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<p>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 (<i>q</i><sub><i>b</i></sub>) to that under fixed-bed conditions (<i>q</i><sub><i>fb</i></sub>), under identical operating conditions. A theoretical efficacy factor (r<i>ₜₕₑₒ</i>) was also derived using the Langmuir model, and its close agreement with r<i>ₑₓₚ</i> confirms the reliability of the approach for assessing and comparing adsorption process efficiency. Performance assessment was based on the final pollutant concentration, <i>C</i><sub><i>fc</i></sub>, defined as the critical concentration at which equal masses of adsorbent are used in both systems. The two processes were evaluated by comparing <i>C</i><sub><i>fc</i></sub> values against a defined threshold limit of 5.0&#xa0;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&#xa0;mg/g in batch mode and 5.0–920.0&#xa0;mg/g in fixed-bed mode. Among them, polymeric resin showed the highest retention, with capacities of 1076.0&#xa0;mg/g (batch) and 917.0&#xa0;mg/g (fixed-bed). Although r<i>ₑₓₚ</i> values suggested that the fixed-bed process was effective in many cases, the batch process showed a clear advantage where the <i>C</i><sub><i>fc</i></sub> 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.</p> Graphical Abstract <p></p>

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Engineering sustainable adsorption processes: batch vs. fixed-bed systems for pollutant removal

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • Khaled Al-Zawahreh,
  • Ahmad B. Albadarin,
  • Chirangano Mangwandi,
  • Esam Ayed Wshah

摘要

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.

Graphical Abstract