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• W2937556601 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 updatedbased on measurements from thetrueplant. The capabilities of our proposed approach were tested on three case studies (one of which is nonsmooth) using a more complex process model for thetruesystemembedded with adequate process disturbance. Lastly, we discussed the advantages and disadvantages of this strategy compared against current existing approaches such as nonlinear model predictive control." @default.
• W2937556601 created "2019-04-25" @default.
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• W2937556601 date "2019-04-15" @default.
• W2937556601 modified "2023-09-27" @default.
• W2937556601 title "Reinforcement Learning for Batch Bioprocess Optimization." @default.
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• W2937556601 hasPublicationYear "2019" @default.
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