Artificial intelligence simulation of water treatment using a novel bimodal micromesoporous nanocomposite.

• Soft computing simulation of separation using a nanomaterial. • Machine learning computation of adsorption equilibrium concentrations. • Evaluation of the effect of adsorbent dosage and pH on removal using the model. Molecular separation using nanostructured materials have attracted much attention recently for various applications. Different classes of materials have been used among which metal organic framework (MOF) and layered double hydroxide (LDH) have been recently developed due to their superior structure in separation, specifically adsorption process. In this work, we have studied removal of dye from water using a nanocomposite of MOF/LDH. The method of investigation is development of an artificial intelligence-based model for prediction of the adsorption process. The adsorption data have been obtained for removal of a dye (orange II reactive dye) from water at different conditions. The model was proposed using artificial neural network for simulation of the adsorption output which was considered to be equilibrium concentration in the solution (C e). Indeed, the equilibrium concentration of solute was assumed as the main output in developing the model, while two inputs were postulated including the pH and adsorbent dosage. The model was built considering two hidden layers in the neural network. The validation and training steps were carried out and statistical analysis indicated excellent agreement between the simulated and measured data possessing high coefficient of determination (R2 greater than 0.999). The model revealed to be a high-performance model for simulation of dye removal using adsorption process with high accuracy. [ABSTRACT FROM AUTHOR]