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231224s2016 xx ||||| 00| ||eng c |
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|a (NLM)27685960
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Zangooei, Hossein
|e verfasserin
|4 aut
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| 245 |
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|a Prediction of coagulation and flocculation processes using ANN models and fuzzy regression
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|c 2016
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Band
|b nc
|2 rdacarrier
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|a Date Completed 01.06.2017
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|a Date Revised 02.12.2018
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|a published: Print
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|a Citation Status MEDLINE
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|a Coagulation and flocculation are two main processes used to integrate colloidal particles into larger particles and are two main stages of primary water treatment. Coagulation and flocculation processes are only needed when colloidal particles are a significant part of the total suspended solid fraction. Our objective was to predict turbidity of water after the coagulation and flocculation process while other parameters such as types and concentrations of coagulants, pH, and influent turbidity of raw water were known. We used a multilayer perceptron (MLP), a radial basis function (RBF) of artificial neural networks (ANNs) and various kinds of fuzzy regression analysis to predict turbidity after the coagulation and flocculation processes. The coagulant used in the pilot plant, which was located in water treatment plant, was poly aluminum chloride. We used existing data, including the type and concentrations of coagulant, pH and influent turbidity, of the raw water because these types of data were available from the pilot plant for simulation and data was collected by the Tehran water authority. The results indicated that ANNs had more ability in simulating the coagulation and flocculation process and predicting turbidity removal with different experimental data than did the fuzzy regression analysis, and may have the ability to reduce the number of jar tests, which are time-consuming and expensive. The MLP neural network proved to be the best network compared to the RBF neural network and fuzzy regression analysis in this study. The MLP neural network can predict the effluent turbidity of the coagulation and the flocculation process with a coefficient of determination (R2) of 0.96 and root mean square error of 0.0106
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|a Journal Article
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|a Aluminum Compounds
|2 NLM
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| 650 |
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|a Chlorides
|2 NLM
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| 650 |
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|a Aluminum Chloride
|2 NLM
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| 650 |
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7 |
|a 3CYT62D3GA
|2 NLM
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| 700 |
1 |
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|a Delnavaz, Mohammad
|e verfasserin
|4 aut
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|a Asadollahfardi, Gholamreza
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Water science and technology : a journal of the International Association on Water Pollution Research
|d 1986
|g 74(2016), 6 vom: 01. Sept., Seite 1296-1311
|w (DE-627)NLM098149431
|x 0273-1223
|7 nnas
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| 773 |
1 |
8 |
|g volume:74
|g year:2016
|g number:6
|g day:01
|g month:09
|g pages:1296-1311
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|d 74
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|h 1296-1311
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