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2010-0264(Print)
2972-3698(Online)
Int. J. Environ. Sci. Dev.
Bimonthly
10.18178/IJESD
Prof. Richard Haynes
Prof. Gordon Huang; Prof. Marc Rosen
Ms. Cherry L. Chen
Scopus, Google Scholar, CNKI, ProQuest, EBSCO.
editor@ijesd.org
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Home > Articles > All Issues > 2020 > Volume 11 Number 2 (Feb. 2020) >
IJESD 2020 Vol.11(2): 62-66 ISSN: 2010-0264 doi: 10.18178/ijesd.2020.11.2.1226

Prediction of Adsorptive Capacity of Various Agricultural Wastes in the Removal of Heavy Metals, Dyes, and Antibiotic in Wastewater Using ANN

Abstract—Artificial Neural Network model was proposed for the prediction of the adsorptive capacity of various agricultural wastes in the removal of heavy metals, dyes, and antibiotic in water. A total amount of 103 data sets were obtained from different literature and was split into training (70%), validation (15%) and testing (15%) data. After considering different architectures, an input layer that uses eight independent variables (molecular weight of the adsorbate, adsorbent, adsorbent pre-treatment preparation, average initial concentration of adsorbate in solution, mass of adsorbent, adsorbent dosage, pH, and temperature), one hidden layer with 18 neurons and one neuron in the output layer was found to give the best result. The overall mean square error was 3487, while the correlation coefficient for the test dataset is 0.91898.

Index Terms—Adsorptive capacity, agricultural wastes, artificial neural network, correlation coefficient.

The authors are with the School of Chemical, Biological, and Materials Engineering and Sciences, Mapua University, Muralla St., Intramuros, Manila, 1002, Philippines (e-mail: adnieva@mapua.edu.ph).

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Cite: Aileen D. Nieva, Rosette Eira E. Camus, Eric R. Halabasco, Bonifacio T. Doma, and Reuben James Q. Buenafe, "Prediction of Adsorptive Capacity of Various Agricultural Wastes in the Removal of Heavy Metals, Dyes, and Antibiotic in Wastewater Using ANN," International Journal of Environmental Science and Development vol. 11, no. 2, pp. 62-66, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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