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Outline
In order to improve the level of on-line detection of ash in coal slime flotation process, the prediction of ash content of tailings in coal slime flotation process was studied, and the prediction model of ash content of flotation tailings based on LightGBM optimized by genetic algorithm was established. Through the collected production data, including slurry flow, concentration, foaming dose, collection dose and dry slime amount, the model training data set was constructed and the model was tested and verified. The experimental results show that the average absolute error of the prediction results of the model is 0.72, which is 11.1% higher than that of the unoptimized LightGBM model. Compared with other models, the average absolute error of the prediction results of the GA-LightGBM model is reduced by 15.8%, which further proves the validity of the model in the prediction accuracy of ash in coal tailings and provides a new technical support for the realization of intelligent flotation.
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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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