Work

Ultra-low-temperature activated sugarcane bagasse for rapid congo red adsorption: optimization and machine learning modeling

Year: 2026
Type: article
Abstract: This study reports the synthesis of a sustainable activated carbon from sugarcane bagasse via a sulfuric acid activation process conducted at ambient temperature (20°C), thereby eliminating the need for energy-intensive thermal treatment. The resulting adsorbent, denoted as AC–SCB, exhibited a moderate BET surface area of 159 ± 8 m 2 /g while showing ultrafast adsorption kinetics, achieving more than 95 % Congo Red (CR) removal within 2 min. Surface characterization revealed the presence of oxygen-containing functional groups, including carboxylic and phenolic moieties, which may contribute to CR adsorption through electrostatic interactions, hydrogen bonding, and π–π interactions at the solid–liquid interface. Equilibrium adsorption data were well described by the Langmuir model, with a maximum adsorption capacity of 1084.6 mg/g, while both pseudo-first-order and pseudo-second-order models adequately described the adsorption kinetics, showing only marginal differences in their statistical performance. Thermodynamic analysis indicated that the adsorption process is spontaneous and exothermic. Process optimization using Box–Behnken design achieved a maximum dye removal efficiency of 98.7 ± 0.4 % under optimal conditions (pH 2.0, adsorbent dosage of 0.508 g/L, and contact time of 14.8 min). Among the predictive models investigated, the optimized SVM model exhibited the highest predictive accuracy (R = 0.995, MSE = 0.192 and RMSE = 0.438), outperforming both the ANN and response surface methodology models. These findings demonstrate a green and low-energy pathway for converting agricultural waste into an efficient adsorbent. The adsorption performance is attributed to the combined effects of surface chemistry, porosity, and solution conditions rather than a single dominant factor.
Language: English
Open Access status: gold
Estimated APC paid: $590
OpenAlex ID: W7203549600
FWCI:
Cites: 52
Cited by:
Related to: 10