Fusion of multi-gate expert mechanism and attention strategy for sintering multi-quality prediction model

Abstract

Predicting the quality indicators of the sintering process is a core component in the intelligent regulation of the steel industry, directly impacting production efficiency and product quality. However, existing research faces two key issues: first, traditional single-task models struggle to leverage the correlations between indicators to improve prediction accuracy, and are prone to performance degradation due to conflicts in feature sharing. Second, noise and outliers in industrial data interfere with model training, reducing prediction robustness. To address these issues, this paper proposes a multi-gate expert mixture model that integrates an attention mechanism and dynamic loss weighting. The model achieves collaborative optimization through a three-layer innovative architecture. First, it uses a multi-expert network and task gating mechanism to dynamically balance feature sharing and specificity between indicators, and integrates a task attention layer to enhance the key features of each indicator, improving the model's adaptability to task differences. It also uses a dynamic loss weighting strategy combining L1 loss and Smooth L1 loss to adaptively suppress outlier interference. Experimental results show that the prediction performance on the sintering industrial dataset significantly outperforms traditional models, with R² values of 0.967, 0.982, and 0.986 for product yield, drum index, and RDI_+3.15, respectively. This model provides an efficient solution for the multi-quality indicator collaborative prediction of complex industrial processes, with strong engineering application value.