Integration of RNN and CatBoost models in a tea-waste biochar filtration system for toxic organic pollutant removal efficiency prediction
Key Points
The integration of RNN and CatBoost models predicts pollutant removal efficiency effectively.
Filtration studies reveal up to 90% removal efficiency of pollutants from synthetic wastewater.
Application of tea-waste biochar enhances filtration performance across different temperatures.
The findings highlight the potential of using machine learning in environmental remediation strategies.
Abstract
Schematic representation of batch and column filtration studies using tea-waste-derived biochar at different temperature for simultaneous removal of organic pollutants from synthetic wastewater.