DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning,arXiv - CS - Systems and Control

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DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning
arXiv - CS - Systems and Control Pub Date : 2021-02-23 , DOI: arxiv-2102.11492
Xianyuan Zhan, Haoran Xu, Yue Zhang, Yusen Huo, Xiangyu Zhu, Honglei Yin, Yu Zheng

Thermal power generation plays a dominant role in the world's electricity supply. It consumes large amounts of coal worldwide, and causes serious air pollution. Optimizing the combustion efficiency of a thermal power generating unit (TPGU) is a highly challenging and critical task in the energy industry. We develop a new data-driven AI system, namely DeepThermal, to optimize the combustion control strategy for TPGUs. At its core, is a new model-based offline reinforcement learning (RL) framework, called MORE, which leverages logged historical operational data of a TGPU to solve a highly complex constrained Markov decision process problem via purely offline training. MORE aims at simultaneously improving the long-term reward (increase combustion efficiency and reduce pollutant emission) and controlling operational risks (safety constraints satisfaction). In DeepThermal, we first learn a data-driven combustion process simulator from the offline dataset. The RL agent of MORE is then trained by combining real historical data as well as carefully filtered and processed simulation data through a novel restrictive exploration scheme. DeepThermal has been successfully deployed in four large coal-fired thermal power plants in China. Real-world experiments show that DeepThermal effectively improves the combustion efficiency of a TPGU. We also report and demonstrate the superior performance of MORE by comparing with the state-of-the-art algorithms on the standard offline RL benchmarks. To the best knowledge of the authors, DeepThermal is the first AI application that has been used to solve real-world complex mission-critical control tasks using the offline RL approach.

更新日期：2021-02-24

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