TY - JOUR
T1 - Preparation of a new adsorbent for the removal of arsenic and its simulation with artificial neural network-based adsorption models
AU - Rodríguez-Romero, J. A.
AU - Mendoza-Castillo, D. I.
AU - Reynel-Ávila, H. E.
AU - De Haro-Del Rio, D. A.
AU - González-Rodríguez, L. M.
AU - Bonilla-Petriciolet, A.
AU - Duran-Valle, C. J.
AU - Camacho-Aguilar, K. I.
N1 - Publisher Copyright:
© 2020 Elsevier Ltd.
PY - 2020/8
Y1 - 2020/8
N2 - The preparation of an alternative material for the adsorption of arsenic from aqueous solution was studied. This adsorbent was obtained from the pyrolysis and ZnCl2 activation of Opuntia ficus indica biomass (widely known as nopal), which is a typical plant of the Mexican landscape. Preparation conditions of this adsorbent were improved to increase its arsenic adsorption properties. Experimental kinetic and isotherm data for the arsenic removal with the best adsorbent were quantified to analyze its performance. A detailed physicochemical characterization of this adsorbent was carried out to obtain insights about the arsenic adsorption mechanism. A set of new isotherm and kinetic equations were also developed for modeling the arsenic adsorption. These novel models were obtained from the hybridization of the traditional adsorption equations with an artificial neural network. The artificial neural network was used to improve the performance of the conventional kinetic and isotherm equations for the simulation of arsenic removal at different conditions of pH and temperature. Performance of these models was assessed using the arsenic adsorption experimental data obtained with tested adsorbent. Results showed that hybrid models outperformed the well-known kinetic and isotherm adsorption equations commonly used in water treatment allowing better calculations for process design. These models can be extended for the study and analysis of the adsorption of a variety of water pollutants.
AB - The preparation of an alternative material for the adsorption of arsenic from aqueous solution was studied. This adsorbent was obtained from the pyrolysis and ZnCl2 activation of Opuntia ficus indica biomass (widely known as nopal), which is a typical plant of the Mexican landscape. Preparation conditions of this adsorbent were improved to increase its arsenic adsorption properties. Experimental kinetic and isotherm data for the arsenic removal with the best adsorbent were quantified to analyze its performance. A detailed physicochemical characterization of this adsorbent was carried out to obtain insights about the arsenic adsorption mechanism. A set of new isotherm and kinetic equations were also developed for modeling the arsenic adsorption. These novel models were obtained from the hybridization of the traditional adsorption equations with an artificial neural network. The artificial neural network was used to improve the performance of the conventional kinetic and isotherm equations for the simulation of arsenic removal at different conditions of pH and temperature. Performance of these models was assessed using the arsenic adsorption experimental data obtained with tested adsorbent. Results showed that hybrid models outperformed the well-known kinetic and isotherm adsorption equations commonly used in water treatment allowing better calculations for process design. These models can be extended for the study and analysis of the adsorption of a variety of water pollutants.
KW - Adsorption modeling
KW - Arsenic
KW - Artificial neural network
KW - Opuntia ficus indica
KW - Water treatment
UR - http://www.scopus.com/inward/record.url?scp=85086379419&partnerID=8YFLogxK
U2 - 10.1016/j.jece.2020.103928
DO - 10.1016/j.jece.2020.103928
M3 - Artículo
AN - SCOPUS:85086379419
SN - 2213-3437
VL - 8
JO - Journal of Environmental Chemical Engineering
JF - Journal of Environmental Chemical Engineering
IS - 4
M1 - 103928
ER -