Reinforcement Learning for Sustainable Combustion in Thermal Power Plant

In recent years, the optimization of thermal power plants has become a critical area of research, driven by the urgent need to enhance energy efficiency and reduce emissions. This paper proposes a novel approach leveraging Reinforcement Learning (RL) techniques to optimize the operation of thermal power plants in real-time. RL, a branch of machine learning, offers a promising framework for decision-making in dynamic and uncertain environments, making it well-suited for the complex and nonlinear nature of power plant operations. By integrating RL algorithms with streaming data from sensors and control systems, our proposed methodology enables adaptive and autonomous control of key parameters such as air-fuel ratio and combustion temperature. This approach not only maximizes combustion efficiency but also minimizes emissions of harmful pollutants such as NOx, SOx, and particulate matter. Through simulation studies and real-world experiments, we demonstrate the effectiveness of our proposed approach, paving the way for the next generation of intelligent and sustainable power plant operation. We present a conceptual framework for RL-based control of thermal power plants highlighting potential benefits such as combustion efficiency, improved environmental performance, and enhanced operational flexibility.