Competitive Adsorption of Organic Micropollutants in the Aqueous Phase onto Activated Carbon Cloth: Comparison of the IAS Model and Neural Networks in Modeling Data

Abstract

The adsorption equilibria of organic micropollutants (phenol, benzoic acid, and para-chlorophenol) onto activated carbon cloths have been determined for a large range of concentrations (from 10-5 to 0.5 mmol L-1) to characterize adsorption mechanisms. Single-solute isotherms tend to confirm the decisive role of the adsorbent's microporosity in the adsorption capacity of the activated carbons. Moreover, it was found that the hydrophobicities and solubilities of adsorbates have a significant influence on the adsorption energy and capacity, respectively. Binary solute isotherms confirm these conclusions, and experimental data were used to investigate two different approaches to competitive modeling: a deterministic model, the ideal adsorbed solution theory, and a statistical one, neural networks. Both models gave good agreement between experimental and predicted data in some cases, but the results also emphasized the difficulty in satisfying the respective hypotheses of these models.