Reinforcement Learning for PSE
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The chemical industry requires efficient control systems to operate at the border of process constraints while optimizing for profit, safety, and sustainability. Reinforcement Learning (RL) is a control philosophy that aims to address the complex control problems present in chemical systems. RL utilises plant data to improve its control performance and has several advantages over other control strategies such as its offline inference time and flexibility to adapt to changing plant conditions. However, RL is an active area of research to develop and implement algorithms suitable for industrial use global optimum due to its inherent robustness.
Max Bloor
Deep Reinforcement Learning for Process Control and Scheduling
Max is a PhD student with a research interest in applying and developing deep reinforcement learning algorithms for chemical process control. Before starting his PhD, he completed his undergraduate degree at the University of Edinburgh and his Masters degree at Imperial College London. Also, he has worked in industry as a process engineering consultant.