Driven by the urgent reduction in industrial energy consumption and nitrogen oxide (NO_x) 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 NO_x 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 NO_x 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.

The thermal wall function method offers an effective pathway for modeling heat transfer in engines. This paper reexamined the traditional construction method of the thermal wall function and improved it by correcting the integration scheme in the boundary layer; then, an enhanced heat transfer model was introduced. Since the heat release from the chemical reactions is of great significance to the near-wall flow, it is necessary to construct a law of the wall including the heat release effect. A new law of the wall including the impacts of the pressure work and the heat release from chemical reactions was obtained based on the analogy between the momentum transfer and the heat transfer predicted using the improved thermal wall function. In the analogy process, the formula of the eddy-viscosity ratio was reconstructed for the whole boundary layer, and then, the Prandtl number for turbulent flow was improved. The current research is beneficial for predicting the near-wall flow and heat transfer characteristics under conditions with strong interactions between the wall and the flame.