Machine learning assisted CFD optimization of fuel-staging natural gas burners for enhanced combustion efficiency and reduced NOx emissions.

Industrial combustion systems are among the primary contributors to nitrogen oxide (NOx) emissions, posing challenges for air quality management and regulatory compliance. This study presents a computational and data-driven approach to the design and optimization of a natural gas burner employing a folded flame pattern with fuel staging. Using Computational Fluid Dynamics (CFD) simulations combined with Machine Learning (ML)-assisted predictive modeling, the burner geometry, fuel-air mixing behavior, and heat transfer dynamics were systematically optimized. A Support Vector Regression-based model was trained on CFD-generated data to guide design modifications and reduce reliance on trial-and-error experimentation. The resulting burner design achieved a 31% reduction in NOx emissions while maintaining combustion efficiency and improving flame stability. Lower peak flame temperatures contributed to reduced pollutant formation. Particle tracing analysis revealed recirculation zones that promoted optimal fuel-air mixing and heat transfer. This integrated CFD-ML framework demonstrates a scalable solution for cleaner combustion design. Future work will focus on experimental validation and the adaptability of the burner to alternative fuels such as hydrogen-rich blends and biogas, aiming to extend the applicability of this approach across diverse industrial settings.