Prediction of CO Concentration in the Sintering Process Using a Process-Guided Kolmogorov–Arnold Network

Accurate prediction of carbon monoxide (CO) concentration at the main exhaust outlet of the sintering process is important for combustion state assessment and CO emission control. Existing data-driven methods treat process variables as homogeneous inputs, neglecting their process affiliations and cross-system interactions. This study proposes a process-guided Kolmogorov–Arnold network (PG-KAN) for CO concentration soft measurement. According to process relationships, 230 process variables are organized into material, flue gas, and auxiliary systems, and independent radial basis function-based Kolmogorov–Arnold network (RBF-KAN) encoding branches extract system-specific nonlinear representations. Cross-system interaction modules capture interactions among these systems. A tail coverage-aware loss reduces large errors and underestimation bias in high-CO concentration intervals. Experiments on 18,749 industrial samples show that PG-KAN achieves a mean absolute error (MAE) of 107.700 mg/m3, a coefficient of determination (R2) of 0.9105, and a Pearson correlation coefficient of 0.9542, outperforming six baseline models. Ablation studies verify the effectiveness of process-guided variable organization, cross-system interactions, and the proposed loss. Edge-function and SHapley Additive exPlanations (SHAP) analyses provide insights into nonlinear mappings and feature contributions learned by the model. The proposed method provides an interpretable, process-guided framework for CO concentration soft measurement in industrial sintering processes.