A large language model empowered multi-scale fusion attention network for industrial temperature prediction

Le résumé fourni par la source

Abstract Accurate extraction tank temperature prediction is essential for ensuring product quality and process reliability in pharmaceutical manufacturing. This task remains challenging due to concealed temporal patterns, long-range dependencies, and heterogeneous dynamics arising from coexisting continuous process variables and binary valve-state signals. To address these problems, this study develops an ensemble model integrating a pre-trained large language model (LLM) with a multi-scale fusion attention network by leveraging the sequence modeling capability of LLMs. Firstly, a multi-perspective refiner attention module is introduced to capture critical spatial and channel features, while a multi-scale feature fusion module is presented to extract heterogeneous characteristics across different temporal resolutions. Secondly, the pre-trained LLM module is designed to capture intricate temporal dynamics and long-term dependencies in industrial time series. Finally, an improved Adaboost ensemble strategy further enhances prediction accuracy and stability. Experiments on six real-world extraction tank datasets show that the proposed model outperforms state-of-the-art baselines, demonstrating its effectiveness for industrial temperature prediction.

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