{"title":"An interpretable data-driven approach for process flowsheet convergence troubleshooting","authors":"","doi":"10.1016/j.aei.2024.102873","DOIUrl":null,"url":null,"abstract":"<div><div>Practitioners typically alleviate the convergence problem of process flowsheet models through manual adjustment of the convergence-related flowsheet inputs, which is labor-intensive and relies heavily on expert experience. This paper aims to realize fast troubleshooting for process flowsheets with convergence problems and proposes an interpretable approach for the adjustment of the flowsheets to liberate the manpower for process model maintenance. Specifically, the flowsheet convergence problem is addressed from a data-driven perspective for the first time. The correlation between flowsheet inputs selected according to expert knowledge and convergence status is modeled utilizing the tree-based framework to capture the flowsheet convergence behavior. In addition, a novel interpretable adjustment procedure based on an adaptive minimum mean strategy is constructed to automatically identify strongly convergence-related flowsheet inputs and provide them with quantitative adjustment suggestions. The proposed approach shows effectiveness on non-convergence flowsheets with a success rate of up to 92.5%.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Engineering Informatics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1474034624005214","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Practitioners typically alleviate the convergence problem of process flowsheet models through manual adjustment of the convergence-related flowsheet inputs, which is labor-intensive and relies heavily on expert experience. This paper aims to realize fast troubleshooting for process flowsheets with convergence problems and proposes an interpretable approach for the adjustment of the flowsheets to liberate the manpower for process model maintenance. Specifically, the flowsheet convergence problem is addressed from a data-driven perspective for the first time. The correlation between flowsheet inputs selected according to expert knowledge and convergence status is modeled utilizing the tree-based framework to capture the flowsheet convergence behavior. In addition, a novel interpretable adjustment procedure based on an adaptive minimum mean strategy is constructed to automatically identify strongly convergence-related flowsheet inputs and provide them with quantitative adjustment suggestions. The proposed approach shows effectiveness on non-convergence flowsheets with a success rate of up to 92.5%.
期刊介绍:
Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.