Assessing the potential of machine learning methods to study the removal of pharmaceuticals from wastewater using biochar or activated carbon

Abstract

Pharmaceuticals in wastewater are rapidly becoming new emerging pollutants, affecting humans and the aquatic ecosystem, and can go undetected due to their microscopic nature. Adsorption proves to be a promising technology for the removal of pharmaceuticals from effluent wastewater owning to its low cost, flexibility, and renewability. Adsorbents are porous materials such as silica, clay, resins, and carbon-based materials (e.g., biochars, carbon nanotubes, and activated carbon) often used to remove pharmaceutical micropollutants during adsorption. Among them biochar is an emerging, cost-effective, and eco-friendly sorbent. Modeling methods such as linear correlativity and multilinear regressions, are often employed to explain the adsorption mechanism, however they show limited accuracy and applicability. On the contrary, data driven machine learning (ML) methods is a powerful tool that could be used to study the complex relationship between adsorption performances and biochar properties. This review provides an overview of recent advances in the use of machine learning (ML) methods to explore the field of pharmaceutical adsorption onto biochar. An introduction to different ML algorithms and their advantages and limitations is provided. Furthermore, the challenges and future prospects of ML applications to study the adsorption mechanism is outlined.