Machine learning for optimizing Reactive Red 120 biosorption onto cross-linked gelatin/alginate-TTAB-montmorillonite composite.

A biodegradable thin-film composite comprising cross-linked gelatin/alginate and tetradecyltrimethylammonium bromide-modified montmorillonite was developed for the adsorption of Reactive Red 120 (RR120). Compared with our previously reported bulk composite, the thin-film architecture shortened diffusion pathways, thereby reducing internal mass-transfer resistance and improving adsorption-site accessibility. Batch adsorption experiments showed that the equilibrium adsorption capacity (qe) increased with contact time, initial dye concentration, and temperature, but decreased with increasing solution pH. Kinetic modeling using the pseudo-first-order, pseudo-second-order, and intraparticle diffusion models indicated that adsorption was governed by surface adsorption and intraparticle diffusion, whereas equilibrium data were best described by the Langmuir model with a maximum adsorption capacity (qm) of 46.79 mg/g. Regeneration using 0.5 M NaCl demonstrated moderate reusability. Among the evaluated machine-learning algorithms, the optimized gradient boosting model achieved the highest predictive performance (test R2 = 0.9606, RMSE = 1.4265 mg/g) and predicted optimum operating conditions of 356 min, 277 mg/L, pH 2.8, and 41 °C. Experimental validation yielded an adsorption capacity of 45.23 ± 0.05 mg/g, with a relative prediction error of only 0.91%. These findings demonstrate that integrating biodegradable thin-film composites with established machine-learning techniques provides an effective strategy for adsorption-process optimization and textile wastewater treatment.