Numerical simulation-driven machine learning and particle swarm optimization of burner fuel distribution for cleaner combustion in a thermal power plant

Coal-fired power plants remain important energy source in many countries, but releasing significant nitrogen oxides (NO x ) emissions with serious environmental and health impacts. This study focuses on optimizing lignite combustion in a large utility boiler to minimize NO x emission by adjusting fuel distribution over the burner tiers. The proposed methodology integrates an in-house developed numerical code, correlation analysis, Extreme Gradient Boosting (XGBoost) model and Particle Swarm Optimization (PSO) algorithm. A database of computational fluid dynamics (CFD) simulations was generated to develop machine learning (ML) models for predicting NO x emission and furnace exit gas temperature (FEGT). Based on the ML models, PSO was applied to minimize NO x emissions while maintaining FEGT close to reference case. The framework is applied to a real scale utility boiler, demonstrating practical applicability and measurable environmental impact. Its multidisciplinary significance lies in the synergistic combination of CFD, artificial intelligence, and PSO for combustion control in large-scale energy system. Numerical simulation confirmed the accuracy of the PSO-based optimization, showing excellent agreement between predicted and simulated NO x emission and FEGT. The optimized case corresponds to a 30% reduction in NO x emission compared to the reference scenario. The results also indicated risk of localized overheating and slagging at the furnace walls and possible problems with ascending flame, highlighting the importance of adjusting the operating conditions and flame control to ensure stable boiler performance. The presented comprehensive method provides a novel and effective solution to improve the environmental performance of thermal power plants through intelligent optimization of combustion parameters.