【摘要】 在氧化铝气态悬浮焙烧过程中，主炉温度是反映工况状态的核心变量，对该变量开展多步超前预测能够为实施故障预警与预测控制提供先决条件，进而稳定产品相变转化率并降低生产风险。然而，焙烧过程固有的多变量强耦合与非线性动态演化等特性增加了预测难度，现有数据驱动模型容易产生自回归误差累积，且常丢失高频动态细节，导致预测曲线平滑，难以为实际生产提供指导。针对上述问题，本文提出了一种数据驱动的时频双域融合大模型(TF-LFM)多步预测方法。该方法以特征解耦和时频双域优化为核心，利用局部加权回归的季节-趋势分解(STL)将复杂的工艺时序信号解耦为趋势分量和周期分量。随后，模型利用语言大模型提取长程趋势进行时序推理，并引入语音大模型重构高频周期波动的频域特征，以弥补传统模型丢失动态细节的缺陷。在结合时频联合损失函数约束下，完成双域预测结果的动态协同与还原。基于某大型铝厂实际生产数据的验证结果表明，该方法克服了误差累积与动态细节丢失问题，相较于所对比的3种时序模型，所提方法在32步长时距预测误差较最优对比模型降低约60.5%,为焙烧过程的故障预警与精细化控制提供了可靠的模型支撑。更多还原

【Abstract】 In the gas suspension roasting process of alumina, the main furnace temperature is the core variable reflecting the operating conditions. Accurate multi-step ahead prediction of this variable can provide a prerequisite for the implementation of fault warning and model predictive control, thereby stabilizing the phase transformation rate of products and reducing production risks. However, the inherent characteristics of the roasting process, including strong multivariable coupling and nonlinear dynamic evolution, increase the difficulty of prediction. Existing data-driven models are prone to autoregressive error accumulation and often lose high-frequency dynamic details, resulting in overly smooth prediction curves that fail to provide effective guidance for actual production. To address the above issues, this paper proposes a data-driven multi-step prediction method based on the Time-Frequency dual-domain Large Foundation Model(TF-LFM). With feature decoupling and time-frequency dual-domain optimization as the core, this method uses the Seasonal-Trend Decomposition Procedure Based on Loess(STL) to decouple complex process time-series signals into trend components and periodic components. Subsequently, the model leverages a large language model to extract long-range trends for temporal reasoning, and introduces a large speech model to reconstruct the frequency-domain features of high-frequency periodic fluctuations, so as to compensate for the defect of dynamic detail loss in traditional models. Under the constraint of the time-frequency joint loss function, the dynamic synergy and restoration of the dual-domain prediction results are completed. The verification results based on actual production data from a large aluminum plant show that the proposed method overcomes the problems of error accumulation and dynamic detail loss. Compared with the 3 time-series models for comparison, the prediction error of the proposed method in 32-step long horizon prediction is reduced by about 60.5% compared with the optimal comparison model, which provides reliable model support for fault warning and refined control of the roasting process.更多还原