A Priori Knowledge-based Dual Hierarchical RNN for Spatial-Temporal Process Modeling: Using a Multi-Tubular Reactor as a Case Study

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.

Automated summary

This paper introduces a new deep learning method for modeling spatial-temporal industrial processes. The proposed model incorporates both domain knowledge and physical rules, and utilizes multiple RNNs to model spatial and temporal relationships. It achieves more accurate predictions with fewer parameters.