سال انتشار:

2020

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عنوان انگلیسی مقاله:

Design of control framework based on deep reinforcement learning and Monte-Carlo sampling in downstream separation

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ترجمه فارسی عنوان مقاله:

طراحی چارچوب کنترل مبتنی بر یادگیری تقویت عمیق و نمونه برداری از مونت-کارلو در جداسازی پایین دست

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منبع:

Sciencedirect - Elsevier - Computers and Chemical Engineering, 140 (2020) 106910. doi:10.1016/j.compchemeng.2020.106910

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نویسنده:

Soonho Hwangbo ∗, Gürkan Sin

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چکیده انگلیسی:

This paper proposes a systematic framework to develop deep reinforcement learning (RL)-based algo- rithms for control system of downstream separation in biopharmaceutical process as follows. First, a sim- ulation model as a digital twin is built and Monte-Carlo sampling generates substantial amounts of sam- ples considering disturbances. Second, the deep RL-based control system is designed and the optimization subject to sample datasets is conducted. The methodology is implemented in a prototype software and relevant codes are shared by Mendeley Data. The proposed model is successfully applied to control the liquid-liquid extraction column for the recovery of fusidic acid as part of downstream processing. The resulting deep RL algorithm provides an operation performance with a better API recovery yield (32 % higher than open loop operation) and lower deviations (23 % lower than open loop operation) against disturbances.
Keywords: Liquid-liquid extraction column | Deep reinforcement learning | Monte-Carlo sampling | Control system | API production | Biopharmaceuticals

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قیمت: رایگان

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توضیحات اضافی:

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