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In this work, 305 sets of data of hydrochar's basic properties was collected from the references. Then, the singletask and multitask prediction models of hydrochar's basic properties (mass yield, higher heating value, and carbon content) were established based on three types of the machine learning algorithms (the decision tree, the random forest, and the gradient boosting decision tree). Results showed that among the three types of the machine learning algorithms, the gradient boosting decision tree model was the best algorithm, where the average determination coefficient values of the test set were 0.88 and 0.87, and the root mean square error values were 0.34 and 0.37. The SHAP method was used to evaluate the input characteristic parameters during the modeling by using the gradient boosting decision tree. The dominant influence factors for the prediction of the mass yield, higher heating value, and carbon content of the hydrochar were the hydrothermal reaction temperature and the C content in raw biomass. The construction of the prediction model of the hydrochar's basic properties was favorable to reduce the cost for the optimization of the hydrochar production conditions.
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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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