Neural Network Technique for Modeling of Cu (II) Removal from Aqueous Solution by Clinoptilolite

John Kabuba; Antoine Mulaba-Bafubiandi; Kim Battle

doi:10.1007/s13369-014-1277-2

Neural Network Technique for Modeling of Cu (II) Removal from Aqueous Solution by Clinoptilolite

John Kabuba, Antoine Mulaba-Bafubiandi, Kim Battle

Metallurgy

University of Johannesburg

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

Abstract

Clinoptilolite was investigated as an ion-exchange material for the removal of Cu (II) from aqueous solution. The effect of operational parameters such as pH, temperature, and initial concentration was studied to optimize the conditions for maximum removal of Cu (II) ions. Optimal operating conditions were determined to be a pH of 4, temperature of 30 °C, and initial concentration of 0.361 mg/l to achieve equilibrium. After backpropagation (BP) training, the neural network (NN) techniques were able to predict the maximum removal efficiency with a tangent sigmoid transfer function (tansig) at hidden layer with 11 neurons and linear transfer function (purelin) at output layer. The Levenberg Marquardts backpropagation training algorithm was found as the best of 10 BP algorithms with mean-squared error of 0.000365. The results showed that NN techniques are able to predict the removal of Cu (II) from aqueous solution.

Original language	English
Pages (from-to)	6793-6803
Number of pages	11
Journal	Arabian Journal for Science and Engineering
Volume	39
Issue number	10
DOIs	https://doi.org/10.1007/s13369-014-1277-2
Publication status	Published - 1 Sept 2014

Keywords

Clinoptilolite
Cu (II) removal
Modeling
Neural network (NN)

ASJC Scopus subject areas

Multidisciplinary

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10.1007/s13369-014-1277-2

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title = "Neural Network Technique for Modeling of Cu (II) Removal from Aqueous Solution by Clinoptilolite",

abstract = "Clinoptilolite was investigated as an ion-exchange material for the removal of Cu (II) from aqueous solution. The effect of operational parameters such as pH, temperature, and initial concentration was studied to optimize the conditions for maximum removal of Cu (II) ions. Optimal operating conditions were determined to be a pH of 4, temperature of 30 °C, and initial concentration of 0.361 mg/l to achieve equilibrium. After backpropagation (BP) training, the neural network (NN) techniques were able to predict the maximum removal efficiency with a tangent sigmoid transfer function (tansig) at hidden layer with 11 neurons and linear transfer function (purelin) at output layer. The Levenberg Marquardts backpropagation training algorithm was found as the best of 10 BP algorithms with mean-squared error of 0.000365. The results showed that NN techniques are able to predict the removal of Cu (II) from aqueous solution.",

keywords = "Clinoptilolite, Cu (II) removal, Modeling, Neural network (NN)",

author = "John Kabuba and Antoine Mulaba-Bafubiandi and Kim Battle",

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AU - Mulaba-Bafubiandi, Antoine

AU - Battle, Kim

PY - 2014/9/1

Y1 - 2014/9/1

N2 - Clinoptilolite was investigated as an ion-exchange material for the removal of Cu (II) from aqueous solution. The effect of operational parameters such as pH, temperature, and initial concentration was studied to optimize the conditions for maximum removal of Cu (II) ions. Optimal operating conditions were determined to be a pH of 4, temperature of 30 °C, and initial concentration of 0.361 mg/l to achieve equilibrium. After backpropagation (BP) training, the neural network (NN) techniques were able to predict the maximum removal efficiency with a tangent sigmoid transfer function (tansig) at hidden layer with 11 neurons and linear transfer function (purelin) at output layer. The Levenberg Marquardts backpropagation training algorithm was found as the best of 10 BP algorithms with mean-squared error of 0.000365. The results showed that NN techniques are able to predict the removal of Cu (II) from aqueous solution.

AB - Clinoptilolite was investigated as an ion-exchange material for the removal of Cu (II) from aqueous solution. The effect of operational parameters such as pH, temperature, and initial concentration was studied to optimize the conditions for maximum removal of Cu (II) ions. Optimal operating conditions were determined to be a pH of 4, temperature of 30 °C, and initial concentration of 0.361 mg/l to achieve equilibrium. After backpropagation (BP) training, the neural network (NN) techniques were able to predict the maximum removal efficiency with a tangent sigmoid transfer function (tansig) at hidden layer with 11 neurons and linear transfer function (purelin) at output layer. The Levenberg Marquardts backpropagation training algorithm was found as the best of 10 BP algorithms with mean-squared error of 0.000365. The results showed that NN techniques are able to predict the removal of Cu (II) from aqueous solution.

KW - Clinoptilolite

KW - Cu (II) removal

KW - Modeling

KW - Neural network (NN)

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ER -

Neural Network Technique for Modeling of Cu (II) Removal from Aqueous Solution by Clinoptilolite

Abstract

Keywords

ASJC Scopus subject areas

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