Batch-to-Batch Iterative Learning Control of a Fed-batch Fermentation Process Using Incrementally Updated Models

Abstract

Batch-to-batch iterative learning control of a fed-batch fermentation process using batchwise linearised models identified from process operation data is presented in this paper. Due to model-plant mismatches and the present of unknown disturbances, off-line calculated control policy may not be optimal when implemented to the real process. The repetitive nature of batch process allows information from the previous batches being used in modifying the control policy of the next batch in the framework of iterative learning control. In order to cope with nonlinear behaviour of batch fermentation processes, the model is lineariesed using the immediate previous batch as a reference batch and the model is updated from batch to batch. The control policy (feed rates) at different batch stages are generally correlated as the overall control policy is obtained to maximize the amount of product at the end of a batch. To address the colinearity issue of the control variable, principal component regression and partial least squares regression are used in estimating the linearised model parameters. Application results on a simulated industrial scale fed-batch fermentation process demonstrate that the proposed strategy is effective.

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