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dc.contributor.author
Martínez, Ernesto Carlos
dc.date.available
2020-03-04T17:17:25Z
dc.date.issued
2000-07
dc.identifier.citation
Martínez, Ernesto Carlos; Batch process modeling for optimization using reinforcement learning; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 24; 2-7; 7-2000; 1187-1193
dc.identifier.issn
0098-1354
dc.identifier.uri
http://hdl.handle.net/11336/98760
dc.description.abstract
Imperfect and incomplete understanding of reaction kinetics compounded with uncontrollable variations not only prevent achieving an optimal operation of batch and semi-batch reactors, but also give rise to potential risks of violating product end- use properties, ecological or safety constraints. This paper proposes a sequential experiment design strategy based on reinforcement learning to accomplish the specific goal of modeling for optimization in batch reactors by making the most effective use of cumulative data and an approximate model. Reactor operating condition is incrementally improved over runs by integrating together estimation of a probabilistic measure of success using an imperfect model and a gradient-based approach so as to trade off exploitation with exploration. An improved operating policy is found by incrementally shrinking the region of interest for policy parameters. The solution strategy focuses on 'learning by doing' using a value function that accounts for endpoint performance and feasibility. Simulation results reveal the robustness of reinforcement learning to parametric and structural modeling errors.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Pergamon-Elsevier Science Ltd
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
BATCH PROCESS
dc.subject
MODELING FOR OPTIMIZATION
dc.subject
REACTION KINETICS
dc.subject.classification
Ingeniería Química
dc.subject.classification
Ingeniería Química
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Batch process modeling for optimization using reinforcement learning
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-03-02T17:44:24Z
dc.journal.volume
24
dc.journal.number
2-7
dc.journal.pagination
1187-1193
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Oxford, UK
dc.description.fil
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
dc.journal.title
Computers and Chemical Engineering
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/S0098-1354(00)00354-9
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