Real-Time Boiler Control Optimization with Machine Learning
This work addresses operational efficiency for coal-fired power plants, but it appears incremental as it integrates existing machine learning and optimization techniques without claiming major breakthroughs.
The paper tackled real-time boiler control optimization in coal-fired power plants to improve stability and energy efficiency, achieving demonstrated effectiveness and efficiency through extensive experiments on a two-month dataset from an industry partner.
In coal-fired power plants, it is critical to improve the operational efficiency of boilers for sustainability. In this work, we formulate real-time boiler control as an optimization problem that looks for the best distribution of temperature in different zones and oxygen content from the flue to improve the boiler's stability and energy efficiency. We employ an efficient algorithm by integrating appropriate machine learning and optimization techniques. We obtain a large dataset collected from a real boiler for more than two months from our industry partner, and conduct extensive experiments to demonstrate the effectiveness and efficiency of the proposed algorithm.