scholarly journals Economic analysis of coal mining costs for underground and strip mining operation

1978 ◽  
Author(s):  
S. Katell
Keyword(s):  
Economic Analysis ◽  
Coal Mining ◽  
Mining Operation ◽  
Strip Mining

Mining Project Design under Villages and Comparison Analysis of Mining Method

2013 ◽  
Vol 734-737 ◽  
pp. 768-772
Author(s):  
Gui Liu ◽  
Nai Zhong Xu ◽  
Chao Gao ◽  
Lei Li
Keyword(s):  
Coal Mining ◽  
Comprehensive Analysis ◽  
Project Design ◽  
Strip Mining ◽  
Mining Method ◽  
Comparison Analysis ◽  
Continuous Miner ◽  
Mining Project ◽  
Mining Conditions ◽  
Mining Width

In order to solve the problem of coal mining under villages of regions of Yaoqiao Mine, through comprehensive analysis, the author considers that strip mining has feasibility in realistic, meanwhile, a series of mining width and retaining width was studied for strip mining and eventually a mining project was put forward to meet the unique conditions. By comparing application conditions and range of application between continuous miner used in strip type of Wongawilli coal mining method and shortwall shearer mining method, the author analyzes the economic benefit and draws a conclusion that shortwall shearer mining method has better applicability. Those all provide a new way to mining under villages in Yaoqiao Mine and the conclusion can also be used for reference in similar mining conditions.

Forensic analysis of the instability of a large-scale slope in a coal mining operation

2013 ◽  
Vol 33 ◽  
pp. 197-211 ◽  
Author(s):  
M. Inmaculada Alvarez-Fernandez ◽  
E. Amor-Herrera ◽  
C. Gonzalez-Nicieza ◽  
F. Lopez-Gayarre ◽  
M. Rodriguez Avial-Llardent
Keyword(s):  
Coal Mining ◽  
Large Scale ◽  
Mining Operation ◽  
Forensic Analysis

scholarly journals Subsidence Control Design Method and Application to Backfill-Strip Mining Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaojun Zhu ◽  
Feng Zha ◽  
Guangli Guo ◽  
Pengfei Zhang ◽  
Hua Cheng ◽  
...  
Keyword(s):  
Coal Mining ◽  
Influencing Factors ◽  
Recovery Rate ◽  
Design Method ◽  
Control Design ◽  
Resource Recovery ◽  
Design Parameters ◽  
Strip Mining ◽  
Filling Material ◽  
Mining Technology

Intensive and massive coal mining causes a series of geological hazards and environmental problems, especially surface subsidence. At present, two major types of subsidence control technology are applied: backfilling technology and partial mining technology. However, the cost of backfill mining is high and partial mining has a low recovery ratio. Therefore, the backfill-strip mining is used to solve the problems of high cost and shortage of filling materials in coal mines at present. A subsidence control design method of backfill-strip mining was proposed in this paper based on the subsidence control effects and economic benefits. First, the stability of the composite support pillar of the filling body and coal pillars in the backfill-strip mining is analyzed, and the values of the main subsidence influencing factors that include the filling material, the size of the backfilling working face, caving mining face, and residual coal pillar are preliminarily determined. Then, the surface movement and deformation are predicted based on the equivalent superposition probability integral method (PIM). The subsidence influencing factors are optimized and determined by comparing the requirements of the safety fortification index of the antideformation ability of surface buildings, resource recovery rate, and coal mining cost. Finally, the mining scheme design parameters of the backfill-strip mining technology are determined. This method is applied in the subsidence control design of backfill-strip mining in the Ezhuang coal mine. Research results show that backfill-strip mining can ensure the safety of surface buildings, increase the resource recovery rate, and reduce coal mining costs through the reasonable design of this method. This study can provide scientific guidance for subsidence disaster control, prevention, and engineering design in backfill-strip mining.

scholarly journals Environmental Engineering Litigation Techniques for the Plaintiff

1986 ◽  
Vol 3 (2) ◽  
Author(s):  
James M. Green
Keyword(s):  
Hazardous Waste ◽  
Mining Operation ◽  
Class Action ◽  
Environmental Engineering ◽  
Strip Mining ◽  
Environmental Action ◽  
Waste Site ◽  
Large Corporation ◽  
Financial Base ◽  
Action Groups

Often times, when environmental class action is desired, the defendant is typically a large corporation or company with a strong financial base and excellent engineering resources. The environmental action for the plaintiff usually consists of a group of individuals who take action against some entity such as a chemical plant, a hazardous waste site, a strip mining operation, a quarry operation or a refinery operation. In some instances, these environmental action groups are interested in actually preventing location of these facilities. Even though the defendants in these types of litigations are extremely well financed with strong engineering support, there are means of using the information and data gathered by the defendants to their disadvantage in environmental litigation. The purpose of this paper is to discuss the avenues open to an attorney who desires to assist poorly financed plaintiffs in this type of class action environmental litigation.

scholarly journals Fire Safety in Indian Coal Mines using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 4003-4005
Keyword(s):  
Machine Learning ◽  
Coal Mining ◽  
Coal Mines ◽  
Mining Industry ◽  
Mining Operation ◽  
Machine Learning Techniques ◽  
Fire Hazards ◽  
Indian Coal ◽  
Learning Software ◽  
The Right

There are around 493 coal mines in India (300+ underground and around190 opencast mines) engaged in coal production for meeting energy and other requirements of our country. Coal and the process of mining itself creates an environment conducive for self-oxidation leading to build up of heat and subsequently break out of fire. This causes safety hazards, decrease in production, increased in de-settlement of colonies, fire related fatalities and risk to life and property. Occurrence of fires in coal mines has always been an undesirable proposition for the coal mining community worldwide due to its high hazard potential towards loss of human lives and property. However, with advent of AI/ML and deep learning, there emerges a vast scope of leveraging its application towards significantly reducing fire hazards in coal mining. Data capturing from such fiery mines, providing machine learning and predicting it beforehand for similar mining situations would significantly enhance safety standard in coal mining industry. This project proposes to develop an algorithm on getting input data from the past incidences/accidents of fire in coal mines and apply machine learning software to help it learn pattern/features vis a vis the fire outcomes. Once the learning is over and data trained, the programme would process the test data of other active projects and may predict for fire threat during forthcoming mining operation. The algorithm aims to enable mining personnel to assess and evaluate the risk of fire in their workplace and take informed decisions based on the predictions based on Machine learning outputs. Also, active fires can as well be studied and predicted in a similar way. This will help the mining team to decide about the right approach of continuing mining operation in such an affected area.

TRACKING BOND RELEASE AT A LARGE WYOMING COAL MINING OPERATION

2011 ◽  
Vol 2011 (1) ◽  
pp. 338-351 ◽  
Author(s):  
Anna Krzyszowska-Waitkus ◽  
Chris Blake
Keyword(s):  
Coal Mining ◽  
Mining Operation
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