Optimizing the upcycling of microplastics to a carbon-based adsorbent for water treatment: An integrated experimental and computational approach

Abstract

Microplastics (MPs) are an escalating environmental hazard because they persist in aquatic ecosystems and resist removal by conventional water treatment technologies. A novel data-driven strategy that upcycles MPs into engineered carbonaceous adsorbents via hydrothermal carbonization (HTC) is presented. By systematically varying three synthesis variables – feedstock loading, acid type and acid concentration – a range of carbonaceous materials (CMs) was produced and evaluated for their ability to adsorb Reactive Orange 84 dye. An integrated full factorial design of experiments encompassing both, material synthesis variables (acid type, acid concentration, and polyester (PES) material concentration) and the application variables (CM dose) was implemented. Subsequent statistical analysis and PCA identified acid type, acid concentration, CM dose, and PES concentration as dominant factors controlling adsorption capacity (q_e) and removal percentage. To refine the optimization, several machine learning (ML) models – linear regression, support vector machines, ensemble methods, and neural networks – were trained on the experimental dataset. Acid treated CMs consistently outperformed those synthesized under neutral conditions, with optimal performance observed at moderate acid concentrations. The key innovation in this study lies in the integrated experimental-computational framework that models the entire process (synthesis −> application), coupling rigorous statistical screening with advanced ML prediction. This delivers actionable guidance for the rational design of acid-modified carbonaceous adsorbents and advances the upcycling of MPs for water treatment applications.