A Priori Knowledge-Based Dual Hierarchical RNN for Spatial–Temporal Process Modeling: Using a Multitubular Reactor as a Case Study

Abstract

Deep learning methods have been rapidly developed in recent decades. In this work, they are extended to model spatial-temporal industrial processes. Instead of pure black-box data-driven modeling approaches, the proposed model encodes the domain knowledge and physical rules governing the spatiotemporal system, called a dual-hierarchical recurrent neural network (DH-RNN). Both spatial and temporal relationships are modeled by multiple RNNs with diverse structures, which need correct specifications of all the interactions between spatial and temporal variables with a priori knowledge of the real process. A more accurate prediction can be obtained with fewer parameters employed in the network. And the effectiveness of the proposed DH-RNN is verified via a real ethylene oxychlorination process.