Sugarcane bagasse-MnFe2O4 magnetic biochar composite for ammonium removal: isotherms, kinetics, RSM optimization and machine learning prediction
- 1Department of Chemistry, Hanoi Pedagogical University 2 32 Nguyen Van Linh, Xuan Hoa Phu Tho Vietnam
- 2Center for Advanced Technology Development, Thai Nguyen University Thai Nguyen Vietnam
- 3Institute for Excellence in Education and Research, Thai Nguyen University Thai Nguyen Vietnam
Abstract
Ammonium (NH4+) pollution in wastewater poses severe ecological and health risks. This study introduces a magnetic biochar composite (SB@MFO) synthesized from sugarcane bagasse and MnFe2O4 for efficient NH4+ removal. The composite exhibits a mesoporous structure (BET surface area: 56.58 m2 g−1; average pore diameter: 11.26 nm) and contains abundant oxygen-containing functional groups that facilitate adsorption. Response surface methodology based on the Box–Behnken design was employed to optimize the adsorption conditions. Under the optimum conditions (MFO : SB = 1 : 3, pH = 7, contact time = 20 min, and adsorbent dosage = 0.03 g·50 mL−1), the experimentally determined adsorption capacity reached 46.66 mg g−1. Equilibrium adsorption was satisfactorily described by both the Langmuir and Temkin isotherm models. The Langmuir model yielded the highest coefficient of determination (R2 = 0.9812) and a maximum adsorption capacity of 47.80 ± 1.72 mg g−1, while the Temkin model exhibited the lowest RMSE, χ2, SSE, and AIC values among the evaluated models. The adsorption kinetics were adequately described by the pseudo-second-order (R2 = 0.9564) and Elovich (R2 = 0.9052) models, suggesting the involvement of surface-controlled adsorption processes. Combined characterization and adsorption-modelling results suggested that NH4+ uptake by SB@MFO may involve the contribution of electrostatic attraction, ion exchange, surface-mediated interactions involving oxygen-containing functional groups, and physical adsorption within the mesoporous structure. Three machine-learning models were developed to explore the adsorption capacity, with the support vector machine (SVM) model showing the best performance within the investigated dataset (R2 = 0.994 and RMSE = 1.558). Feature-importance analysis identified the initial NH4+ concentration and adsorbent dosage as the most influential predictive variables. The SB@MFO composite retained 75.8% of its initial adsorption capacity after five adsorption–desorption cycles, demonstrating acceptable reusability. Owing to its adsorption performance, magnetic recoverability, and agricultural waste origin, SB@MFO shows promising potential for ammonium removal from aqueous systems. Further studies using real wastewater matrices and continuous-flow conditions are recommended to evaluate its practical applicability.
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BibTeX
@article{do2026sugarcane,
title = {{Sugarcane bagasse-MnFe2O4 magnetic biochar composite for ammonium removal: isotherms, kinetics, RSM optimization and machine learning prediction}},
author = {Thuy-Tien Do and Phuong-Anh Nguyen and Huu-Tap Van},
journal = {RSC advances},
year = {2026},
month = oct,
doi = {10.1039/d6ra04526j},
url = {https://europepmc.org/article/PMC/PMC13673666},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Sugarcane bagasse-MnFe2O4 magnetic biochar composite for ammonium removal: isotherms, kinetics, RSM optimization and machine learning prediction
AU - Thuy-Tien Do
AU - Phuong-Anh Nguyen
AU - Huu-Tap Van
PY - 2026
DA - 2026/10/16
JO - RSC advances
DO - 10.1039/d6ra04526j
UR - https://europepmc.org/article/PMC/PMC13673666
AB - Ammonium (NH4+) pollution in wastewater poses severe ecological and health risks. This study introduces a magnetic biochar composite (SB@MFO) synthesized from sugarcane bagasse and MnFe2O4 for efficient NH4+ removal. The composite exhibits a mesoporous structure (BET surface area: 56.58 m2 g−1; average pore diameter: 11.26 nm) and contains abundant oxygen-containing functional groups that facilitate adsorption. Response surface methodology based on the Box–Behnken design was employed to optimize the adsorption conditions. Under the optimum conditions (MFO : SB = 1 : 3, pH = 7, contact time = 20 min, and adsorbent dosage = 0.03 g·50 mL−1), the experimentally determined adsorption capacity reached 46.66 mg g−1. Equilibrium adsorption was satisfactorily described by both the Langmuir and Temkin isotherm models. The Langmuir model yielded the highest coefficient of determination (R2 = 0.9812) and a maximum adsorption capacity of 47.80 ± 1.72 mg g−1, while the Temkin model exhibited the lowest RMSE, χ2, SSE, and AIC values among the evaluated models. The adsorption kinetics were adequately described by the pseudo-second-order (R2 = 0.9564) and Elovich (R2 = 0.9052) models, suggesting the involvement of surface-controlled adsorption processes. Combined characterization and adsorption-modelling results suggested that NH4+ uptake by SB@MFO may involve the contribution of electrostatic attraction, ion exchange, surface-mediated interactions involving oxygen-containing functional groups, and physical adsorption within the mesoporous structure. Three machine-learning models were developed to explore the adsorption capacity, with the support vector machine (SVM) model showing the best performance within the investigated dataset (R2 = 0.994 and RMSE = 1.558). Feature-importance analysis identified the initial NH4+ concentration and adsorbent dosage as the most influential predictive variables. The SB@MFO composite retained 75.8% of its initial adsorption capacity after five adsorption–desorption cycles, demonstrating acceptable reusability. Owing to its adsorption performance, magnetic recoverability, and agricultural waste origin, SB@MFO shows promising potential for ammonium removal from aqueous systems. Further studies using real wastewater matrices and continuous-flow conditions are recommended to evaluate its practical applicability.
SN - 2046-2069
ER -