In chemical processes, grasping the changing trend of critical parameters can help field operators take appropriate adjustments to eliminate potential fluctuations. Thus, deep networks, renowned for its revolutionary feature representation capability, have been gradually exploited for building reliable prediction models from massive data embraced tremendously nonlinearities and dynamics. Because of the inherent complexity, the process trajectories over the whole running duration make distinctive contributions to the ultimate targets. Specifically, features extracted from different secondary variables at different previous instants have diverse impacts on the current state of primary variables. However, this spatiotemporal relevance discrepancy is rarely considered, which may lead to deterioration of prediction performance. Therefore, this paper seamlessly integrates the spatiotemporal attention (STA) mechanism with convolutional neural networks (CNN) and bi-directional long short-term memory (BiLSTM), and proposes a novel predictive model, namely STA-ConvBiLSTM. Using the deep framework composed of CNN and BiLSTM, the integrated model can, not only automatically explore the esoteric spatial correlations among high-dimensional variables at each time step, but also adaptively excavate beneficial temporal characteristics across all time steps. Meanwhile, STA is further introduced to assign corresponding weights to information with dissimilar importance, so as to prevent high target-relevant interactions from being discarded due to overlong sequences and excessive features. STA-ConvBiLSTM is applied in the case of furnace tube temperature prediction of a delayed coking unit, which exhibits a significant improvement of the prediction accuracy.

在化工过程中，掌握关键参数的变化趋势可以帮助现场操作人员进行适当的调整，以消除潜在的波动。因此，以其革命性的特征表示能力而闻名的深度网络已逐渐被用于从包含极大非线性和动态性的海量数据中构建可靠的预测模型。由于固有的复杂性，整个运行期间的过程轨迹对最终目标做出了独特的贡献。具体来说，在不同的先前时刻从不同的次要变量中提取的特征对主要变量的当前状态有不同的影响。然而，很少考虑这种时空相关性差异，这可能会导致预测性能的恶化。所以，本文将时空注意（STA）机制与卷积神经网络（CNN）和双向长短期记忆（BiLSTM）无缝集成，并提出了一种新颖的预测模型，即 STA-ConvBiLSTM。使用由 CNN 和 BiLSTM 组成的深层框架，集成模型不仅可以在每个时间步自动探索高维变量之间深奥的空间相关性，还可以自适应地挖掘所有时间步的有益时间特征。同时，进一步引入STA，为重要性不同的信息分配相应的权重，防止因序列过长和特征过多而丢弃与目标相关的高交互。STA-ConvBiLSTM应用于延迟焦化装置炉管温度预测，