Modeling and Optimization of Defluoridation by Calcined Using Response Surface Methodology and Artificial Neural Network Combined with Experimental Design
Publication: Journal of Hazardous, Toxic, and Radioactive Waste
Volume 21, Issue 3
Abstract
A face-centered central composite design was applied as an input to the artificial neural network (ANN), demonstrating the significance of statistical design for an efficient performance with a lesser number of data. The influence of the initial fluoride concentration, adsorbent dose, and reaction time on the fluoride adsorption capacity of the calcined layered double hydroxide was determined through the response surface methodology (RSM) and ANN. A significant variation of the adsorption capacity () confirmed the importance of the selected process parameters. The adsorption capacity was found to be increased with the increase in the initial fluoride concentration, whereas a reverse trend was observed with the variation of the adsorbent dose. A significant interactive effect was found between the adsorbent dose and the initial fluoride concentration. The mean square of error and associated with the RSM and ANN model are 0.139, 0.135 and 0.993, 0.995, respectively. The several error functions and linear regression among the predicted and experimental values of the ANN and RSM model demonstrated a better applicability of the ANN model.
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Journal of Hazardous, Toxic, and Radioactive Waste
Volume 21 • Issue 3 • July 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Mar 25, 2016
Accepted: Jul 20, 2016
Published online: Sep 1, 2016
Discussion open until: Feb 1, 2017
Published in print: Jul 1, 2017
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