Abstract
Herbicides and pesticides (H & P) are commonly used in agricultural practice and is a serious environmental pollutant in contemporary times. Studies have shown that it can be efficiently mitigated from the environment by adsorption. The aim of this study was to utilise Artificial Neural Networks (ANN) to model the adsorption of H & P from aqueous media based on the sorbate-sorbent interphase. The sorbate-sorbent interphase was characterised by the relative molecular mass (g/mol), specific surface area (m2/g), effective surface area (mol/m2), solubility (mol/l), and preferential adsorption (sorbate mol on sorbent/sorbate mol in solution). The coefficient of determination (R2) at training, validation and testing were 0.9825, 0.9428 and 0.9793 respectively. The accuracy of the model was substantiated by direct comparison and parity plots. The paired samples correlation showed a strong positive correlation (0.980) and statistical significance (p < 0.05) between the model predictions and experimental results. This study reveals that information on the adsorbent specific surface area, adsorbent effective surface area, adsorbate preferential adsorption, adsorbate solubility and adsorbate relative molecular mass can be used to accurately predict the mass adsorption capacity for any H & P from aqueous media.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
