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Outline
Machine learning models, including multiple linear regression (MLR) and decision tree machine learning (LGB) algorithms, were used to predict the degradation efficiency of 57 organic pollutants in the Fenton process. Mechanistic interpretation of the models was performed by SHAP method to identify the key factors affecting the degradation efficiency. The results showed that the LGB model (R2adj = 0.969, Q2LOO = 0.925, R2ext = 0.844) outperformed the MLR model (R2adj = 0.831, Q2LOO = 0.802, R2ext = 0.861) in terms of prediction accuracy. The SHAP analysis revealed that temperature, molecular three-dimensional structure, and atomic ionization capacity were the main factors affecting the degradation efficiency. This study provides a scientific basis for optimizing the operating conditions of the Fenton process and enhancing the degradation efficiency, which is of great significance in guiding the research and practice in the field of water treatment.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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