Hybrid modeling approach for natural gas desulfurization process: Coupling mechanism and data modeling via compact variable identification

Abstract

The accurate process modelling in natural gas industry is beneficial for enterprises to increase potential profit and realize sustainable production. The integration of industrial data and first-principles computation is effective in hybrid process modelling. However, how to enhance their accuracy, robustness, and interpretability remains a challenge. Herein, a general framework integrating artificial intelligence algorithms and process mechanisms for natural gas desulfurization processes is proposed for accurate hybrid modelling. Firstly, the modelling is driven by the biased sulfur content target data generated by the mechanism model. Secondly, the general data information estimation of multivariate interaction and the independence test framework based on proxy data resampling are proposed based on the high-order information relationship. Then, based on Markov Blanket and causal feature selection principle, the compact variable recognition algorithm and the bias prediction model are further established. Finally, the industrial case of natural gas desulfurization is applied for method validation. On one hand, the prediction effect of the developed hybrid model (R2 0.9453, MAE 0.0982, MSE 0.0168, MAPE 0.03018) performs better than that of the mechanism model. On the other hand, it is indicated by the target deviation modelling that the proposed method can not only effectively reduce the excessive variables into 11 compact variables, which proves the effectiveness and accuracy of the proposed method, with the coefficient of determination R2 reaching 0.9152. Moreover, the rationality and interpretability of the proposed method is validated combining the process knowledge and some interpretability techniques. The contribution of this work is to provide an accurate, robust and interpretable method involving both process mechanism and realistic production data for the modelling for the natural gas industry.