{"title":"用于模拟移动床轴向浓度曲线预测的图卷积网络","authors":"","doi":"10.1016/j.cjche.2024.05.029","DOIUrl":null,"url":null,"abstract":"<div><p>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 <em>R</em><sup>2</sup>, indicate that OV-GCN has better prediction accuracy in predicting five essential aromatic compounds' axial concentration profiles of an SMB for separating <em>p</em>-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.</p></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Graph convolutional network for axial concentration profiles prediction in simulated moving bed\",\"authors\":\"\",\"doi\":\"10.1016/j.cjche.2024.05.029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 <em>R</em><sup>2</sup>, indicate that OV-GCN has better prediction accuracy in predicting five essential aromatic compounds' axial concentration profiles of an SMB for separating <em>p</em>-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.</p></div>\",\"PeriodicalId\":9966,\"journal\":{\"name\":\"Chinese Journal of Chemical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chinese Journal of Chemical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1004954124002283\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1004954124002283","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Graph convolutional network for axial concentration profiles prediction in simulated moving bed
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 R2, 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.
期刊介绍:
The Chinese Journal of Chemical Engineering (Monthly, started in 1982) is the official journal of the Chemical Industry and Engineering Society of China and published by the Chemical Industry Press Co. Ltd. The aim of the journal is to develop the international exchange of scientific and technical information in the field of chemical engineering. It publishes original research papers that cover the major advancements and achievements in chemical engineering in China as well as some articles from overseas contributors.
The topics of journal include chemical engineering, chemical technology, biochemical engineering, energy and environmental engineering and other relevant fields. Papers are published on the basis of their relevance to theoretical research, practical application or potential uses in the industry as Research Papers, Communications, Reviews and Perspectives. Prominent domestic and overseas chemical experts and scholars have been invited to form an International Advisory Board and the Editorial Committee. It enjoys recognition among Chinese academia and industry as a reliable source of information of what is going on in chemical engineering research, both domestic and abroad.