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Optimizing the combustion efficiency of a thermal power generating unit(TPGU) is a highly challenging and critical task in the energy industry. Wedevelop a new data-driven AI system, namely DeepThermal, to optimize thecombustion control strategy for TPGUs. At its core, is a new model-basedoffline reinforcement learning (RL) framework, called MORE, which leverageshistorical operational data of a TGPU to solve a highly complex constrainedMarkov decision process problem via purely offline training. In DeepThermal, wefirst learn a data-driven combustion process simulator from the offlinedataset. The RL agent of MORE is then trained by combining real historical dataas well as carefully filtered and processed simulation data through a novelrestrictive exploration scheme. DeepThermal has been successfully deployed infour large coal-fired thermal power plants in China. Real-world experimentsshow that DeepThermal effectively improves the combustion efficiency of TPGUs.We also report the superior performance of MORE by comparing with thestate-of-the-art algorithms on the standard offline RL benchmarks.

DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning