{"title":"The intelligent control system of optimal oil manufacturing production","authors":"H. M. Yassine, V. Shkodyrev","doi":"10.1145/3440840.3440848","DOIUrl":null,"url":null,"abstract":"In the article, we analyze the optimality of an oil production manufacturing via intelligent control digital twines. By examining the process industry, we present the primary keys of oil production lines, productivity, and quality. To spotlight on the intelligent process control system, as well as on the adaptive intelligent optimization of the production process, we used several methods, namely: Multi-Criteria Decision Analysis, Pareto optimization method and approximate neural network integration of all production line process information; in addition to tracking analysis, productivity and quality control. Even though this article discusses the optimality of oil manufacturing, the conclusions determine in this article can be extended to the processing industry worldwide.","PeriodicalId":273859,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3440840.3440848","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In the article, we analyze the optimality of an oil production manufacturing via intelligent control digital twines. By examining the process industry, we present the primary keys of oil production lines, productivity, and quality. To spotlight on the intelligent process control system, as well as on the adaptive intelligent optimization of the production process, we used several methods, namely: Multi-Criteria Decision Analysis, Pareto optimization method and approximate neural network integration of all production line process information; in addition to tracking analysis, productivity and quality control. Even though this article discusses the optimality of oil manufacturing, the conclusions determine in this article can be extended to the processing industry worldwide.
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