Real-time pH monitoring and machine learning prediction of phosphorus precipitation kinetics in urine systems

• Integrate real-time pH monitoring with machine learning for phosphorus precipitation analysis. • Elucidate the interactions of Ca(OH) 2 , MgO, and urine components during precipitation • Identify inhibition mechanisms from mixed precipitants and carbonate intermediates. • Provide a framework for understanding phosphorus dynamics in urine-based systems. Urine, as a primary source of phosphorus in wastewater, has a complex composition, where dynamic interactions among components during storage and treatment significantly affect phosphorus precipitation. However, it is difficult for traditional analytical methods to capture these rapidly evolving processes in real-time, leaving the kinetics of phosphorus precipitation in multi-component systems poorly understood. In this work, real-time pH monitoring was employed in a controlled simulated urine system to effectively identify the initiation and termination of phosphorus precipitation reactions. Based on the pH time-series data, an XGBoost machine learning model was developed to predict precipitation kinetics with high accuracy and further validated using real urine samples. Moreover, Shapley Additive Explanations analysis quantified the contributions and interactions of multiple factors, and experimental validation uncovered the interaction pathways of calcium and magnesium precipitation under high urea hydrolysis conditions. Overall, this work highlights pH as a sensitive indicator of chemical dynamics and introduces a data-driven framework for understanding phosphorus precipitation in complex multi-component environments, offering valuable insights into phosphorus precipitation kinetics with implications for future wastewater treatment.