{"title":"活性污泥废水处理的混合智能控制方案","authors":"E. Sánchez, E. A. Hernández, C. Cadet, J. Béteau","doi":"10.1109/ISIC.2008.4635947","DOIUrl":null,"url":null,"abstract":"This paper presents a neural network identification scheme to estimate substrate, biomass and dissolved oxygen concentrations in an activated sludge wastewater treatment. This scheme is based on a discrete-time high order neural network (RHONN) trained on-line with an extended Kalman filter (EKF)-based algorithm. Then, the identification scheme is associated with a fuzzy control to regulate the ratio between substrate and biomass concentrations. Obtained simulation results are very encouraging.","PeriodicalId":342070,"journal":{"name":"2008 IEEE International Symposium on Intelligent Control","volume":"50 26","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Hybrid Intelligent Control Scheme for Activated Sludge Wastewater Treatment\",\"authors\":\"E. Sánchez, E. A. Hernández, C. Cadet, J. Béteau\",\"doi\":\"10.1109/ISIC.2008.4635947\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a neural network identification scheme to estimate substrate, biomass and dissolved oxygen concentrations in an activated sludge wastewater treatment. This scheme is based on a discrete-time high order neural network (RHONN) trained on-line with an extended Kalman filter (EKF)-based algorithm. Then, the identification scheme is associated with a fuzzy control to regulate the ratio between substrate and biomass concentrations. Obtained simulation results are very encouraging.\",\"PeriodicalId\":342070,\"journal\":{\"name\":\"2008 IEEE International Symposium on Intelligent Control\",\"volume\":\"50 26\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Symposium on Intelligent Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIC.2008.4635947\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Symposium on Intelligent Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIC.2008.4635947","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hybrid Intelligent Control Scheme for Activated Sludge Wastewater Treatment
This paper presents a neural network identification scheme to estimate substrate, biomass and dissolved oxygen concentrations in an activated sludge wastewater treatment. This scheme is based on a discrete-time high order neural network (RHONN) trained on-line with an extended Kalman filter (EKF)-based algorithm. Then, the identification scheme is associated with a fuzzy control to regulate the ratio between substrate and biomass concentrations. Obtained simulation results are very encouraging.
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