{"title":"Application of cascade feed forward neural network to predict coagulant dose","authors":"D. V. Wadkar, R. Karale, M. Wagh","doi":"10.1080/23249676.2021.1927210","DOIUrl":null,"url":null,"abstract":"Inlet water quality fluctuations affect mainly coagulant dose, and outlet water quality of the water treatment plant (WTP). Many complex physical and chemical processes are involved in WTP and water distribution networks (WDN). These technologies show non-linear behavior, which is challenging to be described by linear mathematical models. Thus, there is a need to develop prediction models for coagulation dose. The present study involves the application of cascade feed-forward neural networks (CFFNN) to predict coagulant dose. CFFNN Model was developed by using the Levenberg-Marquardt Training Algorithm and Bayesian Regularization Training Algorithm to predict coagulant dose. During the development of these models, hidden nodes are varied from 15 to 60, and R is found between 0.914 and 0.947. The best results were obtained by the CFFNN model using the Bayesian Regularization Training Algorithm (CFNNCD2) with hidden node 40, where R = 0.945 for training and 0.947 for testing.","PeriodicalId":51911,"journal":{"name":"Journal of Applied Water Engineering and Research","volume":"9 7","pages":"87 - 100"},"PeriodicalIF":1.4000,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23249676.2021.1927210","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Water Engineering and Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23249676.2021.1927210","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"WATER RESOURCES","Score":null,"Total":0}
引用次数: 10
Abstract
Inlet water quality fluctuations affect mainly coagulant dose, and outlet water quality of the water treatment plant (WTP). Many complex physical and chemical processes are involved in WTP and water distribution networks (WDN). These technologies show non-linear behavior, which is challenging to be described by linear mathematical models. Thus, there is a need to develop prediction models for coagulation dose. The present study involves the application of cascade feed-forward neural networks (CFFNN) to predict coagulant dose. CFFNN Model was developed by using the Levenberg-Marquardt Training Algorithm and Bayesian Regularization Training Algorithm to predict coagulant dose. During the development of these models, hidden nodes are varied from 15 to 60, and R is found between 0.914 and 0.947. The best results were obtained by the CFFNN model using the Bayesian Regularization Training Algorithm (CFNNCD2) with hidden node 40, where R = 0.945 for training and 0.947 for testing.
期刊介绍:
JAWER’s paradigm-changing (online only) articles provide directly applicable solutions to water engineering problems within the whole hydrosphere (rivers, lakes groundwater, estuaries, coastal and marine waters) covering areas such as: integrated water resources management and catchment hydraulics hydraulic machinery and structures hydraulics applied to water supply, treatment and drainage systems (including outfalls) water quality, security and governance in an engineering context environmental monitoring maritime hydraulics ecohydraulics flood risk modelling and management water related hazards desalination and re-use.