Intelligent identification method of chemical processes based on maximum mean discrepancy domain generalization

Abstract

BackgroundBenefiting from the massive process data stored in the plant distributed control system, the identification of chemical processes using deep learning methods has been widely studied. However, the statistical characteristics of chemical process data often change over time due to random interference caused by environmental impacts, the complexity and variability of chemical reaction processes, and changes in operating conditions. This makes the model obtained by the deep learning method based on the assumption of independent identical distribution sensitive to distribution changes. MethodsA neural network identification method based on multi-domain multi-kernel maximum mean discrepancy (MD-MK-MMD) domain generalization is proposed. The identification data is split with the maximum average distance to maximize the difference in the distribution of identification data. The MD-MK-MMD domain generalization method is adopted to reduce distribution difference and learn model parameters. Based on the gate recurrent unit (GRU) model and combined with MD-MK-MMD domain generalization, an intelligent identification framework for chemical processes is constructed. Significant findingsExperiments on actual process datasets of a heating furnace and a fractionator show the effectiveness of the proposed chemical process identification framework. The errors of the MD-MK-MMD domain generalization based identification method on the test sets are lower than that of the state-of-the-art neural network-based identification methods, which means the proposed method reduces the model sensitivity to the identification data distribution changes.