Optimum Operation and Control of a Batch Chemical Process using Reinforcement Learning (University of Khartoum Engineering Journal, 2022)
A Reinforcement learning (RL) approach is presented as a new automatic learning approach to theproblem of optimal operation and control of batch chemical processes (e.g. batch reactors and batch distillationcolumns). The approach is particularly suited to batch process optimization problems, especially through notassuming prior detailed process knowledge or availability of a process model. The particular suitability of RL as aframework for optimising batch process operation has been recognised already (Martinez et al. (1998a,b)). In theimplementation of RL, use is made of how the plant responds to control actions aggregated in generalised‘predictive’ models, each linking adjacent intra-batch decision steps. The relative worth of a control action at adecision step is aggregated in the ‘value function’. Both the predictive models and the value function are learnedfrom the accumulating measurement data, batch-to-batch, starting from a small initial set of test batches. Themethodology is exemplified using two simple batch process case studies which were used to test the MATLABcomputer coding of the RL algorithm. In addition, issues regarding the structure of the initial training data set andthe embedded Neural Network have been investigated.