Control Strategy for Denitrification Efficiency of Coal-Fired Power Plant Based on Deep Reinforcement Learning

Abstract

The optimal control of denitrification system in coal-fired power plants in China has recently received widespread attention. The accurate prediction of denitrification efficiency and formulate control strategy of denitrification efficiency can guide the control and operation of the denitrification system better. Meanwhile, it can achieve the effect of energy conservation and Nitrogen oxides (NOx) reduction. In this paper, we take a domestic 1000 MW unit as an example, consider each of the major factors that affect the denitrification efficiency of selective catalytic reduction (SCR). We put forward a deep reinforcement learning (DRL) model by combining the Long short-term memory (LSTM) model and the Asynchronous Advantage Actor - Critic algorithm (A3C). We first use the LSTM to build a prediction model for denitrification efficiency. We then use the DRL model to obtain a control strategy for SCR denitrification efficiency in coal-fired power plants. The experimental results demonstrate that the accuracy of denitrification efficiency prediction model we established is better than other machine learning models, reaching 91.7%. Our control strategy model is industrially feasible and universally applicable.

Keywords

• Coal-fired power plant
• denitrification efficiency
• selective catalytic reduction (SCR)
• long short-term memory (LSTM)
• asynchronous advantage actor critic (A3C)
• deep reinforcement learning