Multi-Physics Modeling and Adaptive Control of Gas Burner Systemsfor Enhanced Energy Efficiency and Emission Reduction in Cement Dry-ing Processes

The cement industry faces significant challenges in achieving sustainable production, pri-marily due to high energy consumption and substantial greenhouse gas emissions during the drying and clinkering processes. This study presents a comprehensive approach to multi-physics modeling and adaptive control of gas burner systems in cement drying units, aimed at enhancing energy efficiency and reducing harmful emissions such as nitro-gen oxides (NOx), carbon monoxide (CO), and carbon dioxide (CO₂). Utilizing advanced simulation tools including Ansys Fluent for gas dynamics, Ansys Maxwell for electro-magnetic analysis, and Ansys Twin Builder for integrated system modeling, a multi-physics framework is developed to simulate the interplay of thermal, fluid, and electro-mechanical processes within the burner system. The proposed intelligent control system incorporates adaptive algorithms, including proportional-integral-derivative (PID) regu-lation for damper positioning, vector and scalar control for asynchronous motors driving fans and shutters, and multi-criteria optimization to balance energy use, emission levels, and process stability. Key innovations include the integration of reduced-order models (ROM) derived from electromagnetic simulations to replace standard library models, ensuring higher accuracy in drive performance predictions. The system is implemented using Schneider Electric hardware, such as Modicon controllers, Altivar frequency con-verters, and Magelis interfaces, enabling real-time parameter adjustment based on varying operational conditions like fuel-air ratios, flame temperature, and material humidity. Simulation results demonstrate a 10-15% reduction in energy consumption for electric drives, NOx emissions below 30-40 ppm in compliance with international standards, and improved drying efficiency through optimized heat and mass transfer. Experimental vali-dation on a prototype gas burner confirms the model's accuracy, with discrepancies in temperature and pressure fields under 5%. This work contributes to the decarbonization of the cement sector by providing a scalable, adaptive solution that minimizes operational costs while enhancing environmental performance. Future extensions could incorporate machine learning for predictive maintenance and further emission forecasting.