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231225s2018 xx |||||o 00| ||eng c |
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|a 10.2166/wst.2018.349
|2 doi
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|a pubmed25n0962.xml
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|a DE-627
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|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Oliveira, Alessandra da Silva
|e verfasserin
|4 aut
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| 245 |
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|a Neural network for fractal dimension evolution
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|c 2018
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 28.01.2019
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|a Date Revised 15.12.2020
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|a published: Print
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|a Citation Status MEDLINE
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|a The coagulation/flocculation process is an essential step in drinking water treatment. The process of formation, growth, breakage and rearrangement of the formed aggregates is key to enhancing the understanding of the flocculation process. Artificial neural networks (ANNs) are a powerful technique, which can be used to model complex problems in several areas, such as water treatment. This work evaluated the evolution of the fractal dimension of aggregates obtained through ANN modeling in the coagulation/flocculation process conducted in high apparent color water (100 ± 5 PtCo), using alum as coagulant in dosages varying from 1 to 12 mg Al3+ L-1, and shear rates from 20 to 60 s-1 for flocculation times from 1 to 60 minutes. Based on raw data, the ANN model resulted in optimized condition of 9.5 mg Al3+ L-1 and pH 6.1, for color removal of 90.5%. For fractal dimension evolution, the ANN was able to represent from 95% to 99% of the results
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|a Journal Article
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|a Water
|2 NLM
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|a 059QF0KO0R
|2 NLM
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| 700 |
1 |
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|a Lopes, Verônica Dos Santos
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Filho, Ubirajara Coutinho
|e verfasserin
|4 aut
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| 700 |
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|a Moruzzi, Rodrigo Braga
|e verfasserin
|4 aut
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|a de Oliveira, André Luiz
|e verfasserin
|4 aut
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| 773 |
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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 78(2018), 3-4 vom: 26. Sept., Seite 795-802
|w (DE-627)NLM098149431
|x 0273-1223
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|g volume:78
|g year:2018
|g number:3-4
|g day:26
|g month:09
|g pages:795-802
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|u http://dx.doi.org/10.2166/wst.2018.349
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