experiment (MBDOE) at the beginning and between the batches. The important difference from the existing approaches is that the MBDOE objective is defined according to the system analysis performed a priori, so that each new batch supplements what is lacking from the previous batches combined, in terms of information. The use of the scheme is illustrated on a fed-batch bioreactor model.