BP-ANN Approach for Modeling Cd(II) Bio-Sorption from Aqueous Solutions Using Cajanus cajan Husk

Abstract

<em>This work aims at the modeling of bio-sorption of cadmium(II) onto physically and chemically activated Cajanus cajan (Pigeon pea) husks. Experimental data obtained were fitted to a number of isotherm and kinetic models, and the results interpreted. The monolayer Cd(II) bio-sorption capacities of the husk were found to considerably increase by 2.82 times due to chemical activation, for bio-sorption from a solution containing an initial Cd(II) concentration of 100 mg/L and by about 1.78 times for a solution containing an initial Cd(II) concentration of 150 mg/L. Further, the </em><em>BackPropagation Artificial Neural Network (BP-ANN) was applied to understand the accuracy and prediction of isotherm and kinetic data. The tangent sigmoid transfer function was used at the input to hidden layer whereas a linear function was used at output layer. The isotherm and kinetic data were distributed into training (65%) and testing (35%) phase. The training, testing, and prediction by BP-ANN were found to be adequate, with an absolute relative percentage error of 2.1827 and correlation coefficient R²of 0.9967 and 0.9863 at prediction for isotherm and kinetic studies, respectively. Comparison of BP-ANN and experimental results indicated that the prediction model is capable of predicting the bio-sorption effectiveness with good accuracy.</em>