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Published on: December 5, 2019
Engineering sustainable adsorption processes: batch vs. fixed-bed systems for pollutant removal
Khaled Al-Zawahreh1, Ahmad B Albadarin2, Chirangano Mangwandi3
1Department of Earth Sciences and Environment, Talal Faculty for Natural Resources and Environment, The Hashemite University, Prince El-Hassan Bin, P.O. Box 330127, Zarqa, 13133, Jordan.
Abstract:
In this work, we present a practical framework for selecting between batch and fixed-bed adsorption processes to achieve efficient pollutant removal while minimizing adsorbent consumption. To evaluate performance, we introduce an experimental efficacy factor (rₑₓₚ), defined as the ratio of adsorption capacity under batch conditions (qb) to that under fixed-bed conditions (qfb), under identical operating conditions. A theoretical efficacy factor (rₜₕₑₒ) was also derived using the Langmuir model, and its close agreement with rₑₓₚ confirms the reliability of the approach for assessing and comparing adsorption process efficiency. Performance assessment was based on the final pollutant concentration, Cfc, defined as the critical concentration at which equal masses of adsorbent are used in both systems. The two processes were evaluated by comparing Cfc values against a defined threshold limit of 5.0 mg/L. The model's validity was tested using data from 31 different adsorbents assessed under both batch and fixed-bed conditions. The screened adsorbents-including activated carbon, chitosan, resin, collagen, and a wide range of bioadsorbents-demonstrated high dye uptake capacities, ranging from 30.0-1070.0 mg/g in batch mode and 5.0-920.0 mg/g in fixed-bed mode. Among them, polymeric resin showed the highest retention, with capacities of 1076.0 mg/g (batch) and 917.0 mg/g (fixed-bed). Although rₑₓₚ values suggested that the fixed-bed process was effective in many cases, the batch process showed a clear advantage where the Cfc values in batch systems were consistently below the threshold, indicating more effective pollutant removal. The proposed model provides a valuable framework for selecting appropriate adsorption methods, particularly when synthetic adsorbents are costly. Overall, it supports informed decision-making and process optimization in adsorption-based pollutant removal.
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