Experimental and Artificial Neuron Network Insights into the Removal of Organic Dyes from Wastewater Using a Clay/Gum Arabic Nanocomposite.

Organic dyes are pollutants that threaten aquatic life and human health. These dyes are used in various industries; therefore, recent research focuses on the problem of their removal from wastewater. The aim of this study is to examine the clay/gum arabic nanocomposite (CG/NC) as an adsorbent to adsorb methylene blue (MB) and crystal violet (CV) dyes from synthetic wastewater. The CG/NC was characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brunaure-Emmett-Teller (BET). The effect of parameters that may influence the efficiency of removing MB and CV dyes was studied (dosage of CG/NC, contact time, pH values, initial concentration, and temperature), and the optimal conditions for removal were determined. Furthermore, an artificial neural network (ANN) model was adopted in this study. The results indicated that the adsorption behavior adhered to the Langmuir model and conformed to pseudo-second-order kinetics. The results also indicated that the removal efficiency reached 99%, and q reached 66.7 mg/g and 52.9 mg/g for MB and CV, respectively. Results also proved that CG/NC can be reused up to four times with high efficiency. The ANN models proved effective in predicting the process of the removal, with low mean squared errors (MSE = 1.824 and 1.001) and high correlation coefficients (R = 0.945 and 0.952) for the MB and CV dyes, respectively.