Fully Bayesian Reinforcement Learning for Control of Continuous Industrial Processes

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.

This CDT's focus on using "Future Computing Systems" to move "Towards a Data-driven Future" resonates strongly with two themes of non-academic organisation. In both themes, albeit for slightly different reasons, commodity data science is insufficient and there is a hunger both for the future leaders that this CDT will produce and the high-performance solutions that the students will develop.

The first theme is associated with defence and security. In this context, operational performance is of paramount importance. Government organisations (e.g., Dstl, GCHQ and the NCA) will benefit from our graduates' ability to configure many-core hardware to maximise the ability to extract value from the available data. The CDT's projects and graduates will achieve societal impact by enabling these government organisations to better protect the world's population from threats posed by, for example, international terrorism and organised crime.

There is then a supply chain of industrial organisations that deliver to government organisations (both in the UK and overseas). These industrial organisations (e.g., Cubica, Denbridge Marine, FeatureSpace, Leonardo, MBDA, Ordnance Survey, QinetiQ, RiskAware, Sintela, THALES (Aveillant) and Vision4ce) operate in a globally competitive marketplace where operational performance is a key driver. The skilled graduates that this CDT will provide (and the projects that will comprise the students' PhDs) are critical to these organisations' ability to develop and deliver high-performance products and services. We therefore anticipate economic impact to result from this CDT.

The second theme is associated with high-value and high-volume manufacturing. In these contexts, profit margins are very sensitive to operational costs. For example, a change to the configuration of a production line for an aerosol manufactured by Unilever might "only" cut costs by 1p for each aerosol, but when multiplied by half a billion aerosols each year, the impact on profit can be significant. In this context, industry (e.g., Renishaw, Rolls Royce, Schlumberger, ShopDirect and Unilever) is therefore motivated to optimise operational costs by learning from historic data. This CDT's graduates (and their projects) will help these organisations to perform such data-driven optimisation and thereby enable the CDT to achieve further economic impact.

Other organisations (e.g., IBM) provide hardware, software and advice to those operating in these themes. The CDT's graduates will ensure these organisations can be globally competitive.

The specific organisations mentioned above are the CDT's current partners. These organisations have all agreed to co-fund studentships. That commitment indicates that, in the short term, they are likely to be the focus for the CDT's impact. However, other organisations are likely to benefit in the future. While two (Lockheed Martin and Arup) have articulated their support in letters that are attached to this proposal, we anticipate impact via a larger portfolio of organisations (e.g., via studentships but also via those organisations recruiting the CDT's graduates either immediately after the CDT or later in the students' careers). Those organisations are likely to include those inhabiting the two themes described above, but also others. For example, an entrepreneurial CDT student might identify a niche in another market sector where Distributed Algorithms can deliver substantial commercial or societal gains. Predicting where such niches might be is challenging, though it seems likely that sectors that are yet to fully embrace Data Science while also involving significant turn-over are those that will have the most to gain: we hypothesise that niches might be identified in health and actuarial science, for example.

As well as training the CDT students to be the leaders of tomorrow in Distributed Algorithms, we will also achieve impact by training the CDT's industrial supervisors.