Simultaneous online model identification and production optimization using modifier adaptation

José Matias

doi:10.1016/j.jprocont.2021.12.009

Simultaneous online model identification and production optimization using modifier adaptation

2022-02-01

3

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J

José Matias *

V

Vyacheslav Kungurtsev

M

Malcolm Egan

DOI：10.1016/j.jprocont.2021.12.009

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A key problem for many industrial processes is to limit exposure to system malfunction. The system health state can be represented by different models. However, it is often the case that control cost minimization is prioritized over model identification. Indeed, model identification is typically not considered in production optimization, which can lead to delayed awareness and alerting of malfunction. In this paper, we address the problem of simultaneous production optimization and system identification. We develop new algorithms based on modifier adaptation and reinforcement learning, which efficiently manage the tradeoff between cost minimization and identification. For two case studies based on a chemical reactor and subsea oil and gas exploration, we show that our algorithms yield control costs comparable to existing methods while yielding rapid identification of system degradation. (C)& nbsp;2021 Elsevier Ltd. All rights reserved.

Keywords:

Real-time optimization

Modifier adaptation

Reinforcement learning

Process monitoring

Simultaneous online model identification and production optimization using modifier adaptation

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