Simultaneous Process Design and Control Optimization using Reinforcement Learning

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Simultaneous Process Design and Control Optimization using Reinforcement Learning

Petsagkourakis, P; Sachio, S; del Rio Chanona, A; (2021) Simultaneous Process Design and Control Optimization using Reinforcement Learning. In: Workshop: Machine Learning for Engineering Modeling, Simulation and Design. NeurIPS 2020 Green open access

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Abstract

With the ever-increasing numbers in population and quality in healthcare, it is inevitable for the demand of energy and natural resources to rise. Therefore, it is important to design highly efficient and sustainable chemical processes in the pursuit of sustainability. The performance of a chemical plant is highly affected by its design and control. A design cannot be evaluated without its controls and vice versa. To optimally address design and control simultaneously, one must formulate a bi-level mixed-integer nonlinear program with a dynamic optimization problem as the inner problem; this, is intractable. However, by computing an optimal policy using reinforcement learning, a controller with close-form expression can be found and embedded into the mathematical program. In this work, an approach using a policy gradient method along with mathematical programming to solve the problem simultaneously is proposed. The approach was tested in two case studies and the performance of the controller was evaluated. It was shown that the proposed approach outperforms current state-of-the-art control strategies. This opens a whole new range of possibilities to address the simultaneous design and control of engineering systems.

Type:	Proceedings paper
Title:	Simultaneous Process Design and Control Optimization using Reinforcement Learning
Event:	Workshop: Machine Learning for Engineering Modeling, Simulation and Design
Open access status:	An open access version is available from UCL Discovery
Publisher version:	https://ml4eng.github.io/camera_readys/17.pdf
Language:	English
Additional information:	This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering
URI:	https://discovery.ucl.ac.uk/id/eprint/10119663