Journal
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
Volume 39, Issue 10, Pages 6793-6803Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13369-014-1277-2
Keywords
Neural network (NN); Modeling; Clinoptilolite; Cu (II) removal
Categories
Funding
- University of Johannesburg
Ask authors/readers for more resources
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 A degrees 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available