Machine learning, conventional and statistical physics modeling of 2,4-Dichlorophenoxyacetic acid (2,4-D) herbicide removal using biochar prepared from Vateria indica fruit biomass
Document Type
Article
Publication Title
Chemosphere
Abstract
The adsorption properties of 2,4-Dichlorophenoxyacetic acid (2,4-D) onto biochar, obtained through HCl-assisted hydrothermal carbonization process of Vateria indica fruits (VI-BC), were extensively studied using traditional and statistical physics approaches. The traditional adsorption investigations encompassed kinetics, equilibrium, and thermodynamics studies. Subsequently, the Hill statistical physics model was employed to interpret the mechanism. Also, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) machine learning tools were successfully employed to model the adsorption data wherein both models had high prediction potential (R2 > 0.99). The outcomes demonstrated that the produced VI-BC exhibited remarkable adsorptive traits, having a considerable specific surface area (111.54 m2/g), pore size (5.89 nm), a variety of functional groups, and appropriate attributes for efficiently adsorbing 2,4-D. For 10 mg/L 2,4-D, at pH 2.0 and with 0.3 g/L dose, an impressive 91.67% adsorption efficiency was achieved within a 120-min. Pseudo-second-order model aptly depicted the kinetic behavior of 2,4-D adsorption, while the Freundlich model provided a more accurate representation of the isotherms. 2,4-D maximum adsorption capacity stood at 131.39 mg/g at 303 K. The Hill statistical physics model elucidated that the adsorption primarily occurred via physisorption mechanisms, involving electrostatic attractions, π-π conjugation, and pore filling. This conclusion was further substantiated by post-adsorption characterization of the VI-BC. Thermodynamic analysis indicated that the interactions between VI-BC and 2,4-D were favorable, spontaneous, and exothermic. The calculated low energy of adsorption (1.255 kJ/mol) and ΔH° value (−20.49 kJ/mol) further supported physisorption as the dominant mechanism. In summary, this study underscores the significant potential of the newly developed biochar as a promising alternative material for efficiently removing the 2,4-D herbicide from polluted environments.
DOI
10.1016/j.chemosphere.2024.141130
Publication Date
2-1-2024
Recommended Citation
Vinayagam, Ramesh; Nagendran, Vasundra; Goveas, Louella Concepta; and Narasimhan, Manoj Kumar, "Machine learning, conventional and statistical physics modeling of 2,4-Dichlorophenoxyacetic acid (2,4-D) herbicide removal using biochar prepared from Vateria indica fruit biomass" (2024). Open Access archive. 6911.
https://impressions.manipal.edu/open-access-archive/6911
