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Reinforcement learning for batch bioprocess optimization

Petsagkourakis, P; Sandoval, IO; Bradford, E; Zhang, D; del Rio-Chanona, EA; (2020) Reinforcement learning for batch bioprocess optimization. Computers & Chemical Engineering , 133 , Article 106649. 10.1016/j.compchemeng.2019.106649. Green open access

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Abstract

Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossil-based materials. However, they are generally difficult to optimize due to their unsteady-state operation modes and stochastic behaviours. Furthermore, biological systems are highly complex, therefore plant-model mismatch is often present. To address the aforementioned challenges we propose a Reinforcement learning based optimization strategy for batch processes. In this work we applied the Policy Gradient method from batch-to-batch to update a control policy parametrized by a recurrent neural network. We assume that a preliminary process model is available, which is exploited to obtain a preliminary optimal control policy. Subsequently, this policy is updated based on measurements from the true plant. The capabilities of our proposed approach were tested on three case studies (one of which is nonsmooth) using a more complex process model for the true system embedded with adequate process disturbance. Lastly, we discussed advantages and disadvantages of this strategy compared against current existing approaches such as nonlinear model predictive control.

Type: Article
Title: Reinforcement learning for batch bioprocess optimization
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.compchemeng.2019.106649
Publisher version: https://doi.org/10.1016/j.compchemeng.2019.106649
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Machine learning, Batch optimization, Recurrent neural networks, Bioprocesses, Policy gradient, Uncertain dynamic systems, Nonsmooth
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/10111828
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