Accurate forecasting of the hearth lining temperature in blast furnaces is essential for operational safety and efficiency, however it remains challenging owing to the complex spatiotemporal coupling and time-lag effects among process variables. To address this issue, we present a new spatiotemporal feature modeling framework that integrates gated recurrent units (GRUs) with a dual-attention mechanism to capture multi-scale temporal dependencies and dynamically assess variable importance. A convolutional neural network module is incorporated to extract localized spatial features from the time-series data, thereby enhancing the representation of the underlying metallurgical mechanisms. The validation on real industrial data showed that the proposed model achieved a root mean square error of 0.0523 and a hit rate of 94.26%, outperforming conventional long short-term memory and GRU models. This approach offers a reliable solution for intelligent health monitoring and proactive maintenance in modern data-driven ironmaking operations under highly dynamic and uncertain conditions.
Chen et al. (Sun,) studied this question.