Monitoring and forecasting of sintering temperature (ST) is vital for safe, stable, and efficient operation of rotary kiln production process. Due to the complex coupling and time-varying characteristics of process data collected by the distributed control system, its long-range prediction remains a challenge. In this article, we propose a multivariate time series forecasting model based on dynamic spatio-temporal graph attention network (GAT) to model time-varying spatio-temporal correlation between the process data and perform long-range forecasting of ST. Aiming at the problem that there is no preset graph structure for multivariate data, we first propose an adaptive adjacency matrix generation algorithm to construct an elementary graph structure for the process data. Then, we design a spatio-temporal graph attention module, which consists of a multihead GAT for extracting time-varying spatial features and a gated dilated convolutional network for temporal features. Finally, considering the different time delay and rhythm of each process variable, we use dynamic system analysis to estimate the delay time and rhythm of each variable to guide the selection of dilation rates in dilated convolutional layers. The application results based on actual data show that the method has high prediction accuracy, and has broad application prospects in industrial processes.

中文翻译：

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用于回转窑烧结温度远程预测的时空图注意网络


烧结温度（ST）的监测和预测对于回转窑生产过程的安全、稳定、高效运行至关重要。由于分布式控制系统采集的过程数据具有复杂的耦合性和时变特性，其远程预测仍然是一个挑战。在本文中，我们提出了一种基于动态时空图注意网络（GAT）的多元时间序列预测模型，对过程数据之间的时变时空相关性进行建模，并对 ST 进行长期预测。针对多元数据没有预设图结构的问题，我们首先提出一种自适应邻接矩阵生成算法来构造过程数据的基本图结构。然后，我们设计了一个时空图注意力模块，它由用于提取时变空间特征的多头 GAT 和用于时间特征的门控扩张卷积网络组成。最后，考虑到每个过程变量的不同时间延迟和节奏，我们利用动态系统分析来估计每个变量的延迟时间和节奏，以指导扩张卷积层中扩张率的选择。实际数据应用结果表明，该方法预测精度较高，在工业过程中具有广阔的应用前景。