Machine learning predict the degradation efficiency of aqueous refractory organic pollutants by ultrasound-based advanced oxidation processes

Abstract

Ultrasound based advanced oxidation processes (AOPs) are effective for removing refractory organic pollutants by generating reactive species. Machine learning (ML) can systematically provide an excellent opportunity to determine the relationship between feature variables and output variables through large amounts of data, thereby reducing the need for experimental measurements. In this study, a ML approach was applied for the first time to predict the degradation efficiency of aqueous refractory organic pollutants by ultrasound-based AOPs. The obtained dataset was normalized and introduced into four ML models. Among them, Random Forest Regressor and XGB Regressor models had the best fit with 0.9546 and 0.9749 for training set R 2 and 0.9548 and 0.9740 for test set R 2 , respectively. Relative importance analysis and SHAP analysis showed that catalyst type, oxidant concentration, reaction time, and catalyst dosage are the important variables affecting the degradation efficiency of refractory organic pollutants. In addition, excessive ultrasonic intensity, ultrasonic frequency, oxidant concentration, and catalyst dosage had little effect on the increase of degradation efficiency. Based on the descriptive data analysis, high degradation efficiency was obtained with ultrasonic intensity of 2 W·mL −1 , ultrasonic frequency of 20–40 kHz, reaction time of 60–90 min, pH 3–7, oxidant concentration of ∼7.5 mmol·L −1 , and catalyst dosage of >0.4 g·L −1 . This work demonstrated a new predictive method to understand the comprehensive influence of operative factors on degradation efficiency in US-based AOPs, guiding parameter and process optimization. • ML predicts the efficacy of ultrasound-based AOPs to remove organic pollutants for the first time. • RFR and XG-Boost had the best model fit, with R 2 reaching above 0.9. • Catalyst type is the most important parameter affecting degradation efficacy. • Predictions using machine learning can save workload and costs.