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
- 1pH, and temperature), one hidden layer with
- 2the School of Chemical, Biological, and Materials
Abstract
Artificial Neural Network model was proposed forthe prediction of the adsorptive capacity of various agriculturalwastes in the removal of heavy metals, dyes, and antibiotic inwater. A total amount of 103 data sets were obtained fromdifferent literature and was split into training (70%), validation(15%) and testing (15%) data. After considering differentarchitectures, an input layer that uses eight independentvariables (molecular weight of the adsorbate, adsorbent,adsorbent pre-treatment preparation, average initialconcentration of adsorbate in solution, mass of adsorbent,adsorbent dosage, pH, and temperature), one hidden layer with18 neurons and one neuron in the output layer was found to givethe best result. The overall mean square error was 3487, whilethe correlation coefficient for the test dataset is 0.91898.
Keywords
- Adsorptive capacity
- agricultural wastes
- artificial neural network
- correlation coefficient
How to Cite
adsorbent dosage, 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. https://doi.org/10.18178/ijesd.2020.11.2.1226
Copyright & License
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).