Iterative Learning Modelling and Control of Batch Fermentation Processes

In this paper a novel method for batch-to-batch modelling and optimization, Iterative Learning Partial Least Squares Optimization (IL-PLSO) is proposed. This method uses a recursive technique to update a multi-way PLS model so that it is able to track the varying dynamics from one batch to the next. Based on the model obtained at the end of one batch, a Quadratic Programme (QP) is used to identify the required trajectory for the primary manipulated variable in the subsequent batch to ensure that the target end-point quality is met. This target quality can be gradually increased to optimise the productivity, or yield of the process. The capabilities of the proposed IL-PLSO method are illustrated through its application to optimise the end-point product quality of a benchmark simulation of a fermentation process. In this application, the proposed algorithm is able to identify an optimal trajectory for the manipulated variable after approximately 10 batches. The results are shown to compare very favourably with alternative approaches.