Machine Learning Prediction of Phosphate Adsorption on Red Mud Modified Biochar Beads: Parameter Optimization and Experimental Validation

Designing phosphate adsorbents is often hindered by trial-and-error optimization that overlooks nonlinear coupling between preparation parameters and operational conditions. Here we present a unified, explainable machine-learning framework that links red mud modified biochar bead (RM/CSBC) preparation (red mud dosage, biomass dosage, and pyrolysis temperature) to operating variables (initial pH, reaction temperature, contact time, and initial phosphate concentration) and directly guides condition selection. Using 95 independent experiments, six regressors were trained and compared. Random Forest (RF) model demonstrated strong prediction accuracy, with R2 values of 0.916 for the training set and 0.892 for the test set. Support Vector Regression (SVR) model showed superior performance, achieving R2 values of 0.984 and 0.967 for training and test sets, respectively, with low RMSE (0.068 and 0.083) and PBIAS (5.41% and 6.86%). Feature importance analysis revealed red mud and biomass doses positively influenced phosphate adsorption, with surface active sites and phosphate concentration gradient playing significant roles. Experimental verification confirmed RF and SVR models provided accurate predictions under three representative conditions, with deviations between predictions and measurements of +0.66, +0.19, and −0.69 mg·g−1 for SVR and −1.08, −0.79, and −1.15 mg·g−1 for RF, offering reliable guidance for phosphate removal in wastewater using RM/CSBC. This work highlights the potential of using machine learning to optimize waste-based adsorbent materials for wastewater treatment, significantly reducing time and experimental costs.