Full
Outline
In the production process of coking, blending coal as the main raw material determines not only quality of coke, but also cost and profit of coking enterprises. At present, the adjustment for proportion of single coal with experiences is mainly applied for coal blending in coking enterprises, which lacks scientific basis, so it is difficult to achieve high cost-effective coal blending scheme to optimize the coal blending structure for coking. In this paper, it is introduced the purposes of reducing cost of blending coal, improving coke quality, reducing cost of metallurgical coke and improving benefit of enterprises are achieved by using artificial neural network to predict coke quality and using simulated annealing algorithm to optimize proportion of single coal with coke quality as the constraint condition and cost price of blending coal as the optimization objective.
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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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