Graph convolutional network for axial concentration profiles prediction in simulated moving bed  

作　　者：Can Ding Minglei Yang Yunmeng Zhao Wenli Du 

机构地区：[1]Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

出　　处：《Chinese Journal of Chemical Engineering》2024年第9期270-280,共11页中国化学工程学报（英文版）

基　　金：supported by the National Key Research and Development Program of China(2022YFB3305900);National Natural Science Foundation of China(62293501,62394343);the Shanghai Committee of Science and Technology,China(22DZ1101500);Major Program of Qingyuan Innovation Laboratory(00122002);Fundamental Research Funds for the Central Universities(222202417006);Shanghai AI Lab

摘　　要：The simulated moving bed(SMB)chromatographic separation is a continuous compound separation process based on the differences in adsorption capacity exhibited by distinct constituents of a mixture on the fluid phase and stationary phase.The prediction of axial concentration profiles along the beds in a unit is crucial for the operating optimization of SMB.Though the correlation shared by operating variables of SMB has an enormous impact on the operational state of the device,these correlations have been long overlooked,especially by the data-driven models.This study proposes an operating variable-based graph convolutional network(OV-GCN)to enclose the underrepresented correlations and precisely predict axial concentration profiles prediction in SMB.The OV-GCN estimates operating variables with the Spearman correlation coefficient and incorporates them in the adjacency matrix of a graph convolutional network for information propagation and feature extraction.Compared with Random Forest,K-Nearest Neighbors,Support Vector Regression,and Backpropagation Neural Network,the values of the three performance evaluation metrics,namely MAE,RMSE,and R^(2),indicate that OV-GCN has better prediction accuracy in predicting five essential aromatic compounds'axial concentration profiles of an SMB for separating p-xylene(PX).In addition,the OV-GCN method demonstrates a remarkable ability to provide high-precision and fast predictions in three industrial case studies.With the goal of simultaneously maximizing PX purity and yield,we employ the non-dominated sorting genetic algorithm-II optimization method to perform multi-objective optimization of the PX purity and yield.The outcome suggests a promising approach to extracting and representing correlations among operating variables in data-driven process modeling.

关 键 词：CHROMATOGRAPHY PREDICTION Operating variables Graph convolutional network OPTIMIZATION 

分 类 号：TQ051.13[化学工程] TP183[自动化与计算机技术—控制理论与控制工程]