Development of a Mathematical Model Based on an Artificial Neural Network (ANN) to Predict Nickel Uptake Data by a Natural Zeolite

In this investigation, an artificial-neural-network-based mathematical model was developed for the prediction of nickel adsorption data. As input variables, the initial concentration, adsorbent dosage, and pH of the nickel solution were chosen, while the removal efficiency was chosen as an output variable. The hyperparameters were optimized to determine the perfect topology for the model. The study demonstrated that the 3-2-1 ANN architecture was the most suitable topology. The determination coefficient of 0.98 and the mean squared error of 0.02 indicated the high performance of the developed model, which was successfully applied for isotherm data prediction.