The recovery of nitrogen and phosphorus from simulated wastewater in the form of struvitewas investigated through a Machine Learning (ML) -based approach. The Extreme Gradient BoostingAlgorithm (XGBoost) and Random Forest (RF) models were used for single-objective and multi-ob⁃jective prediction of the recovery rates of N and P, respectively. The effects of seven process conditionson struvite crystallization were identified. The results showed that XGBoost outperformed RF in both sin⁃gle-objective (R2 = 0.91 ~ 0.93) and multi-objective (R2 = 0.89) predictions. Furthermore, experi⁃mental validation was conducted with initial phosphorus concentrations of 10 mg / L and 1 000 mg / L todetermine the optimized process conditions for struvite recovery using the multi-objective model. Theoptimal conditions were found to be: N ∶ P ratio of 1.2 ∶ 1, Mg ∶ P ratio of 1 ∶ 1, pH of 9.5, reactiontime of 80 min, reaction temperature of 25 ℃, and stirring rate of 240 r/ min.
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