Artificial intelligence based simulation of Cd(II) adsorption separation from aqueous media using a nanocomposite structure

Applications of soft computing techniques for prediction of pollutant removal by environmentally friendly adsorbents (case study: the nitrate adsorption on modified hydrochar)

Artificial intelligence has emerged as a powerful tool for solving real-world problems in various fields. This study investigates the simulation and prediction of nitrate adsorption from an aqueous solution using modified hydrochar prepared from sugarcane bagasse using an artificial neural network (ANN), support vector machine (SVR), and gene expression programming (GEP). Different parameters, such as the solution pH, adsorbent dosage, contact time, and initial nitrate concentration, were introduced to the models as input variables, and adsorption capacity was the predicted variable. The comparison of artificial intelligence models demonstrated that an ANN with a lower root mean square error (0.001) and higher R² (0.99) value can predict nitrate adsorption onto modified hydrochar of sugarcane bagasse better than other models. In addition, the contact time and initial nitrate concentration revealed a higher correlation between input variables with the adsorption capacity.

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model's training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.