Deep Reinforcement Learning for Smart Steel Processing

Sequential decision making describes a situation where the decision maker makes successive observations of a process before a final decision is made. With recent advanced in artificial intelligence, and especially "deep learning" (artificial neural networks), much progress has been made in developing computer agents that are able to make sequential decision on their own. In this PhD project we will develop advanced artificial intelligence algorithms for sequential decision making for applications in smart steel processing.

The PhD project is part of SUSTAIN, an EPSRC-funded programme co-created by the 5 major UK steel producers (Tata, Liberty, British Steel, Celsa, Sheffield Forgemasters) and the three principal Universities that have expertise in this area (Swansea, Warwick and Sheffield) to provide academic leadership in the field of steel innovation. Although the steel industry has generated "big data" for over 30 years, the production benefits have been limited so far. In this PhD project, we will develop novel data-driven techniques that leverage the latest advances in data science and machine learning. Ultimately, we will deliver an AI system for Smart Steel Processing able to automate and optimise certain processes that still rely heavily on manual intervention. We will exploit existing historical data repositories made available by our industrial collaboration and the availability of next-generation sensors that are now replacing traditional sampling methods in extreme environments. We will also develop a "digital twin", a simulation-based environment to help us test and develop novel reinforcement learning algorithms.