{"title":"废水净化技术的能量优化","authors":"G. Ionescu, Gabriella Böhm","doi":"10.2478/jaes-2022-0023","DOIUrl":null,"url":null,"abstract":"Abstract The paper presents the main methods of energy optimization of the purification processes, by implementing computer-based technologies. Both the process control possibilities using fuzzy logic, for situations where the process model is not very certain, but it must be well known, as well as the use of feed-forward artificial neural networks trained by the back-propagation method using the learning mechanism are reviewed and supervised (using the Matlab program).","PeriodicalId":44808,"journal":{"name":"Journal of Applied Engineering Sciences","volume":"12 1","pages":"173 - 178"},"PeriodicalIF":1.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy Optimization of Wastewater Cleaning Technologies\",\"authors\":\"G. Ionescu, Gabriella Böhm\",\"doi\":\"10.2478/jaes-2022-0023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The paper presents the main methods of energy optimization of the purification processes, by implementing computer-based technologies. Both the process control possibilities using fuzzy logic, for situations where the process model is not very certain, but it must be well known, as well as the use of feed-forward artificial neural networks trained by the back-propagation method using the learning mechanism are reviewed and supervised (using the Matlab program).\",\"PeriodicalId\":44808,\"journal\":{\"name\":\"Journal of Applied Engineering Sciences\",\"volume\":\"12 1\",\"pages\":\"173 - 178\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Engineering Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/jaes-2022-0023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Engineering Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/jaes-2022-0023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Energy Optimization of Wastewater Cleaning Technologies
Abstract The paper presents the main methods of energy optimization of the purification processes, by implementing computer-based technologies. Both the process control possibilities using fuzzy logic, for situations where the process model is not very certain, but it must be well known, as well as the use of feed-forward artificial neural networks trained by the back-propagation method using the learning mechanism are reviewed and supervised (using the Matlab program).