Long-chain perfluoroalkyl carboxylic acids removal by biochar: Experimental study and uncertainty based data-driven predictive model
Sepideh Nasrollahpour, Amin Tanhadoust, Rama Pulicharla, Satinder Kaur Brar
Abstract
Given the persistence and toxicity of long-chain perfluoroalkyl carboxylic acids (PFCAs) and their rising concentrations, there is an urgent need for effective removal strategies. This study investigated the adsorptive removal of PFCAs, specifically perfluorononanoic acid (PFNA) and perfluorodecanoic acid (PFDA), using biochar derived from wood and compost. Factors such as biochar size, weight, and initial PFCA concentrations were analyzed to assess their impact on adsorption efficiency over time. The adsorption of PFDA and PFNA reached 90.13% and 85.8%, respectively, at an initial concentration of 500 μg/L. Advanced machine learning techniques, specifically deep neural networks, were employed to model adsorption behavior, incorporating noise injection to account for data uncertainties and preventing overfitting. Results demonstrated the superior performance of compost-derived biochar due to its higher aromaticity and functional group availability. The longer chain length of PFDA contributed to its higher adsorption efficiency compared to PFNA.