Artificial Neural Network (ANN) Approach for Modeling Co2+ Ion Adsorption from Aqueous Solutions by Loess Soil Nanoparticles
- *Corresponding Author:
- Heydartaemeh MR
Faculty of Mining, Petroleum and Geophysics
Shahrood University of Technology
E-mail: [email protected]
Received Date: May 22, 2017 Accepted Date: June 07, 2017 Published Date: September 11, 2017
Citation: Heydartaemeh MR, Panjipour R, Karamouzian M (2017) Artificial Neural Network (ANN) Approach for Modeling Co2+ Ion Adsorption from Aqueous Solutions by Loess Soil Nanoparticles. Adv Recycling Waste Manag 2: 134. doi:10.4172/2475-7675.1000134
Copyright: © 2017 Heydartaemeh MR, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This study discussed the Artificial Neural Network (ANN) based classification technique is applied for the prediction of percentage adsorption efficiency for the removal of Co2+ Ion from Aqueous Solutions by Loess Soil Nanoparticles. The effect of operational parameters was performed to investigate the effect of pH, contact time, initial concentration, and temperature on adsorption process are studied to optimize the conditions for maximum removal of Co2+ Ion. The experimental data were studied in terms of kinetic characteristic of adsorption using pseudo-first-order and pseudo-second-order models, and it was found that Co2+ Ion adsorption on both adsorbent fitted well with pseudo-second-order models. The equilibrium experimental data were analyzed using Langmuir and Freundlich isotherm models. The finding indicated that Loess Soil Nanoparticles especially could be used as an appropriate adsorbent to remove potentially harmful metals such as Co2+ Ion from contaminated water.