Artificial Neural Networks Modeling of Dynamic Adsorption From Aqueous Solution

Abstract

The aim of this work is to use multilayered perceptron artificial neural networks (MLP-ANN) and multiple linear regressions (MLR) models to predict the dynamic adsorption of the complex system of adsorbent-adsorbate in solid-liquid phase. A set of 1859 data points were used. For the (MLP-ANN), nine neurons were used in the input layer, sixteen neurons at hidden layer and one was used in the output layer. Levenberg Marquardt learning (LM) algorithm, logarithmic sigmoid transfer function and linear transfer function were used at the hidden and output layer respectively. The comparison of the obtained results in term of root mean square error (RMSE) and correlation coefficient (R) using the (MLP-ANN) and MLR models revealed the superiority of the (MLP-ANN) model in predicting of dynamic adsorption process.

The statistic results showed a correlation coefficient R = 0.991 with root mean square error RMSE= 0.0521for the (MLP-ANN) model and R= 0.80 with RMSE=0.237for the MLR model. Thus, it can be suggested that the artificial neural network model gave far better and more significant results.