Continuous fixed-bed adsorption of cationic dyes by starch nanocrystals with breakthrough modeling of competitive interactions in multicomponent systems
Abstract
The removal of synthetic dyes from industrial wastewater remains a major environmental challenge due to their persistence and toxicity. This study investigated starch nanocrystals (SNCs), a carbohydrate-based biological macromaterial, as a sustainable biosorbent for the continuous removal of malachite green (MG), methylene blue (MB), and crystal violet (CV) from single-, binary-, and ternary-component systems using fixed-bed column adsorption. Breakthrough behavior was evaluated using six models, including Thomas, Yoon–Nelson, Adams–Bohart, mass-transfer, dose–response, and bed-depth service time (BDST) models. Model performance was assessed using the coefficient of determination (R ² ) and normalized root mean square error (NRMSE). SNCs exhibited high adsorption affinity toward all three dyes under single-component conditions, with removal efficiencies of 70.87–71.88% and saturation capacities ranging from 5.16 to 5.44 mg/g. Competitive adsorption in binary and ternary systems reduced removal efficiency (59.24–73.01%), adsorption capacity (3.67–4.95 mg/g), and column service time (1772.73–1942.86 min) relative to single-component systems. However, the extent of performance reduction varied among different dye combinations, with systems containing MB and CV exhibiting less severe reductions than other mixed-dye systems, indicating composition-dependent competitive interactions rather than net synergistic effects. Among the evaluated models, the dose–response and BDST models provided the most accurate predictions across all adsorption systems (R ² > 0.99, NRMSE = 3.00–6.02%). The Thomas model also showed good fit quality (R ² = 98.78–99.46%) but exhibited variable NRMSE values (5.25–9.21%), with the highest errors observed in binary systems, indicating that its accuracy is system-dependent and its parameters have limited interpretability under competitive conditions. The Yoon–Nelson model showed moderate predictive capability (R ² = 97.92–99.15%, NRMSE = 6.10–12.51%), while the Adams–Bohart and mass-transfer models exhibited the highest NRMSE values (8.65–32.89% and 21.03–25.71%, respectively), indicating limited applicability under multicomponent conditions. A statistical regression framework incorporating dummy variables and interaction effects was developed to quantify the combined influence of adsorption time (quantitative variable) and system composition (qualitative variable: single-, binary-, and ternary-component systems) on breakthrough behavior. The model structure includes linear and quadratic time terms, categorical dummy variables representing different dye systems, and interaction terms (time × system composition) to account for composition-dependent adsorption kinetics. This framework successfully described competitive adsorption behavior and explained more than 98% of the variability in the experimental data (R ² = 98.02%, adjusted R ² = 97.72%, NRMSE = 0.053%), with the interaction terms revealing that the effect of adsorption time on breakthrough behavior was significantly modulated by system composition, particularly in mixed-dye systems containing MB and CV. The results demonstrate the potential of SNCs as environmentally friendly adsorbents for dye-contaminated wastewater and provide useful insights for the design and optimization of fixed-bed adsorption systems operating under realistic multicomponent conditions.
Related
- Multicomponent adsorption of heavy metals: competitive interaction and critical evaluation of reusability of magnetic activated carbon.
- An accurate and efficient framework for modeling multimetal competitive adsorption on clay minerals
- Phase transitions in multicomponent systems relevant to atmospheric new particle formation
- Competitive adsorption mechanisms of Cd( ii ), Cu( ii ) and Pb( ii ) on bioinspired mesoporous silica revealed by complementary adsorption/isothermal titration calorimetry studies
- A fully convolutional neural network to classify UXOs using multicomponent electromagnetic induction data
BibTeX
@article{bahrami2026continuous,
title = {{Continuous fixed-bed adsorption of cationic dyes by starch nanocrystals with breakthrough modeling of competitive interactions in multicomponent systems}},
author = {Mehdi Bahrami and Mohammad Javad Amiri and Zahra Rahsepar Chargi and Mohammad Reza Mahmoudi},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76180-x},
url = {https://www.nature.com/articles/s41598-026-76180-x},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Continuous fixed-bed adsorption of cationic dyes by starch nanocrystals with breakthrough modeling of competitive interactions in multicomponent systems
AU - Mehdi Bahrami
AU - Mohammad Javad Amiri
AU - Zahra Rahsepar Chargi
AU - Mohammad Reza Mahmoudi
PY - 2026
DA - 2026/10/25
JO - Scientific Reports
DO - 10.1038/s41598-026-76180-x
UR - https://www.nature.com/articles/s41598-026-76180-x
AB - The removal of synthetic dyes from industrial wastewater remains a major environmental challenge due to their persistence and toxicity. This study investigated starch nanocrystals (SNCs), a carbohydrate-based biological macromaterial, as a sustainable biosorbent for the continuous removal of malachite green (MG), methylene blue (MB), and crystal violet (CV) from single-, binary-, and ternary-component systems using fixed-bed column adsorption. Breakthrough behavior was evaluated using six models, including Thomas, Yoon–Nelson, Adams–Bohart, mass-transfer, dose–response, and bed-depth service time (BDST) models. Model performance was assessed using the coefficient of determination (R ² ) and normalized root mean square error (NRMSE). SNCs exhibited high adsorption affinity toward all three dyes under single-component conditions, with removal efficiencies of 70.87–71.88% and saturation capacities ranging from 5.16 to 5.44 mg/g. Competitive adsorption in binary and ternary systems reduced removal efficiency (59.24–73.01%), adsorption capacity (3.67–4.95 mg/g), and column service time (1772.73–1942.86 min) relative to single-component systems. However, the extent of performance reduction varied among different dye combinations, with systems containing MB and CV exhibiting less severe reductions than other mixed-dye systems, indicating composition-dependent competitive interactions rather than net synergistic effects. Among the evaluated models, the dose–response and BDST models provided the most accurate predictions across all adsorption systems (R ² > 0.99, NRMSE = 3.00–6.02%). The Thomas model also showed good fit quality (R ² = 98.78–99.46%) but exhibited variable NRMSE values (5.25–9.21%), with the highest errors observed in binary systems, indicating that its accuracy is system-dependent and its parameters have limited interpretability under competitive conditions. The Yoon–Nelson model showed moderate predictive capability (R ² = 97.92–99.15%, NRMSE = 6.10–12.51%), while the Adams–Bohart and mass-transfer models exhibited the highest NRMSE values (8.65–32.89% and 21.03–25.71%, respectively), indicating limited applicability under multicomponent conditions. A statistical regression framework incorporating dummy variables and interaction effects was developed to quantify the combined influence of adsorption time (quantitative variable) and system composition (qualitative variable: single-, binary-, and ternary-component systems) on breakthrough behavior. The model structure includes linear and quadratic time terms, categorical dummy variables representing different dye systems, and interaction terms (time × system composition) to account for composition-dependent adsorption kinetics. This framework successfully described competitive adsorption behavior and explained more than 98% of the variability in the experimental data (R ² = 98.02%, adjusted R ² = 97.72%, NRMSE = 0.053%), with the interaction terms revealing that the effect of adsorption time on breakthrough behavior was significantly modulated by system composition, particularly in mixed-dye systems containing MB and CV. The results demonstrate the potential of SNCs as environmentally friendly adsorbents for dye-contaminated wastewater and provide useful insights for the design and optimization of fixed-bed adsorption systems operating under realistic multicomponent conditions.
SN - 2045-2322
ER -