{"title":"活性污泥工艺的系统识别","authors":"Huong Pei Choo, S. Sahlan, N. Wahab","doi":"10.1109/CCSII.2012.6470488","DOIUrl":null,"url":null,"abstract":"In this paper, an activated sludge process model is obtained using two nonlinear system identification techniques. These techniques are Nonlinear ARX Modeling and Hammerstein-Wiener Modeling. A set of raw data from an existing activated sludge process, considered as black box, two different models are obtained and compared in terms of its best fit percentage. From the result, it is concluded that the Hammerstein-Wiener Modeling technique yields better result with lower order and best fit of 91.6%.","PeriodicalId":389895,"journal":{"name":"2012 IEEE Conference on Control, Systems & Industrial Informatics","volume":"99 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"System identification of activated sludge process\",\"authors\":\"Huong Pei Choo, S. Sahlan, N. Wahab\",\"doi\":\"10.1109/CCSII.2012.6470488\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an activated sludge process model is obtained using two nonlinear system identification techniques. These techniques are Nonlinear ARX Modeling and Hammerstein-Wiener Modeling. A set of raw data from an existing activated sludge process, considered as black box, two different models are obtained and compared in terms of its best fit percentage. From the result, it is concluded that the Hammerstein-Wiener Modeling technique yields better result with lower order and best fit of 91.6%.\",\"PeriodicalId\":389895,\"journal\":{\"name\":\"2012 IEEE Conference on Control, Systems & Industrial Informatics\",\"volume\":\"99 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Conference on Control, Systems & Industrial Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCSII.2012.6470488\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Conference on Control, Systems & Industrial Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCSII.2012.6470488","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, an activated sludge process model is obtained using two nonlinear system identification techniques. These techniques are Nonlinear ARX Modeling and Hammerstein-Wiener Modeling. A set of raw data from an existing activated sludge process, considered as black box, two different models are obtained and compared in terms of its best fit percentage. From the result, it is concluded that the Hammerstein-Wiener Modeling technique yields better result with lower order and best fit of 91.6%.
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