Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents.

This paper illustrates the application of artificial neural network (ANN) for prediction of performances in competitive adsorption of phenol and resorcinol from aqueous solution by conventional and low cost carbonaceous adsorbent materials, such as activated carbon (AC), wood charcoal (WC) and rice husk ash (RHA). The three layer's feed forward neural network with back propagation algorithm in MATLAB environment was used for estimation of removal efficiencies of phenol and resorcinol in bi-solute water environment based on 29 sets of laboratory batch study results. The input parameters used for training of the neural network include amount of adsorbent (g/L), initial concentrations of phenol (mg/L) and resorcinol (mg/L), contact time (h), and pH. The removal efficiencies of phenol and resorcinol were considered as an output of the neural network. The performances of the developed ANN models were also measured using statistical parameters, such as mean error, mean square error, root mean square error, and linear regression. The comparison of the removal efficiencies of pollutants using ANN model and experimental results showed that ANN modeling in competitive adsorption of phenolic compounds reasonably corroborated with the experimental results.