Thermodynamic Multi-Objective Optimization: A Deep Learning and Evolutionary Algorithm Approach

As global demands for enhanced energy efficiency and environmental protection continue to grow, optimizing the combustion processes of industrial boilers has become a critical challenge.These processes involve complex thermodynamic and chemical reactions that directly impact energy utilization and pollutant emissions.However, existing methods for combustion prediction and optimization often fall short in terms of accuracy and efficiency, making it difficult to adapt to varying operational conditions.This study proposes a thermodynamics-based multi-objective optimization method for industrial boiler combustion.A high-precision combustion prediction model is established using deep learning, and an evolutionary algorithm is employed for multi-objective optimization, aiming to achieve an optimal balance between combustion efficiency and pollutant emissions.The findings of this research not only offer new insights into combustion optimization for industrial boilers but also contribute valuable theoretical and practical implications for enhancing energy utilization efficiency and reducing environmental pollution in industrial production.