scholarly journals Pattern Recognition of Signals for the Fault-Slip Type of Rock Burst in Coal Mines

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
X. S. Liu ◽  
J. Tan ◽  
Y. L. Tan ◽  
S. C. Hu
Keyword(s):  
Pattern Recognition ◽  
Electromagnetic Radiation ◽  
Rock Burst ◽  
Early Warning ◽  
Mechanical Model ◽  
Fault Slip ◽  
Coal Face ◽  
Major Threat ◽  
Good Agreement

The fault-slip type of rock burst is a major threat to the safety of coal mining, and effectively recognizing its signals patterns is the foundation for the early warning and prevention. At first, a mechanical model of the fault-slip was established and the mechanism of the rock burst induced by the fault-slip was revealed. Then, the patterns of the electromagnetic radiation, acoustic emission (AE), and microseismic signals in the fault-slip type of rock burst were proposed, in that before the rock burst occurs, the electromagnetic radiation intensity near the sliding surface increases rapidly, the AE energy rises exponentially, and the energy released by microseismic events experiences at least one peak and is close to the next peak. At last, in situ investigations were performed at number 1412 coal face in the Huafeng Mine, China. Results showed that the signals patterns proposed are in good agreement with the process of the fault-slip type of rock burst. The pattern recognition can provide a basis for the early warning and the implementation of relief measures of the fault-slip type of rock burst.

Early-warning of rock burst in coal mine by low-frequency electromagnetic radiation

2020 ◽  
Vol 279 ◽  
pp. 105755 ◽  
Author(s):  
Liming Qiu ◽  
Zhentang Liu ◽  
Enyuan Wang ◽  
Xueqiu He ◽  
Junjun Feng ◽  
...  
Keyword(s):  
Electromagnetic Radiation ◽  
Rock Burst ◽  
Coal Mine ◽  
Early Warning ◽  
Low Frequency

scholarly journals Multiparameter Monitoring and Prevention of Fault-Slip Rock Burst

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Shan-chao Hu ◽  
Yun-liang Tan ◽  
Jian-guo Ning ◽  
Wei-Yao Guo ◽  
Xue-sheng Liu
Keyword(s):  
Rock Burst ◽  
Early Warning ◽  
Large Diameter ◽  
Early Warning Systems ◽  
Fault Slip ◽  
Pressure Relief ◽  
Stick Slip ◽  
Research Results ◽  
Rock Bursts ◽  
Induced Stress

Fault-slip rock burst is one type of the tectonic rock burst during mining. A detailed understanding of the precursory information of fault-slip rock burst and implementation of monitoring and early warning systems, as well as pressure relief measures, are essential to safety production in deep mines. This paper first establishes a mechanical model of stick-slip instability in fault-slip rock bursts and then reveals the failure characteristics of the instability. Then, change rule of mining-induced stress and microseismic signals before the occurrence of fault-slip rock burst are proposed, and multiparameter integrated early warning methods including mining-induced stress and energy are established. Finally, pressure relief methods targeting large-diameter boreholes and coal seam infusion are presented in accordance with the occurrence mechanism of fault-slip rock burst. The research results have been successfully applied in working faces 2310 of the Suncun Coal Mine, and the safety of the mine has been enhanced. These research results improve the theory of fault-slip rock burst mechanisms and provide the basis for prediction and forecasting, as well as pressure relief, of fault-slip rock bursts.

Atomic Layer Deposition of LiF and Lithium Ion Conducting (AlF3)(LiF)x Alloys Using Trimethylaluminum, Lithium Hexamethyldisilazide and Hydrogen Fluoride

2017 ◽  
Author(s):  
Younghee Lee ◽  
Daniela M. Piper ◽  
Andrew S. Cavanagh ◽  
Matthias J. Young ◽  
Se-Hee Lee ◽  
...  
Keyword(s):  
Lithium Ion ◽  
Atomic Layer ◽  
Mass Gain ◽  
Alloy Film ◽  
Ex Situ ◽  
X Ray ◽  
Layer Deposition ◽  
Ion Conducting ◽  
Good Agreement

<div>Atomic layer deposition (ALD) of LiF and lithium ion conducting (AlF<sub>3</sub>)(LiF)<sub>x</sub> alloys was developed using trimethylaluminum, lithium hexamethyldisilazide (LiHMDS) and hydrogen fluoride derived from HF-pyridine solution. ALD of LiF was studied using in situ quartz crystal microbalance (QCM) and in situ quadrupole mass spectrometer (QMS) at reaction temperatures between 125°C and 250°C. A mass gain per cycle of 12 ng/(cm<sup>2</sup> cycle) was obtained from QCM measurements at 150°C and decreased at higher temperatures. QMS detected FSi(CH<sub>3</sub>)<sub>3</sub> as a reaction byproduct instead of HMDS at 150°C. LiF ALD showed self-limiting behavior. Ex situ measurements using X-ray reflectivity (XRR) and spectroscopic ellipsometry (SE) showed a growth rate of 0.5-0.6 Å/cycle, in good agreement with the in situ QCM measurements.</div><div>ALD of lithium ion conducting (AlF3)(LiF)x alloys was also demonstrated using in situ QCM and in situ QMS at reaction temperatures at 150°C A mass gain per sequence of 22 ng/(cm<sup>2</sup> cycle) was obtained from QCM measurements at 150°C. Ex situ measurements using XRR and SE showed a linear growth rate of 0.9 Å/sequence, in good agreement with the in situ QCM measurements. Stoichiometry between AlF<sub>3</sub> and LiF by QCM experiment was calculated to 1:2.8. XPS showed LiF film consist of lithium and fluorine. XPS also showed (AlF<sub>3</sub>)(LiF)x alloy consists of aluminum, lithium and fluorine. Carbon, oxygen, and nitrogen impurities were both below the detection limit of XPS. Grazing incidence X-ray diffraction (GIXRD) observed that LiF and (AlF<sub>3</sub>)(LiF)<sub>x</sub> alloy film have crystalline structures. Inductively coupled plasma mass spectrometry (ICP-MS) and ionic chromatography revealed atomic ratio of Li:F=1:1.1 and Al:Li:F=1:2.7: 5.4 for (AlF<sub>3</sub>)(LiF)<sub>x</sub> alloy film. These atomic ratios were consistent with the calculation from QCM experiments. Finally, lithium ion conductivity (AlF<sub>3</sub>)(LiF)<sub>x</sub> alloy film was measured as σ = 7.5 × 10<sup>-6</sup> S/cm.</div>

scholarly journals Millisecond lattice gasification for high-density CO2- and O2-sieving nanopores in single-layer graphene

2021 ◽  
Vol 7 (9) ◽  
pp. eabf0116
Author(s):  
Shiqi Huang ◽  
Shaoxian Li ◽  
Luis Francisco Villalobos ◽  
Mostapha Dakhchoune ◽  
Marina Micari ◽  
...  
Keyword(s):  
Single Layer ◽  
High Density ◽  
Single Layer Graphene ◽  
Pore Density ◽  
Vacancy Defects ◽  
Carbon Gasification ◽  
Layer Graphene ◽  
Gas Molecules ◽  
Good Agreement

Etching single-layer graphene to incorporate a high pore density with sub-angstrom precision in molecular differentiation is critical to realize the promising high-flux separation of similar-sized gas molecules, e.g., CO2 from N2. However, rapid etching kinetics needed to achieve the high pore density is challenging to control for such precision. Here, we report a millisecond carbon gasification chemistry incorporating high density (>1012 cm−2) of functional oxygen clusters that then evolve in CO2-sieving vacancy defects under controlled and predictable gasification conditions. A statistical distribution of nanopore lattice isomers is observed, in good agreement with the theoretical solution to the isomer cataloging problem. The gasification technique is scalable, and a centimeter-scale membrane is demonstrated. Last, molecular cutoff could be adjusted by 0.1 Å by in situ expansion of the vacancy defects in an O2 atmosphere. Large CO2 and O2 permeances (>10,000 and 1000 GPU, respectively) are demonstrated accompanying attractive CO2/N2 and O2/N2 selectivities.

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

Measuring Reservoir Compaction Through Radioactive Marker Technique

2002 ◽  
Vol 124 (4) ◽  
pp. 269-275
Author(s):  
Paolo Macini ◽  
Ezio Mesini
Keyword(s):  
Land Subsidence ◽  
Reservoir Rock ◽  
Reservoir Compaction ◽  
Critical Comparison ◽  
In Situ Method ◽  
Sufficient Precision ◽  
Good Agreement

Radioactive Marker Technique (RMT), an in-situ method to measure reservoir rock compaction and to evaluate uniaxial compressibility coefficients Cm, is examined here. Recent field applications seems to confirm that RMT-derived Cm’s match with sufficient precision with those calculated from land subsidence observed over the field by means of geodetic surveys, but are not always in good agreement with those derived from lab measurements. In particular, here is reported an application of RMT in the Italian Adriatic offshore, which highlights the discrepancies of Cm’s measurements from lab and RMT. At present, these discrepancies aren’t thoroughly understood, so, from an applicative standpoint, it is still necessary to perform a critical comparison and integration between both set of data.

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