Modelling of lead adsorption from industrial sludge leachate on red mud by using RSM and ANN

Geyikçi F., Kılıç E., Çoruh S., Elevli S.

CHEMICAL ENGINEERING JOURNAL, cilt.183, ss.53-59, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 183
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.cej.2011.12.019
  • Dergi Adı: CHEMICAL ENGINEERING JOURNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.53-59
  • Anahtar Kelimeler: Lead, Red mud, Box-Behnken design, Artificial neural network, RESPONSE-SURFACE METHODOLOGY, HEAVY-METAL IONS, AQUEOUS-SOLUTION, EXPERIMENTAL-DESIGN, ACTIVATED CARBON, WASTE-WATER, REMOVAL, BIOSORPTION, OPTIMIZATION, COPPER(II)
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

In this study, response surface methodology (RSM) and artificial neural network (ANN) were employed to develop prediction models for lead removal from industrial sludge leachate using red mud. The leaching characteristics of industrial sludge were observed by Toxicity Characteristics Leaching Procedure (TCLP). Dosage, time and pH were considered as independent experimental factors. Box-Behnken design (BBD) was chosen for the response surface design setup and was also used as Neural Network Training Set for comparison purposes. To evaluate the accuracy of results, several experiments were then conducted. The results of ANN were found to be more reliable than RSM since better statistical parameters were obtained. (C) 2011 Elsevier B.V. All rights reserved.