A comparative study of artificial neural network models for the prediction of Cd removal efficiency of polymer inclusion membranes

In this study, three different artificial neural network (ANN) including feed forward back-propagation (FFBPNN), recurrent neural network (RNN), and generalized regression neural network (GRNN) were proposed to estimate Cd removal efficiency through polymer inclusion membranes (PIMs). A multiple linear regression (MLR) statistical technique was also applied to evaluate PIMs efficiency. The proposed ANN models and MLR results were compared regarding statistical performance criteria such as root-mean-squared error, mean absolute error and coefficient of determination (R-2). In the modeling, time, film thickness, extractant type and amount, plasticizer type and amount and polymer molecular weight were considered as inputs while Cd removal efficiency was output. Furthermore, sensitivity analysis is performed to investigate the effect of each input parameter on the output regarding magnitude. According to performance criteria of models, FFBPNN and RNN have the best prediction capability as compared with GRNN and MLR. Sensitivity analysis results demonstrated that extractant amount, plasticizer type and plasticizer amount are more influential operating parameters than time, extractant type, film thickness, and polymer molecular weight. The results of FFBPNN and RNN models are superior and reliable in the prediction of PIMs Cd removal efficiency due to the nonlinearity of data set.