Removal of direct blue 129 from aqueous medium using surfactant-modified zeolite: a neural network modeling

Mahmoud Zarei,Leila Khadir,Nader Djafarzadeh

doi:10.15171/ehem.2018.15

Abstract

Background: Conserving water for human survival and providing future security are important issues that need to be addressed. Methods: In this study, a zeolite modified with hexadecyl trimethyl ammonium bromide (HDTMA-Br), a cationic surfactant, and its application in removing direct blue 129 (DB129) was examined. Fourier transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM) were used to characterize both modified and unmodified zeolites. The effects of operational parameters such as the amount of adsorbent, initial dye concentration and pH on the removal efficiency of the dye were examined. Results: The results showed that in the initial dye concentration of 50 mg/L, the optimum amounts of adsorbent and pH were 0.3 g and 7, respectively. Increasing the dye concentration from 20 to 100 mg/L resulted in the reduction of the removal efficiency from 100% to 79% in the contact time of 90 minutes. The results indicated the highest attracting correlation with Langmuir model. The maximum adsorbent capacity obtained from Langmuir model was 25 mg/g. The kinetics of the dye adsorption on the modified zeolite followed pseudo-second-order kinetics model. Calculated thermodynamic parameters showed that Gibbs free energy changes (DGo) at temperatures of 20 and 45°C were -29.41 and -35.20 kJ/mol, respectively. Enthalpy (DHo) and entropy changes were equal to 41.181 kJ/mol and 0.241 J/mol K, respectively. The results showed that the processing was a spontaneous endothermic reaction. The process modeled by artificial neural networks (ANN) showed that the experimental results can be accurately modeled using neural network model. The correlation coefficient found between the experimental and the model results was 0.951. Conclusion: Due to the low cost, high abundance and availability of zeolite, the removal efficiency of this adsorbent can be increased to desirable levels by modifying. Keywords: Zeolite, Adsorption, Kinetics, Thermodynamics, Neural networks

Highlights

Simultaneous with the growth of human populations, communities, science and technology, new horizons are being reached in our world
Surface modification of natural zeolite by a cationic surfactant and its application for the removal of direct blue 129 were investigated. Surface properties of both modified and unmodified zeolites were evaluated by infrared spectroscopy and scanning electron microscopy (SEM) images
Examining the effect o f concentration showed that with increasing dye conce n tration, the adsorption volume by modified zeolite was reduced, since at increased dye concentration, repulsion occurred between anionic groups, preventing the adsorption of the dye by the adsorbent

Summary

Introduction

Simultaneous with the growth of human populations, communities, science and technology, new horizons are being reached in our world This growth is associated with high costs. Dyeing units of textile industry are one of the most important environmental contaminants due to the use of thousands of dyeing chemicals. The stability of the molecular structure leads to their resistance against chemical or biological decomposition These compounds, as a vital threat, must be eliminated aaddssoorbrbenetn.t.TThehseesemomdoedlselsarearueseudsetdo tporovpirdoevidsoemseompeersppeecrtsipveectoivn aaddssoorprptitoino,n,susrufarfcaeceprpoproeprteiertsi,easd, saodrspotiropntioafnfinaiftfyinaitnyd atnoddetoscrdiebsecrthibee. Results: The results showed that in the initial dye concentration of 50 mg/L, the optimum amounts of adsorbent and pH were 0.3 g and 7, respectively. Conclusion: Due to the low cost, high abundance and availability of zeolite, the removal efficiency of this adsorbent can be increased to desirable levels by modifying. Removal of direct blue 129 from aqueous medium using surfactant-modified zeolite: a neural network modeling.

Results

Discussion

Conclusion