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Two-way Valorization of Blast Furnace Slag: Synthesis of Precipitated Calcium Carbonate and Zeolitic Heavy Metal Adsorbent
Published on: February 21, 2017
Combining equilibrium and dynamical models to describe two-component adsorption in a fixed bed
A Valverde1, T G Myers2, A Cabrera-Codony3
1Department of Chemical Engineering, Universitat Politècnica de Catalunya, Escola Superior d'Enginyeries Industrial, Aeroespacial i Audiovisual de Terrassa, 08222 Terrassa, Spain.
Abstract:
We introduce a novel approach for predicting fixed-bed adsorption involving two competing contaminants. The method leads to simple analytical expressions for the concentrations and amount adsorbed throughout the column during an experiment. It primarily relies on equilibrium (isotherm) data, with just a single parameter fitted to breakthrough data. The advantage of employing the isotherm is that the coefficients are obtained using a range and combination of concentrations ensuring a wide range of validity. The isotherm analysis provides key information such as shifts in breakthrough time due to competition, the magnitude of the roll-up effect and relations between adsorption and displacement rates. From the breakthrough data we are able to determine the coefficient describing the rate at which the dominant species replaces the other species and from this all other coefficients may be calculated. Although the dynamic model neglects desorption it is accounted for through the isotherm analysis. The method is validated against novel and literature-based competitive column experiments, where the isotherm-based predictions reproduce full curves with R2>0.95 and a low relative error. Compared to a previous travelling wave approximation, on which this work is based, the present approach yields similar accuracy with the added advantage of providing physically interpretable parameters and allowing predictions of half-times and roll-up height over a wider range of validity. This may then be used to reduce experimental effort while maintaining strong predictive performance.
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