Artificial neural network simulations and experimental results: Removal of trichlorophenol from water using Chromolaena odorata stem

Abstract

A novel adsorbent for trichlorophenol (TCP) has been developed through the treatment of Chromolaena odorata (Odorata) with iodated table salt. Odorata is an abundant and problematic alien plant which we have found to be effective in removing TCP from aqueous solutions. Kinetic batch tests demonstrated that at pH 5, 99% of TCP could be removed from a solution given sufficient adsorbent loading rate and adsorption contact time with Odorata treated with table salt. Adsorption data were found to fit a 2-layer feed-forward artificial neural network (ANN) with 10 neurons using the Levenberg– Marquardt (trainlm) algorithm. The ability of Odorata to extract TCP from water was tested using equilibrium, kinetic and thermodynamic studies. Thermodynamic studies showed that the adsorption of TCP by the new adsorbent is thermally feasible and is governed by a chemical adsorption mechanism. It was established that the experimental data fit the selected adsorption isotherms in the following order: Langmuir > Freundlich > Temkin > Dubinin-Radushkevich (D-R). Kinetic modelling was done using intra-particle diffusion, liquid-film, pseudo-first order and pseudo-second order models. With the aid of the normalised standard deviation, the pseudo-second order was found to be the appropriate rate expression for the adsorption data. Liquid-film diffusion was the rate-determining stage of the adsorption process.

Keywords: Chromolaena odorata; TCP; adsorption; table salt; ANN