NOx emission reduction of coal‐fired power plants through data‐driven model and particle swarm optimization

Abstract

Several studies have aimed to predict and control carbon emissions from coal‐fired power plants. However, the highly complex combustion mechanisms in coal‐fired power plant boilers pose a significant challenge in direct modeling and optimization. To tackle this challenge, this study introduced a data‐driven approach along with model‐based process optimization to mitigate NOx emissions from coal‐fired power plants. The process involved collecting a 5‐month operational dataset containing 67 controllable parameters from a 500 MW coal‐fired power plant. Steady‐state data was isolated from the load output using a moving average method, followed by the application of an isolation forest algorithm to detect and remove anomalies. Correlation analysis was then used to evaluate parameter relationships and eliminate redundant ones. Subsequently, a NOx prediction model was developed, combining an extra tree regressor data‐driven prediction model with particle swarm optimization to optimize the most influential controllable parameters for reducing NOx emissions. Testing the proposed model across four different target loads consistently resulted in a reduction of over 20% in NOx emissions by optimizing boiler combustion parameters, representing a significant achievement in optimizing coal‐fired combustion.