Neural Network of Roof Cutting Blasting Parameters Based on Mines with Different Roof Conditions

Xingen Ma, Manchao He, Jiandong Sun, Haohao Wang, Xiaoyu Liu and Enze Zhen
Additional contact information
Xingen Ma: State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
Manchao He: State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
Jiandong Sun: School of Safety Engineering, North China Institute of Science & Technology, Hebei 065201, China
Haohao Wang: State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
Xiaoyu Liu: State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
Enze Zhen: State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China

Energies, 2018, vol. 11, issue 12, 1-22

Abstract: The design and construction of roof cutting and blasting is a key part of the roof cutting pressure releasing gob-side entry retaining (RCPRGER) technology. In the existing research, the blasting parameters of roof cutting have been primarily determined by field tests. However, the disadvantages of field tests include a complicated process, which hinders the succession of related procedures, and an unstable roof cutting effect. Therefore, in this work, the authors attempt to use a mathematical analysis method to simplify the design process of the key parameters of roof cutting blasting. First, the mechanics process mechanism of surrounding rocks with roof cutting and pressure releasing is investigated, and the stress evolution process of the surrounding rock is divided into the following six stages: original rock stress state, excavation stress state, supporting stress state, roof cutting stress state, premining stress state, and postmining stress state. Furthermore, the relationship between roof cutting and entry retaining from the perspective of Mohr’s stress circle is discussed. Next, using four typical mines, including the Tashan, Yuanlin, Jinfeng, and Hengyuan coal mines, as examples, the existing design methods of roof cutting and blasting, geological data characteristics of each mine, distribution rule of roof cutting connectivity rate, and explosive charge structure of roof cutting blasting are summarized. Based on these results, the logic of roof cutting blasting design is obtained, the key indices affecting blasting design are determined, and the blasting design is defined as a complex fuzzy problem with multiple factors. Finally, based on the study of the above mechanics mechanism and blasting rule, a three-layer back propagation (BP) neural network, including six input units, nine hidden units, and three output units, is developed with the four typical mines as the sample space. This neural network realizes the rapid determination of the three key parameters pertaining to sealing length, blasthole spacing, and the explosive charge weight of a single hole. Through training, the calculation error is less than 0.48%, which considerably simplifies the design process of the blasting parameters. The charge structure parameters can also be determined according to this method. At present, the construction of this neural network has the shortcomings of limited sample space. This problem can be overcome by supplementing the sample size in the subsequent research and practice, which will improve the efficiency and accuracy of this design method and promote the application and development of the RCPRGER technology. The interdisciplinary research reported in this paper is an attempt that uses an intelligent algorithm to simplify the design process of roof cutting blasting in RCPRGER, and it represents not only an application development of the intelligent algorithm, but also a new step regarding the intelligent design of RCPRGER technology.

Keywords: roof cutting and pressure releasing; gob side entry retaining; blasting parameters; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/12/3468/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/12/3468/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:12:p:3468-:d:189797

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().