Modelling diffusion and adsorption of As species in Fe/GAC adsorbent beds

M. SIGRIST; H. BELDOMÉNICO; E.E. TARIFA; C.L. PIECK; C.R. VERA

JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY

JOHN WILEY & SONS LTD

Lugar: Hoboken, New Jersey; Año: 2011

0268-2575

BACKGROUND                Arsenic decontamination of drinking water by adsorption conveniently features ease and robustness of operation.  When designing packed bed adsorbers, the main problems are the slow diffusion kinetics of As in microporous media and the lack of simple equations for predicting the performance of the equipment. Commercial iron-doped granular activated carbon adsorbents (Fe/GAC) for groundwater arsenic abatement were studied in this work.  Basic parameters for arsenate (AsV) adsorption were measured and their performance at a larger scale was simulated with an approximate analytical model. RESULTS In the 0-300 mgAs L-1 range, the AsV adsorption isotherm on Fe/GAC was found to be approximately linear.  Assuming Henry’s law for adsorption and homogeneous surface diffusion with constant diffusivity for intrapellet mass transfer, an approximate model for flow and adsorption of arsenate inside packed bed adsorbers was written, solved and reduced to an analytic compact solution using the quasi lognormal distribution (Q-LND) approximation.   The use of this model with fitted and reported parameters enabled the approximate simulation of industrial adsorbers and home point-of-use filters.  The results show that industrial adsorbers meet the breakthrough condition with incomplete utilization of the adsorbent unless convenient process configurations are used.  In point-of-use systems with short residence times intraparticle diffusion would drastically reduce the adsorbent performance.  CONCLUSION Assuming linear adsorption of AsV over Fe/GAC an analytical approximate solution for flow and adsorption in packed beds can be obtained.  The model seems to correctly represent the main features of industrial and home filters.  However more experimental data seems necessary for scaling-up purposes.