{"title":"Cloud-Edge Cooperative Control System in Continuous Annealing Processes","authors":"Wenshuo Song, Weihua Cao, Wenkai Hu, Min Wu","doi":"10.20965/jaciii.2023.p0638","DOIUrl":null,"url":null,"abstract":"This study proposes a cloud-edge collaboration framework for temperature regulation in continuous annealing processes. A multiobjective optimization is formulated by ensuring the control accuracy of the temperature to reduce energy consumption and increase efficiency with cloud computing. Based on process analytics, a framework for clustering operating conditions with high real-time requirements is proposed. Further, a recommendation mechanism for furnace temperatures with low real-time requirements is developed in the cloud. Compared with traditional architectures, the cloud-edge collaboration approach improves energy savings and control stability, which demonstrates its effectiveness and practicality.","PeriodicalId":45921,"journal":{"name":"Journal of Advanced Computational Intelligence and Intelligent Informatics","volume":"34 3","pages":"638-644"},"PeriodicalIF":0.7000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Computational Intelligence and Intelligent Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20965/jaciii.2023.p0638","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This study proposes a cloud-edge collaboration framework for temperature regulation in continuous annealing processes. A multiobjective optimization is formulated by ensuring the control accuracy of the temperature to reduce energy consumption and increase efficiency with cloud computing. Based on process analytics, a framework for clustering operating conditions with high real-time requirements is proposed. Further, a recommendation mechanism for furnace temperatures with low real-time requirements is developed in the cloud. Compared with traditional architectures, the cloud-edge collaboration approach improves energy savings and control stability, which demonstrates its effectiveness and practicality.