Optimization of the Process Parameters of Fully Mechanized Top-Coal Caving in Thick-Seam Coal Using BP Neural Networks
| Autor: | Minfu Liang, Chengjun Hu, Rui Yu, Lixin Wang, Baofu Zhao, Ziyue Xu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Environmental effects of industries and plants
Renewable Energy Sustainability and the Environment Geography Planning and Development process parameters TJ807-830 Management Monitoring Policy and Law BP neural network TD194-195 similarity simulation test Renewable energy sources Environmental sciences top-coal caving mining GE1-350 decision model |
| Zdroj: | Sustainability; Volume 14; Issue 3; Pages: 1340 Sustainability, Vol 14, Iss 1340, p 1340 (2022) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su14031340 |
| Popis: | The method of fully mechanized top-coal caving mining has become the main method of mining thick-seam coal. The process parameters of fully mechanized caving will affect the recovery rate and gangue content of top coal. Through numerical simulation software, the top-coal recovery rate and gangue content, under different fully mechanized caving process parameters, were simulated, and the influence law of different fully mechanized caving process parameters on top-coal recovery rate and gangue content was obtained. A decision model for top-coal caving process parameters was established with a BP neural network, and the optimal top-coal caving parameters were obtained for the actual situation of a working face. On this basis, a in-lab similarity simulation test of the particle material was carried out. The results show that the top-coal recovery rate and gangue content were 86.56% and 3.45%, respectively, and the coal caving effect was good. A BP neural network was used to study the decisions optimizing fully mechanized caving process parameters, which effectively improved the decision-making efficiency thereabout and provided a basis for realizing intelligent, fully mechanized caving mining. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|