Advances on biochar applications for organic wastewater Treatment: Material design, removal mechanisms, innovative machine learning and challenges

Biochar has gained significant attention in organic wastewater treatment due to its cost-effectiveness, eco-friendliness, and high adsorption and catalytic properties. This study reviews the preparation and modification strategies for biochar, including the carbonization of biomass, activation, and heteroatom doping, as well as the development of composite materials based on biochar. The adsorption and degradation mechanisms for removing pollutants were emphasized, and the differences between the radical and non-radical pathways in the degradation process of advanced oxidation processes (AOP) were clarified. The applications for removing organic pollutants such as antibiotics, dyes, polycyclic aromatic hydrocarbons (PAHs), and endocrine-disrupting chemicals (EDCs) were also summarized. Especially, the review highlights the transformative role of machine learning (ML) in biochar, particularly in optimizing production, predicting pollutant removal efficiency, and guiding material design. ML models such as Random Forest (RF) and Artificial Neural Networks (ANN) have demonstrated high accuracy in predicting biochar performance based on pyrolysis conditions, biochar characteristics, and experimental parameters. Furthermore, the challenges and future research directions in the field of biochar treatment of organic wastewater were discussed. By promoting the integration of ML and biochar design and application, providing meaningful guidance for further development of biochar in organic wastewater treatment.