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dc.contributor.authorPetsagkourakis, Panagiotis
dc.contributor.authorSandoval, Ilya Orson
dc.contributor.authorBradford, Eric
dc.contributor.authorZhang, Dongda
dc.contributor.authordel Rio-Chanona, Ehecatl Antonio
dc.date.accessioned2020-04-15T07:59:43Z
dc.date.available2020-04-15T07:59:43Z
dc.date.created2019-12-04T17:13:46Z
dc.date.issued2020
dc.identifier.issn0098-1354
dc.identifier.urihttps://hdl.handle.net/11250/2651084
dc.description.abstractBioprocesses 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.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleReinforcement Learning for Batch Bioprocess Optimizationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.volume133en_US
dc.source.journalComputers and Chemical Engineeringen_US
dc.identifier.doi10.1016/j.compchemeng.2019.106649
dc.identifier.cristin1756826
dc.description.localcode© 2019. This is the authors’ accepted and refereed manuscript to the article. Locked until 18.11.2021 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ "en_US
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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal