Automatic control of simulated moving bed process with deep Q-network.

Oh, Tae Hoon; Kim, Jong Woo; Son, Sang Hwan; Kim, Hosoo; Lee, Kyungmoo; Lee, Jong Min

Oh, Tae Hoon; Kim, Jong Woo; Son, Sang Hwan; Kim, Hosoo; Lee, Kyungmoo; Lee, Jong Min.

Afiliação

Oh TH; School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.
Kim JW; School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.
Son SH; School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.
Kim H; LG CHEM R & D Campus, Yuseong-gu, Daejeon, 34122, Republic of Korea.
Lee K; LG CHEM R & D Campus, Yuseong-gu, Daejeon, 34122, Republic of Korea.
Lee JM; School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea. Electronic address: jongmin@snu.ac.kr.

J Chromatogr A ; 1647: 462073, 2021 Jun 21.

Article em En | MEDLINE | ID: mdl-33964620

Assuntos

Cromatografia/métodos; Aprendizado Profundo; Simulação por Computador

Palavras-chave

Deep neural network; Optimal control; Reinforcement learning; Simulated moving bed

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cromatografia / Aprendizado Profundo Tipo de estudo: Prognostic_studies Idioma: En Revista: J Chromatogr A Ano de publicação: 2021 Tipo de documento: Article

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