College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou, Zhejiang, 310058, China.
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou, Zhejiang, 310058, China.
Environ Pollut. 2024 Apr 1;346:123667. doi: 10.1016/j.envpol.2024.123667. Epub 2024 Feb 28.
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 (R = 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 (R = 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.
热脱附（TD）修复多环芳烃（PAH）污染场地，其能耗和成本高是众所周知的。解决这一问题的关键在于分析 PAHs 脱附过程，定义修复终点，并开发预测模型，以防止过度修复。建立准确的修复效率预测模型，需要系统考虑土壤特性、TD 参数和 PAH 特征，这是一个重大挑战。本研究采用机器学习方法，根据批量实验结果预测修复效率。结果表明，极端梯度提升（XGB）模型的预测结果最为准确（R=0.9832）。对预测过程中特征的重要性进行了量化。提出了一种模型优化方案，该方案基于特征的相关性、重要性和偏依赖性对特征进行集成。这种集成不仅减少了输入特征的数量，而且提高了预测精度（R=0.9867），而没有消除任何特征。使用来自现场的土壤对优化后的 XGB 模型进行了验证，预测误差小于 30%。优化后的 XGB 模型有助于确定热脱附的最优化条件，以在相对成本和节能条件下，最大限度地提高 PAH 污染场地的修复效率。
