Design optimization of bimetal-modified biochar for enhanced phosphate removal performance in livestock wastewater using machine learning

Abstract

Mg-modified biochar shows high adsorption performance under weakly acidic and neutral water conditions. However, its phosphate removal efficiency markedly decreases in naturally alkaline wastewater, such as that released in livestock farming (anaerobic wastewater with a high phosphate concentration). This research employed six machine learning models to predict and optimize the phosphate removal performance of bimetal-modified biochar (i.e., Mg–Ca/Al/Fe/La) to develop material design strategies suitable for achieving high removal efficiency in alkaline wastewater. Random forest, gradient boosting regressor, and extreme gradient boosting models achieved high prediction accuracy (R2 > 0.98). Model predictions and experimental validations indicated that Mg–Ca-modified biochar still maintained high adsorption capacity under acidic conditions and could effectively realize phosphate adsorption under alkaline conditions, with a removal rate of 99.33 %. Overall, this research focuses on material performance optimization using machine learning, offering insights and methods for developing biochar materials for practical water-treatment applications.