Research status and prospects of intelligent combustion control models for industrial heating furnaces

As core thermal energy equipment in steel, power, and petrochemical industries, industrial heating furnaces' combustion control directly impacts energy efficiency, costs, and pollutant emissions-critical for industrial decarbonization. However, combustion processes' high nonlinearity, time-variability, multi-disturbance, and multi-objective constraints make traditional manual/single strategies ineffective for complex fluctuations and optimization, failing high-efficiency, low-carbon demands. Recent intelligent control technologies, integrating data-driven and mechanism-based models, have advanced temperature field modeling, parameter prediction, multi-objective optimization, and advanced control, boosting accuracy, stability, and emission reduction. This paper reviews intelligent combustion control model progress, compares model applicability, addresses key limitations (e.g., poor generalization, weak real-time integration), and proposes future directions (cross-model integration, data-scarce modeling, online optimization), providing theoretical and technical references for heating furnaces' energy-efficient, lowcarbon, and intelligent upgrading.