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Outline
In order to reduce the energy consumption of ball mill, ZQM250×100 ball mill was used to study the grinding energy consumption of an iron ore after high-pressure roller grinding, and the energy required for the newly generated －0.074 mm grade product after grinding was used as the evaluation standard of grinding energy consumption. The effects of medium filling rate, ore addition quantity, water addition quantity and mill frequency on the energy consumption of the newly generated －0.074 mm product, the significance of the interaction between the factors and the optimal test conditions were analyzed and predicted by the response surface methodology, and the results were compared with the single factor test results. The optimum conditions obtained by single factor test are as follows: medium filling rate is 40%, ore addition quantity is 700 g, water addition quantity is 300 mL (mass concentration is 70%), mill frequency is 50 Hz, and energy consumption of the newly generated －0.074 mm grade product is 169.54 (kW·h)/t. The response surface methodology analysis show that the mill frequency has the greatest influence on the energy consumption of the newly generated －0.074 mm grade product, followed by the medium filling rate, water addition quantity, and ore addition quantity. The interaction between medium filling rate and mill frequency is the most obvious, followed by water addition quantity and mill frequency, ore addition quantity and water addition quantity, medium filling rate and ore addition quantity, ore addition quantity and mill frequency, medium filling rate and water addition quantity. The model prediction based on the response surface methodology shows that the optimum test conditions are: 39.71% medium filling rate, 698.15 gore addition quantity, 339.65 mL (mass concentration 67.27%) water addition quantity, and 49.73 Hz mill frequency. Under this optimal condition, the predicted energy consumption of the newly generated －0.074 mm product is 120.53 (kW·h)/t, and the average energy consumption of the verification test is 118.97 (kW·h)/t, which is very close to the predicted value, indicating that the response surface prediction model is reasonable and effective.
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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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