Data-Driven Co-Optimization of Multiple Structural Parameters for the Combustion Chamber in a Coke Oven with a Multi-Stage Air Supply System

Driven by the urgent reduction in industrial energy consumption and nitrogen oxide (NOx) emissions, numerical simulation becomes a significant tool to understand the internal working process and optimize the structure of the combustion chamber in coke oven. However, conventional numerical simulation is computationally expensive and impractical for real-time monitoring or multi-parameter optimization. To address this challenge, this study proposes a novel parameter fusion convolutional network (PFCN) to rapidly reconstruct the spatial temperature distribution in the combustion chamber of a coke oven. The key innovation of PFCN is its dual-stream encoding mechanism, which processes structural parameters (1 × 5 vector) and spatial coordinates (25 × 200 matrix) separately via dedicated encoders, followed by a cross-modal fusion to effectively integrate these heterogeneous inputs. Furthermore, a support vector machine (SVM) is coupled downstream of the PFCN to estimate the exhaust NOx emissions based on the predicted physical information. This coupled PFCN–SVM framework allows universal applicability across different combustion chamber configurations. Based on this framework, parametric influence analysis and co-optimization of five key structural parameters are conducted for a three-stage air-supply coke oven. The results reveal that both the air staging ratio and staging height significantly affect combustion performance. Compared to the basecase, the optimized design simultaneously improves temperature homogeneity by 15.2% and reduces NOx emissions by 8%, with negligible computational cost. This integrated data-driven approach demonstrates considerable potential for combustion chamber optimization, transient process predictions, multi-physics coupling analyses, and online control implementations.