scholarly journals GA-Based Early Warning Method for Rock Burst with Microseismic and Acoustic Emission in Steeply Inclined Coal Seam

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yarong Xue ◽  
Dazhao Song ◽  
Zhenlei Li ◽  
Jianqiang Chen ◽  
Xueqiu He ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Rock Burst ◽  
Coal Mine ◽  
Early Warning ◽  
Characteristic Parameter ◽  
Control Measures ◽  
Risk Level ◽  
Parameter System ◽  
Fitness Value ◽  
Rock Burst Risk

Aiming at problem of low efficacy of early warning of rock burst in coal mine, a multisystem and multiparameter integrated early warning method based on genetic algorithm (GA) is proposed. In this method, firstly, the temporal-spatial-intensity information of energy incubation process of rock burst is deeply mined, and the multidimensional precursory characteristic parameter system of rock burst is constructed. Secondly, the genetic algorithm is used to train the historical monitoring data to obtain the optimal critical value and fitness value of each precursory characteristic parameter, and then the early warning index WC of each monitoring system is calculated. Finally, the integrated rock burst early warning index IC is obtained by synthesizing the early warning index WC of each system. The value of IC corresponds to the specific rock burst risk level of the mine. This method is applied to Wudong coal mine in Xinjiang, China. Based on the actual situation of the mine, a multidimensional precursory characteristic parameter system of rock burst is constructed, which includes energy deviation (DE), frequency ratio (Fr), frequency deviation (DF), degree of dispersion (DS), and total high value of energy deviation (DH). After analyzing the rock burst danger status and risk level in the monitoring area, the early warning capability of this method is found to reach 0.896. Combining with the specific prevention and control measures corresponding to different rock burst risk levels, it can provide effective guidance for the field work.

scholarly journals Microseismic Signals in Heading Face of Tengdong Coal Mine and Their Application for Rock Burst Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
JianJu Ren ◽  
Wenlong Zhang ◽  
Zheng Wu ◽  
Ji Li ◽  
Ying Shen
Keyword(s):  
Rock Burst ◽  
Coal Mine ◽  
Early Warning ◽  
Great Influence ◽  
Field Tests ◽  
Vibration Signal ◽  
Propagation Distance ◽  
Large Energy ◽  
Rock Burst Risk ◽  
Interference Signals

Microseismic (MS) monitoring is an important and commonly used geophysical method in coal mines to predict rock burst which has great influence on safety production. MS monitoring technology and analysis method of the whole mine or working face have been matured, but its use in heading faces of coal mine is not mature due to small disturbances and narrow layout spaces. To carry out MS monitoring and early warning in the heading face, signal recognition must be adequately performed first, and monitoring objects and indicators must be obtained. Through field tests of MS systems at the 117 track gateway of Tengdong coal mine, interference signals of equipment operation and effective signals of coal vibration are accurately collected. After analysis, the waveform characteristics, spectrum, and propagation distance of the interference signals and coal vibration signal are different. Some effective signals with small energy (one-channel triggering) cannot be used as early warning indicators because they are concealed by interference signals. Through trial operation, it is found that large energy (three-channel and four-channel triggering) coal vibration events successfully predicted a rock burst. The MS system of 117 track gateway of Tengdong coal mine should be able to remove the interference signals in real time through the algorithm and take the number of large energy coal vibration signal rather than all coal vibration events as the predictor for rock burst risk monitoring.

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 Reasonable Determination of Terminal Mining Lines Using the Stress Field with Seismic Wave Excitation in Deep Coalfaces

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Guang-an Zhu ◽  
Huan Liu ◽  
Bo-ru Su ◽  
Qi-peng Jiang ◽  
Hai-yang Liu
Keyword(s):  
Stress Field ◽  
Theoretical Analysis ◽  
Rock Burst ◽  
Seismic Wave ◽  
Coal Mine ◽  
Evaluation Method ◽  
Coal Pillar ◽  
Field Observations ◽  
Wave Excitation ◽  
Rock Burst Risk

Many field observations have shown that rock bursts occur frequently near the terminal mining line (TML) and dip coal pillar area in deep coalfaces. Taking the “7.26” rock burst in coalface 3302 in Xingcun Coal Mine as an example, the rock burst mechanism was investigated based on theoretical analysis and field observations, and a combined evaluation method using the stress field under seismic wave excitation was established to determine the reasonable TML of coalface 3302. Firstly, the static geological data revealed during roadway excavation were used for preevaluation of rock burst risk at the working face. By theoretically analyzing the stress transfer mechanism of the two types of the roof structure, the computational model of abutment pressure was established and the calculation method giving the abutment stress was proposed. Subsequently, a dynamic evaluation method that adopts microseismic and stress online monitoring system to monitor dynamic information, such as mine tremors and stress during coalface mining, was developed to define stress anomaly areas and then dynamically determine the TML. Finally, the proposed model was used to optimize the position of the TML of LW3302 in Xingcun Coal Mine; findings obtained in this study provide theoretical guidance for safe coal mining. Combined with the results of theoretical analysis (255 m), online stress monitoring (200 m), microseismic (MS) monitoring (262 m), and passive seismic velocity tomography (220–250 m), it can be finally determined that the width of the protective coal pillar for the TML of coalface 3302 should be at least 262 m.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals Directional Hydraulic Fracturing (DHF) of the Roof, as an Element of Rock Burst Prevention in the Light of Underground Observations and Numerical Modelling

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 562
Author(s):  
Marek Jendryś ◽  
Andrzej Hadam ◽  
Mateusz Ćwiękała
Keyword(s):  
Rock Mass ◽  
Hydraulic Fracturing ◽  
Coal Mining ◽  
Rock Burst ◽  
Coal Mine ◽  
Seismic Activity ◽  
Black Coal ◽  
Directional Hydraulic Fracturing ◽  
Before And After ◽  
The Face

The following article analyzes the effectiveness of directional hydraulic fracturing (DHF) as a method of rock burst prevention, used in black coal mining with a longwall system. In order to define changes in seismic activity due to DHF at the “Rydułtowy” Black Coal Mine (Upper Silesia, Poland), observations were made regarding the seismic activity of the rock mass during coal mining with a longwall system using roof layers collapse. The seismic activity was recorded in the area of the longwall itself, where, on a part of the runway, the rock mass was expanded before the face of the wall by interrupting the continuity of the rock layers using DHF. The following article presents measurements in the form of the number and the shock energy in the area of the observed longwall, which took place before and after the use of DHF. The second part of the article unveils the results of numerical modeling using the discrete element method, allowing to track the formation of goafs for the variant that does not take DHF into consideration, as well as with modeled fractures tracing DHF carried out in accordance with the technology used at “Rydułtowy” coal mine.

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.

Solving Vibration Control Problems Using Reduced Order Models for Flexible Structures: A Genetic Algorithm Approach

1997 ◽  
Author(s):  
Sourav Kundu ◽  
Kentaro Kamagata ◽  
Shigeru Sugino ◽  
Takeshi Minowa ◽  
Kazuto Seto
Keyword(s):  
Genetic Algorithm ◽  
Vibration Control ◽  
Controller Design ◽  
Fitness Function ◽  
Flexible Structures ◽  
Design Solution ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Fitness Value ◽  
Genetic Algorithm Approach

Abstract A Genetic Algorithm (GA) based approach for solution of optimal control design of flexible structures is presented in this paper. The method for modeling flexible structures with distributed parameters as reduced-order models with lumped parameters, which has been developed previously, is employed. Due to some restrictions on controller design it is necessary to make a reduced-order model of the structure. Once the model is established the design of flexible structures is considered as a feedback search procedure where a new solution is assigned some fitness value for the GA and the algorithm iterates till some satisfactory design solution is achieved. We propose a pole assignment method to determine the evaluation (fitness) function to be used by the GA to find optimal damping ratios in passive elements. This paper demonstrates the first results of a genetic algorithm approach to solution of the vibration control problem for practical control applications to flexible tower-like structures.

Towards Academic Successor Selection Modelling with Genetic Algorithm in Multi-Criteria Problems

2018 ◽  
Vol 7 (4.33) ◽  
pp. 130
Author(s):  
Atiqa Zukreena Zakuan ◽  
Shuzlina Abdul-Rahman ◽  
Hamidah Jantan ◽  
. .
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Succession Planning ◽  
Fitness Function ◽  
Academic Staff ◽  
Fitness Value ◽  
Selection Decision ◽  
The University ◽  
Hierarchy Process

Succession planning is a subset of talent management that deals with multi-criteria and uncertainties which are quite complicated, ambiguous, fuzzy and troublesome. Besides that, the successor selection involves the process of searching the best candidate for a successor for an optimal selection decision. In an academic scenario, the quality of academic staff contributes to achieving goals and improving the performance of the university at the international level. The process of selecting appropriate academic staff requires good criteria in decision-making. The best candidate's position and criteria for the selection of academic staff is the responsibility of the Human Resource Management (HRM) to select the most suitable candidate for the required position. The various criteria that are involved in selecting academic staff includes research publication, teaching skills, personality, reputation and financial performance. Previously, most studies on multi-criteria decision-making adopt Fuzzy Analytical Hierarchy Process (FAHP). However, this method is more complex because it involved many steps and formula and may not produce the optimum results. Therefore, Genetic Algorithm (GA) is proposed in this research to address this problem in which a fitness function for the successor selection is based on the highest fitness value of each chromosome.    

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