Reinforcement Learning in Process Industries:Review and Perspective

被引：0

作者：

Oguzhan Dogru ^{[1
]}

Junyao Xie ^{[2
,1
]}

Om Prakash ^{[1
]}

Ranjith Chiplunkar ^{[1
]}

Jansen Soesanto ^{[1
]}

Hongtian Chen ^{[2
,1
]}

Kirubakaran Velswamy ^{[1
]}

Fadi Ibrahim ^{[1
]}

Biao Huang ^{[2
,1
]}

机构：

[1] the Department of Chemical and Materials Engineering, University of Alberta

[2] IEEE

来源：

IEEE/CAA Journal of Automatica Sinica | 2024年 / 11卷 / 02期

基金：

加拿大自然科学与工程研究理事会;

关键词：

D O I：

暂无

中图分类号：

TP181 [自动推理、机器学习]; TP273 [自动控制、自动控制系统];

学科分类号：

080201 ; 0835 ;

摘要：

This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.

引用

页码：283 / 300

页数：18

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