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

被引：176

作者：

Geyikci, Feza ^{[2
]}

Kilic, Erdal ^{[3
]}

Coruh, Semra ^{[4
]}

Elevli, Sermin ^{[1
]}

机构：

[1] Ondokuz Mayis Univ, Ind Eng Dept, Samsun, Turkey

[2] Ondokuz Mayis Univ, Chem Eng Dept, Samsun, Turkey

[3] Ondokuz Mayis Univ, Comp Eng Dept, Samsun, Turkey

[4] Ondokuz Mayis Univ, Environm Eng Dept, Samsun, Turkey

来源：

CHEMICAL ENGINEERING JOURNAL | 2012年 / 183卷

关键词：

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);

D O I：

10.1016/j.cej.2011.12.019

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页码：53 / 59

页数：7

共 44 条

[1] Removal of cationic heavy metal from aqueous solution by activated carbon impregnated with anionic surfactants [J].