Transfer reinforcement learning control for a selective catalytic reduction denitration system under complex conditions

被引：1

作者：

Sun X.-M. ^{[1
]}

Peng C. ^{[1
]}

Cheng C.-L. ^{[1
]}

机构：

[1] School of Mechatronic Engineering and Automation, Shanghai University, Shanghai

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2024年 / 41卷 / 03期

基金：

中国国家自然科学基金;

关键词：

reinforcement learning; SCR denitration system; transfer learning; unknown working condition; variable working condition;

D O I：

10.7641/CTA.2023.21030

中图分类号：

学科分类号：

摘要：

Aiming at the problem that selective catalytic reduction (SCR) system is difficult to achieve precise denitration control performance under complex working conditions, an intelligent control method based on transfer reinforcement learning is proposed in this paper. The overall operation process is firstly divided into different stages according to the changes of unit load. Then a reinforcement learning controller is trained to learn different characteristics of each stage, respectively, so as to realize accurate control of the SCR denitration system under variable working conditions. In addition, the idea of transfer learning is used for reference to deal with unexpected unknown working conditions and avoid adverse effects caused by unknown working conditions. Finally, the trained controller is applied to the control of an actual SCR denitration system. Experimental results show that the proposed method can effectively control NOx emissions of a coal-fired power unit under complex working conditions, and provide an idea for intelligent control of the SCR denitration system under complex working conditions. © 2024 South China University of Technology. All rights reserved.

引用

页码：496 / 501

页数：5

共 26 条

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