Prediction of micropollutant degradation kinetic constant by ultrasonic using machine learning

A prediction model based on XGBoost is proposed for ultrasonic degradation of micropollutants' kinetic constants. After parameter optimization through iteration, the model achieves Evaluation metrics with R² and SMAPE reaching 0.99 and 2.06%, respectively. The impact of design parameters on predicting kinetic constants for ultrasound degradation of trace pollutants was assessed using Shapley additive explanations (SHAP). Results indicate that power density and frequency significantly impact the predictive performance. The database was sorted based on power density and frequency values. Subsequently, 800 raw data were split into small databases of 200 each. After confirming that reducing the database size doesn't affect prediction accuracy, ultrasound degradation experiments were conducted for five pollutants, yielding experimental data. A small database with experimental conditions within the numerical range was selected. Data meeting both feature conditions were filtered, resulting in an optimized 60-data group. After incorporating experimental data, a model was trained for prediction. Degradation kinetic constants for experiments (k_E) were compared with predicted constants (for 800 data-based model: k_P-800 and for 60 data-based model: k_P-60). Results showed ibuprofen, bisphenol A, carbamazepine, and 17β-Estradiol performed better on the 60-data group (k_P-60/k_E: 1.00, 0.99, 1.00, 1.00), while caffeine suited the model trained on the 800-data group (k_P-800/k_E: 1.02).