The production of polyvinyl chloride (PVC) encounters challenges stemming from the temporal and spatial coupling characteristics inherent in the fixed bed ethylene oxychlorination process. Consequently, the implementation of enhanced safety measures and risk reduction strategies becomes imperative. This study introduces a pioneering methodology leveraging a spectral temporal graph neural network. By leveraging reactor temperature data, spatial variable decoupling facilitated by the Fourier transform, and a self-attentive mechanism within graph neural networks, the proposed approach adeptly forecasts future reactor states. The model's seamless alignment with the physical knowledge of reaction processes, validated through the adjacency matrix and hotspot region identification, underscores its efficacy in ensuring process safety and mitigating operational risks in PVC production. Empirical findings further validate the effectiveness of the approach, with predictions demonstrating an error margin of less than 0.5°C in forecasting future reactor temperatures.

中文翻译：

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通过时空耦合分析加强对乙烯氧氯化反应器状态的预测监测


聚氯乙烯 （PVC） 的生产遇到了来自固定床乙烯氧氯化工艺固有的时间和空间耦合特性的挑战。因此，实施增强的安全措施和降低风险策略变得势在必行。本研究介绍了一种利用光谱时间图神经网络的开创性方法。通过利用反应堆温度数据、傅里叶变换促进的空间变量解耦以及图神经网络中的自关注机制，所提出的方法可以熟练地预测未来的反应堆状态。该模型与反应过程的物理知识无缝对齐，并通过邻接矩阵和热点区域识别进行验证，强调了其在确保 PVC 生产中的过程安全和降低操作风险方面的有效性。实证结果进一步验证了该方法的有效性，预测表明预测未来反应器温度的误差范围小于 0.5°C。