A modeling approach for iron concentration in sand filtration effluent using adaptive neuro-fuzzy model

Effluent iron concentration is an important water quality criterion used for the assessment of the performance of rapid sand filters, in addition to other criteria. This study deals with the prediction of effluent iron concentrations by adaptive neuro-fuzzy (ANFIS) model with input parameters including filter hydraulic loading rate, influent iron concentration, bed porosity and operation time. With trying various types of membership functions, two rule base generation methods, namely subtractive clustering and grid partition were used for a first order Sugeno type inference system. Models were evaluated using root mean squared error (RMSE), index of agreement (IA) and R2 as statistical performance parameters. The fit between experimental results and model outputs showed good agreement for tap water and deionized water; testing RMSE values were 36.33 and 7.66 µg/L, the IA values were 0.996 and 0.971, and R2 values were 0.99 and 0.89, respectively. It was concluded that neuro-fuzzy modeling may be successfully used to predict effluent iron concentration in sand filtration. © 2009 Elsevier Ltd. All rights reserved.

Dergi Adı Expert Systems with Applications
Dergi Cilt Bilgisi 37
Dergi Sayısı 2
Sayfalar 1369 - 1373
Yayın Yılı 2010
Eser Adı
[dc.title]
A modeling approach for iron concentration in sand filtration effluent using adaptive neuro-fuzzy model
Yazar
[dc.contributor.author]
Çakmakçı, Mehmet
Yazar
[dc.contributor.author]
Kınacı, Cumali
Yazar
[dc.contributor.author]
Bayramoğlu, Mahmut
Yazar
[dc.contributor.author]
Yıldırım, Yılmaz
Yayın Yılı
[dc.date.issued]
2010
Yayın Türü
[dc.type]
article
Özet
[dc.description.abstract]
Effluent iron concentration is an important water quality criterion used for the assessment of the performance of rapid sand filters, in addition to other criteria. This study deals with the prediction of effluent iron concentrations by adaptive neuro-fuzzy (ANFIS) model with input parameters including filter hydraulic loading rate, influent iron concentration, bed porosity and operation time. With trying various types of membership functions, two rule base generation methods, namely subtractive clustering and grid partition were used for a first order Sugeno type inference system. Models were evaluated using root mean squared error (RMSE), index of agreement (IA) and R2 as statistical performance parameters. The fit between experimental results and model outputs showed good agreement for tap water and deionized water; testing RMSE values were 36.33 and 7.66 µg/L, the IA values were 0.996 and 0.971, and R2 values were 0.99 and 0.89, respectively. It was concluded that neuro-fuzzy modeling may be successfully used to predict effluent iron concentration in sand filtration. © 2009 Elsevier Ltd. All rights reserved.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
ANFIS
Konu Başlıkları
[dc.subject]
Effluent iron concentration
Konu Başlıkları
[dc.subject]
Modeling
Konu Başlıkları
[dc.subject]
Sand filtration
Künye
[dc.identifier.citation]
Çakmakci, M., Kinaci, C., Bayramoğlu, M. ve Yildirim, Y. (2010). A modeling approach for iron concentration in sand filtration effluent using adaptive neuro-fuzzy model. Expert Systems with Applications, 37(2), 1369-1373. doi:10.1016/j.eswa.2009.06.082
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
0957-4174
İlk Sayfa Sayısı
[dc.identifier.startpage]
1369
Son Sayfa Sayısı
[dc.identifier.endpage]
1373
Dergi Adı
[dc.relation.journal]
Expert Systems with Applications
Dergi Sayısı
[dc.identifier.issue]
2
Dergi Cilt Bilgisi
[dc.identifier.volume]
37
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1016/j.eswa.2009.06.082
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/3952
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
30
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
08 Şubat 2024 08:12
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concentration values neuro-fuzzy parameters performance agreement effluent testing outputs deionized showed results experimental between statistical (RMSE) squared reserved rights Elsevier filtration predict successfully modeling concluded respectively Effluent evaluated loading hydraulic filter including (ANFIS) adaptive concentrations
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