Fully Bayesian Reinforcement Learning for Control of Continuous Industrial Processes

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This exciting and innovative PhD, in partnership with NSG, relates to settings where a continuous manufacturing process is monitored and so controlled with a focus on both guaranteeing the quality of the product and minimising the costs of doing so, e.g. by minimising the amount of excess material used to guarantee that certain specifications of the product (e.g. thickness or defect rate) are met. The focus is on manufacture and treatment of glass. In such settings there is often a significant latency (i.e. minutes) between the control input changing and the response being observable. It is challenging to apply feedback control in these contexts, so existing Engineering solutions often make use of physical models for the process and employ predictive model-based control. While this does make it possible to produce desired variations in the product, the approach relies on the physical models for the process and the models for the sensors to be known. These models are well understood in general, but there are aspects where it is not possible to build accurate models that, for example, can infer how the fine detail of the thickness profile is impacted by variation in the power applied to heating elements at some historic time. Furthermore, the real-world changes over time (e.g. because valves become worn or because scheduled maintenance has not occurred recently) and while it is possible to develop work-arounds to adapt to these changes, these work-arounds can fail. Such failures can result in sudden and significant degradation in the quality of product. The fundamental challenge is then to develop a control strategy that fully capitalises on: offline historic data; parameterised models that capture the extensive but incomplete understanding of the processes and sensors' performance; offline simulated experience derived from those models; online data from sensors. Developing such a control strategy will require numerical Bayesian inference algorithms (e.g. Markov Chain Monte Carlo) to make inferences about the models in a way that exploits the historic data and domain experts' existing understanding. Borrowing from recent successful applications of Reinforcement Learning (RL) in other domains, RL will then be used to learn how best to apply the control given the inferred model. Such RL is computationally intensive and will therefore require use of High-Performance Computing resources.