CMAFNet: A cross-modal adaptive fusion network for operating condition identification in zinc oxide rotary kilns

Accurate identification of operating conditions in zinc oxide rotary kilns is essential to improving energy efficiency, product quality, and sustainability in non-ferrous metallurgical production. However, traditional monitoring approaches predominantly rely on operators' empirical observation of kiln head flames, rendering the assessment subjective and limiting the effective integration of multimodal information. To overcome these constraints, this study develops a multimodal dataset tailored to zinc smelting scenarios and introduces a Cross-Modal Adaptive Fusion Network (CMAFNet). Specifically, the proposed framework incorporates a Cross-Modal Interaction Transformer (CMIT) to capture fine-grained dependencies across heterogeneous image, text, and process data. Subsequently, the Enhanced Adaptive Selection Fusion (EASF) module is developed to suppress redundancy, while the Dynamic Adaptive Fusion (DAF) module performs adaptive aggregation of the refined features at the relation level. Furthermore, a composite loss function combining alignment, center, and triplet losses is designed to mitigate cross-modal distribution discrepancies and enhance discriminative capability by reinforcing intra-class compactness and inter-class separability. Experimental results demonstrate that CMAFNet achieves an accuracy of 93.65%, an F1 score of 92.16%, and a Matthews correlation coefficient (MCC) of 0.894 on the seven-class operating condition identification task, demonstrating robust discriminative performance even for minority classes that are highly similar and prone to confusion. In the conventional three-class setting, the model attains an overall accuracy of 94.52%, with each class exceeding 90%, outperforming representative trimodal fusion baselines. Moreover, it shows competitive performance on a public dataset. Finally, ablation studies and visualizations substantiate the effectiveness and interpretability of the proposed framework.