Machine learning-driven prediction of phosphorus removal performance of metal-modified biochar and optimization of preparation processes considering water quality management objectives

Developing an optimized and targeted design approach for metal-modified biochar based on water quality conditions and management is achievable through machine learning. This study leveraged machine learning to analyze experimental data on phosphate adsorption by metal-modified biochar from literature published in Web of Science. Using six machine learning models, the phosphate adsorption capacity of biochar and residual phosphate concentration were predicted. After hyperparameter optimization, the gradient boosting model exhibited superior training performance (R