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Updated: Jul 18, 2026

Two-way Valorization of Blast Furnace Slag: Synthesis of Precipitated Calcium Carbonate and Zeolitic Heavy Metal Adsorbent
Published on: February 21, 2017
Three-component competitive adsorption model for fixed-bed and moving-bed granular activated carbon adsorbers. Part
Lance C Schideman1, Benito J Mariñas, Vernon L Snoeyink
1Department of Civil & Environmental Engineering and Center of Advanced Materials for the Purification of Water with Systems, University of Illinois, Urbana, Illinois 61801, USA.
Abstract:
Heterogeneous natural organic matter (NOM) present in all natural waters impedes trace organic contaminant adsorption, and predictive modeling of granular activated carbon (GAC) adsorber performance is often compromised by inadequate accounting forthese competitive effects. Thus, a 3-component adsorption model, COMPSORB-GAC, is developed that separately tracks NOM adsorption and its competitive effects as a function of NOM surface loading. In this model, NOM is simplified into two fictive fractions with distinct competitive effects on trace compound adsorption: a smaller, strongly competing fraction that reduces equilibrium capacity and a larger pore-blocking fraction that reduces adsorption kinetics (both external film mass transfer and surface diffusion). COMPSORB-GAC tracks these two NOM fractions, along with the trace compound, and changes adsorption parameters according to the local surface loading of the two NOM fractions. Model parameters are allowed to vary both temporally and spatially to reflect differences in the NOM preloading conditions that occur in GAC columns. This dual-resistance model is based on homogeneous surface diffusion with external film mass-transfer limitations. The governing equations are expressed in a moving-grid finite-difference formulation to accommodate the modeling of spatially varying parameters and moving-bed reactors with counter-current adsorbent flow. A series of short-term adsorption tests with fresh and preloaded GAC is proposed to determine the necessary model input parameters. The accompanying manuscript demonstrates the parameterization procedure and verifies the model with experimental data.
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