The removal of methylene blue from aqueous solutions by polyethylene microplastics: Modeling batch adsorption using random forest regression

Abstract

In light of the extensive contamination of water sources by microplastics, their substantial specific surface area makes them favorable candidates as adsorbents for the simultaneous removal of coexisting contaminants in wastewater. In this regard, polyethylene microplastics were utilized to eliminate methylene blue dye from water. MB adsorption onto microplastics reached equilibrium in just 30 min at pH 7. The better fit of fractional power and Redlich-Peterson models on kinetic and equilibrium adsorption data, respectively, revealed that the MB removal process is a chemisorption in multilayer adsorption on the heterogeneous surface of the microplastics particles. The reusability of the microplastics adsorbent was confirmed based on the promising outcomes observed after five cycles. The results of the random forest regression exhibited an R2 of 97.55% for the correlation between the model-computed and measured amounts of MB reduction. The sensitivity analysis illustrated that the MB sorption process on microplastics is highly influenced by the initial MB concentration and adsorbent mass. These results show that although microplastics may pose potential risks to water environments, their adsorption potential can be utilized to simultaneously omit other pollutants from the aqueous solutions.

Keywords

• Batch adsorption
• Methylene blue
• Microplastics
• Random Forest regression