Predictive modeling and simulation of wastewater treatment process using nano-based materials: Effect of pH and adsorbent dosage

Abstract

A machine learning simulation via artificial neural network was implemented in this work to analyse adsorption removal of cadmium ions from aqueous solutions. The considered solid adsorbent in this work was a nanocomposite of mesoporous silica type and a polymer. To separate ions from water the mesoporous nanocomposite was selected due to its large pores. The simulation runs were carried out to correlate the adsorption parameters including solution pH and adsorbent dosage to the equilibrium concentration of ions in the liquid phase. The training and validation of the neural network indicated agreement with the experimental data for adsorption of cadmium ions, with high accuracy. The statistical evaluation of the data indicated great accuracy with R2 greater than 0.999 for the fitting. The findings of the modelling indicated that the effect of adsorbent dosage is not significant compared to the solution’s pH. The adsorption removal was increased with enhancing the solution pH due to the interaction between the ions and the surface of adsorbent. The results revealed that the developed neural network simulation is robust for prediction of ions removal from water using nanocomposite adsorbents.