Modeling and Optimization of Fe(III) Adsorption from Water using Bentonite Clay: Comparison of Central Composite Design and Artificial Neural Network

Abstract

Abstract Adsorption of Fe(III) from water was optimized using response surface methodology and artificial neural network. The initial concentration of Fe(III), contact time, and concentration of adsorbent were defined as input variables, while the percentage of adsorbed Fe(III) was labeled as output variable. Bentonite clay served as an adsorbent. The correlation coefficient, root mean square error, and mean absolute error were calculated after fitting the experimental data by the second‐order polynomial model at the central composite design (CCD). The multilayer perceptron with architecture of 3‐9‐1 provided the best performance. A logistic sigmoid was applied as an activation function in the hidden layer, while in the output layer a linear function was used.