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.

As one of the most important parameters for coke oven of steel industry, heating flue temperature plays a pivotal role in obtaining quality-guaranteed final product. While the complexity such as nonlinearity, time-delay, and coupling relationship with its heating fuel, in particular, blast furnace gas (BFG), brings about challenges for heating flue temperature prediction and optimization. As such, a data-mechanism combined driven systematic approach considering both internal and external influencing factors of coke oven is proposed in this study. To provide a solid dataset, a density-based spatial clustering of applications with noise (DBSCAN) based outlier detection algorithm is designed at first for preprocessing, which accommodates the data characteristics in practice. Then, taking full consideration of the periodic and trend features of flue temperature data, a neural network (NN) based multi-channel prediction model is constructed for temperature forecasting. In order to establish dynamic rather than static constraints for the following temperature optimization, a mechanism based controllable region assessment method is proposed. Finally, the flue temperature is optimized via a well-designed fuzzy-based approach along with swarm and evolutionary algorithms for parameter determination. Based on the real data, the simulation results demonstrate the superiority of the proposed systematic approach compared with other partially applied methods, so as to manifest its benefits for the operational optimization of coke oven in steel industry.