Modelling of As3+ adsorption from aqueous solution using Azadirachta indica by artificial neural network

Abstract

An intensive study has been made on the removal efficiency of As3+ from aqueous solution by zinc oxide nanoparticle entrenched on activated silica (ZnO-NPs-AS) using aqueous leaf extract of Azadirachta indica. ZnO-NPs-AS is characterized using SEM-EDX, FT-IR and XRD. The effect of various parameters such as initial concentration of As3+, adsorbent dosage, contact time, pH and agitation is studied systematically. The maximum adsorption of As3+ is found to be 98.31% at pH 5, equilibrium time of 50 min using adsorbent of 3 g/L and initial concentration of 0.06 mg/L at agitation speed of 250 rpm. Adsorption parameters for the Langmuir, Freundlich, Tempkin and BET isotherms were determined. The equilibrium data were best described by Langmuir isotherm model and fits quite well with the experimental data with good correlation coefficient of 0.974. The results of intraparticle diffusion model suggested that intraparticle diffusion was not the rate-controlling process. From the values, it is accomplished that the maximum adsorption corresponds to a saturated monolayer of As3+ molecules on the adsorbent surface with constant energy. The data were analysed using kinetics models akin to pseudo-first and second order. All the findings presented in this study suggested following pseudo-second-order equation for the adsorption of As3+ on to Zno-NPs-AS. The data collected from laboratory-scale experimental set up are used to train a feed forward back propagation learning algorithm having 5-20-1 three-layered architecture. The model uses tangent sigmoid transfer function at input to hidden layer whereas a linear purelin function is used at output layer. The data are divided into training (70%), testing (15%), and validation (15%) sets. The network is found to be working satisfactorily as absolute mean square percentage error of 0.0014 is obtained during training phase. Comparison between the model results and experimental data gives a high degree of correlation (R2 = 0.986) indicating that the matlab nntool 2010a neural network model is able to predict the sorption efficiency with reasonable accuracy.