复杂工况下选择性催化还原脱硝系统的迁移强化学习控制

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

中文关键词  SCR脱硝系统  变工况  未知工况  强化学习  迁移学习

英文关键词  SCR denitration system  variable working condition  unknown working condition  reinforcement learning  transfer learning

中文摘要

      针对复杂工况下选择性催化还原(SCR)系统难以实现精确脱硝控制的问题, 本文提出一种基于迁移强化学 习的智能控制方法. 首先根据机组负荷的变化将整体运行过程划分为不同阶段. 然后训练了强化学习控制器以分 别学习各个阶段的不同特征, 从而实现了变工况下SCR脱硝系统的精确控制. 此外, 借鉴了迁移学习的思路以应对 预料之外的未知工况, 避免了因工况未知所造成的不利影响. 最后将训练好的控制器用于实际SCR脱硝系统的控制 中, 实验结果表明所提方法可以有效地控制复杂工况下燃煤机组NOx的排放量, 为复杂工况下SCR脱硝系统的智能 控制提供了借鉴.

英文摘要

      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 coalfired power unit under complex working conditions, and provide an idea for intelligent control of the SCR denitration system under complex working conditions.