Optimization by Artificial Neural Network (ANN) of Malachite Green Adsorption on Clays: Contribution to the Achievement of the Sustainable Development Goals (SDGs)

This study focused on the adsorption of malachite green onto clays in aqueous solutions. The results were simulated using an artificial neural network (ANN). Materials were characterized using X-ray fluorescence spectrometry, Fourier transform infrared spectroscopy, X-ray diffraction, and nitrogen adsorption at 77 K. Sorption experiments were carried out in the discontinuous mode, examining the influences of contact time, adsorbent dose, solution pH, initial concentration, and temperature. The neural network topology was 4–10-1. The results predicted by this model show a good agreement with experimental data. The mathematical modelling of the obtained isotherms revealed that the Freundlich isotherm model is perfectly consistent with the experimental data. The thermodynamic parameters, such as the changes in Gibbs free energy, enthalpy, and entropy, are determined. The MG adsorption is physical, spontaneous, and exothermic for both adsorbents. This method, therefore, appears as an effective means to achieve the objectives of sustainable development of the United Nations.