MgO/Clinoptilolite Nanocomposite for Adsorption of Anionic Dyes from Aqueous Solution: Modeling and Optimization

Abstract

Different methods have been used for wastewater treatment [1]. Among these methods, adsorption has been used extensively because of its operational simplicity and no need for external energy to do the purification treatment [2]. Dyes are one of the main classes of water pollutants, which can be found in the effluent of different industries. Zeolites are extensively used as adsorbents in environmental systems because of their porous structure, high adsorption capacity, low cost, non-toxicity and abundance in nature. Recently, MgO has been reported as an excellent adsorbent for anionic dye removal [3]. In this study, in order to the stabilizing of MgO nanostructures to facility, and also investigation of the synergistic effect of MgO and clinoptilolite in the anionic dye removal from wastewater, the MgO-clinoptilolite nanocomposites were prepared by co-precipitation method. The prepared nanocomposites were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR). The XRD analysis revealed that MgO have superior dispersion with less agglomeration and sintering on the clinoptilolite. The performance of these nanocomposites was investigated as adsorbent in the removal of metyle orange as an anionic dye pollutant. The clinoptilolite showed a very low performance, by the addition of MgO nanoparticles, the removal percentage of methylene blue was extremely increased (50 wt.% MgO-clinoptilolite has the best performance). In recent years, the RSM is widely used for modeling and opti- mization of many fields of engineering studies [4]. To model and optimize the performance of 50 wt.% MgO-clinoptilolite nano-adsorbent, the response surface methodology (RSM) based on a central composite design (CCD) was employed. The effect of process variables, including pH, temperature and adsorbate to adsorbent weigh ratio was studied. Analysis of variance confirmed the accuracy and precision of generating quadratic models. The temperature and pH had the most pronounced effects on dye removal efficiency. The maximum dye removal efficiency (99 %) was predicted and experimentally validated at the optimum conditions: pH of 10, temperature of 57 °C and adsorbate to adsorbent weigh ratio of 1.18 mg/g.