Predicting thermal desorption efficiency of PAHs in contaminated sites based on an optimized machine learning approach.

Thermal desorption (TD) remediation of polycyclic aromatic hydrocarbon (PAH)-contaminated sites is known for its high energy consumption and cost implications. The key to solving this issue lies in analyzing the PAHs desorption process, defining remediation endpoints, and developing prediction models to prevent excessive remediation. Establishing an accurate prediction model for remediation efficiency, which involves a systematic consideration of soil properties, TD parameters, and PAH characteristics, poses a significant challenge. This study employed a machine learning approach for predicting the remediation efficiency based on batch experiment results. The results revealed that the extreme gradient boosting (XGB) model yielded the most accurate predictions (R2 = 0.9832). The importance of features in the prediction process was quantified. A model optimization scheme was proposed, which involved integrating features based on their relevance, importance, and partial dependence. This integration not only reduced the number of input features but also enhanced prediction accuracy (R2 = 0.9867) without eliminating any features. The optimized XGB model was validated using soils from sites, demonstrating a prediction error of less than 30%. The optimized XGB model aids in identifying the most optimal conditions for thermal desorption to maximize the remediation efficiency of PAH-contaminated sites under relative cost and energy-saving conditions.