Spatiotemporal attention mechanism-based deep network for critical parameters prediction in chemical process

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.