Adaptive neuro-fuzzy modeling of head loss in iron removal with rapid sand filtration

Breakthrough and terminal head loss are the main parameters that determine the performance of rapid sand filters. Carman-Kozeny and Ergun equations can be applied to estimate head loss, but can only be applied to clean filter beds. Elaborated models are needed to predict head loss in dirty filters. In this study, a neuro-fuzzy modeling approach was proposed to estimate head loss in dirty filters. Hydraulic loading rate, influent iron concentration, bed porosity, and operating time were selected as input variables. Various types of membership functions were tried. Two rule-base generation methods - subtractive clustering and grid partition - were used for a first-order, Sugeno-type inference system. Using 11 rules and the grid-partition method, an optimum rule base set was developed and the lowest root mean squared error (RMSE) was obtained. Tap and deionized waters were used to obtain testing RMSE values of 1.094 and 0.926, respectively. The fit between experimental results and model outputs was excellent, with the multiple correlation coefficient (R2) greater than 0.99. Based on these findings, the authors conclude that neurofuzzy modeling may successfully be used to predict filter head loss.

Yazar Çakmakci M.
Kinaci C.
Bayramoglu M.
Yayın Türü Conference Object
Tek Biçim Adres https://hdl.handle.net/20.500.12628/4136
Tek Biçim Adres 10.2175/106143008X304659
Konu Başlıkları ANFIS modeling
Filtration
Head loss
Iron
Koleksiyonlar Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu
Dergi Adı Water Environment Research
Dergi Cilt Bilgisi 80
Dergi Sayısı 12
Sayfalar 2268 - 2275
Yayın Yılı 2008
Eser Adı
[dc.title]
Adaptive neuro-fuzzy modeling of head loss in iron removal with rapid sand filtration
Yayıncı
[dc.publisher]
Water Environment Federation
Yayın Türü
[dc.type]
conferenceObject
Özet
[dc.description.abstract]
Breakthrough and terminal head loss are the main parameters that determine the performance of rapid sand filters. Carman-Kozeny and Ergun equations can be applied to estimate head loss, but can only be applied to clean filter beds. Elaborated models are needed to predict head loss in dirty filters. In this study, a neuro-fuzzy modeling approach was proposed to estimate head loss in dirty filters. Hydraulic loading rate, influent iron concentration, bed porosity, and operating time were selected as input variables. Various types of membership functions were tried. Two rule-base generation methods - subtractive clustering and grid partition - were used for a first-order, Sugeno-type inference system. Using 11 rules and the grid-partition method, an optimum rule base set was developed and the lowest root mean squared error (RMSE) was obtained. Tap and deionized waters were used to obtain testing RMSE values of 1.094 and 0.926, respectively. The fit between experimental results and model outputs was excellent, with the multiple correlation coefficient (R2) greater than 0.99. Based on these findings, the authors conclude that neurofuzzy modeling may successfully be used to predict filter head loss.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Yılı
[dc.date.issued]
2008
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.2175/106143008X304659
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/4136
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
ANFIS modeling
Konu Başlıkları
[dc.subject]
Filtration
Konu Başlıkları
[dc.subject]
Head loss
Konu Başlıkları
[dc.subject]
Iron
Yazar
[dc.contributor.author]
Çakmakci M.
Yazar
[dc.contributor.author]
Kinaci C.
Yazar
[dc.contributor.author]
Bayramoglu M.
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
1061-4303
Yazar Departmanı
[dc.contributor.department]
Zonguldak Bülent Ecevit Üniversitesi
İlk Sayfa Sayısı
[dc.identifier.startpage]
2268
Son Sayfa Sayısı
[dc.identifier.endpage]
2275
Dergi Adı
[dc.relation.journal]
Water Environment Research
Dergi Sayısı
[dc.identifier.issue]
12
Dergi Cilt Bilgisi
[dc.identifier.volume]
80
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
14
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
10 Şubat 2024 01:19
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Tıklayınız
filters predict filter modeling applied estimate values testing grid-partition method obtain waters deionized obtained optimum (RMSE) squared lowest developed Breakthrough successfully neurofuzzy conclude authors findings greater coefficient respectively correlation multiple excellent outputs results experimental between
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