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: email@example.com).
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).