Application of multilayer perceptron network and random forest models for modelling the adsorption of chlorobenzene on a modifed bentonite by intercalation with hexadecyltrimethyl ammonium (HDTMA)
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Date
2021-11-19
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Reaction Kinetics, Mechanisms and Catalysis
Abstract
Prediction of adsorption capacity, one of the most important properties of any adsorbent-adsorbate system, is crucial for adsorption studies. In this investigation, two
approaches such as multilayer perceptron (MLP) and random forest (RF) were used
to predict the adsorption capacity of hexadecyltrimethyl ammonium modifed bentonite to remove chlorobenzene (CB) from aqueous solution. The adsorption study
was conducted in batch mode at diferent adsorption parameters. The results show
that the adsorption processes were best described by Freundlich isotherm model,
while the adsorption mechanism followed the pseudo-second order kinetics. It was
observed that the structure of MLP model that give the best prediction consisted
of three layers: input layer with four neurons, output layer with one neuron, and
four neurons at hidden layer. The three important parameters for RF model were
ntree=500, mtry=1, and node size=1. According to the results obtained, the MLP
model provided slightly higher levels of accuracy with a consistently high coefcient
of determination (R2=0.996) and low root mean square error (RMSE=0.00101)
compared to RF model. (R2=0.969, RMSE=0.03008). Therefore, the initial concentration of CB with 35.29%, appeared to be the most infuential parameter in the
adsorption of CB on the modifed bentonite.