Novel spatiotemporal graph attention model for production prediction and energy structure optimization of propylene production processes

Abstract

Propylene is essential to the national economy. However, the propylene production process data have complex spatiotemporal, nonlinear and dynamic features, and the consideration of spatiotemporal effects can predict the propylene production more accurately. Therefore, a novel graph attention network (GAT) based long short-term memory network (LSTM)(GAT-LSTM) is proposed for the production prediction and energy structure optimization of propylene production processes. The GAT extracts spatial features to obtain correlations between variables, while the improved LSTM model extracts potential temporal-dependent features. The collected spatiotemporal interaction features are fused by the spatiotemporal fusion module and the adaptive control is realized. Finally, a fully connected layer maps spatiotemporal interaction features to the target domain for obtaining the output. Experimental results show that the GAT-LSTM outperforms the Conv-LSTM, the LSTM, the GAT, the Attribute-relevant distributed variational autoencoder and the Attention-LSTM. Additionally, the GAT-LSTM can provide the optimal raw material scheme for actual propylene production processes.