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.