756 research outputs found
Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors
Coal burst is a severe hazard that can result in fatalities and damage of facilities in underground coal mines. To address this issue, a robust unascertained combination model is proposed to study the coal burst hazard based on an updated database. Four assessment indexes are used in the model, which are the dynamic failure duration (DT), elastic energy index (WET), impact energy index (KE) and uniaxial compressive strength (RC). Four membership functions, including linear (L), parabolic (P), S and Weibull (W) functions, are proposed to measure the uncertainty level of individual index. The corresponding weights are determined through information entropy (EN), analysis hierarchy process (AHP) and synthetic weights (CW). Simultaneously, the classification criteria, including unascertained cluster (UC) and credible identification principle (CIP), are analyzed. The combination algorithm, consisting of P function, CW and CIP (P-CW-CIP), is selected as the optimal classification model in function of theory analysis and to train the samples. Ultimately, the established ensemble model is further validated through test samples with 100% accuracy. The results reveal that the hybrid model has a great potential in the coal burst hazard evaluation in underground coal mines. © 202
Experimental and numerical modelling investigations into coal mine rockbursts and gas outbursts
Rockbursts and gas outbursts are a longstanding hazard in underground coal mining due to their sudden occurrences and high consequences. These hazards are becoming prominent due to the increase in mining depth, difficult mining conditions, and adverse gas pressure conditions. Several researchers have proposed different theories, mechanisms, and indices to determine the rockbursts and gas outbursts liability but most of them focus on only some aspects of the complex engineering system for the ease to represent them using partial differential equations. They have often ignored the dynamics of changing mining environment, coal seam heterogeneity and stochastic variations in the rock properties. Most of the indices proposed were empirical and their suitability to different mining conditions is largely debated.
To overcome the limitations of previous theories, mechanisms and indices, a probabilistic risk assessment framework was developed in this research to mathematically represent the complex engineering phenomena of rockbursts and gas outbursts for a heterogeneous coal seam. An innovative object-based non-conditional simulation approach was used to distribute lithological heterogeneity occurring in the coal seam to respect their geological origin. The dynamically changing mining conditions during a longwall top coal caving mining (LTCC) was extracted from a coupled numerical model to provide statistically sufficient data for probabilistic analysis. The complex interdependencies among several parameters, their stochastic variations and uncertainty were realistically implemented in the GoldSim software, and 100,000 equally likely scenarios were simulated using the Monte Carlo method to determine the probability of rockbursts and gas outbursts.
The results obtained from the probabilistic risk assessment analysis incorporate the variations occurring due to lithological heterogeneity and give a probability for the occurrence of rockbursts, coal and gas outbursts, and safe mining conditions. The framework realistically represents the complex mining environment, is resilient and results are reliable. The framework is generic and can be suitably modified to be used in different underground mining scenarios, overcoming the limitations of earlier empirical indices used.Open Acces
Design and Implementation of Coal Mine Physiological Parameters Monitoring Protocol
Modernization in the industries also concerns with the safety of workers especially for underground mining?s. This paper mainly deals with surveillance and safety measures for mine workers, which is most essential in underground mining areas. Here, a concept of wireless sensors network is used to monitor the environment parameters of underground mine area and all sensed parameters are sent to host computer. Arduino Microcontroller is a heart of a system used to build a fully automated measuring system with reliability, high accuracy and smooth control. Upon detecting critical conditions, alert system starts and the same information is transmitted to remote location by ZigBee Communication. The observed changes in the parameters will also be displayed on the host computer at base station which makes it easier for the underground control center to monitor and to take necessary immediate action to avoid damages
Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine
It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction
Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine
It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the highfrequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction
Rockburst and gas outburst forecasting using a probabilistic risk assessment framework in longwall top coal caving faces
A probabilistic risk assessment framework was developed to mathematically represent the complex engineering phenomena of rock bursts and gas outbursts for a heterogeneous coal seam. An innovative object-based non-conditional simulation approach was used to distribute lithological heterogeneity present in the coal seam to respect their geological origin. The changing mining conditions during longwall top coal caving mining (LTCC) were extracted from a coupled numerical model to provide statistically sufficient data for probabilistic analysis. The complex interdependencies among abutment stress, pore pressure, the volume of total gas emission and incremental energy release rate, their stochastic variations and uncertainty were realistically implemented in the GoldSim software, and 100,000 equally likely scenarios were simulated using the Monte Carlo method to determine the probability of rock bursts and gas outbursts. The results obtained from the analysis incorporate the variability in mechanical, elastic and reservoir properties of coal due to lithological heterogeneity and result in the probability of the occurrence of rock bursts, coal and gas outbursts, and safe mining conditions. The framework realistically represents the complex mining environment, is resilient and results are reliable. The framework is generic and can be suitably modified to be used in different underground mining scenarios, overcoming the limitations of earlier empirical indices used
Handbook for methane control in mining
"This handbook describes effective methods for the control of methane gas in mines and tunnels. It assumes the reader is familiar with mining. The first chapter covers facts about methane important to mine safety, such as the explosibility of gas mixtures. The second chapter covers methane sampling, which is crucial because many methane explosions have been attributed to sampling deficiencies. Subsequent chapters describe methane control methods for different kinds of mines and mining equipment, primarily for U.S. coal mines. These coal mine chapters include continuous miners and longwalls, including bleeders. Coal seam degasification is covered extensively. Other coal mine chapters deal with methane emission forecasting and predicting the excess gas from troublesome geologic features like faults. Additional coal chapters contain methane controls for shaft sinking and shaft filling, for surface highwall mines, and for coal storage silos. Major coal mine explosion disasters have always involved the combustion of coal dust, originally triggered by methane. Thus, a chapter is included on making coal dust inert so it cannot explode. Methane is surprisingly common in metal and nonmetal mines around the world, as well as in many tunnels as they are excavated. Accordingly, a chapter is included on metal and nonmetal mines and another on tunnels. Proper ventilation plays the major role in keeping mines free of hazardous methane accumulations. The ventilation discussed in this handbook, except for the chapter on bleeder systems, deals only with so-called face ventilation, i.e., ventilation of the immediate working face area, not ventilation of the mine as a whole. The omission of whole-mine ventilation was necessary to keep this handbook to a reasonable size and because a huge amount of excellent information is available on the subject." - NIOSHTIC-2by Fred N. Kissell."June 2006."Also available via the World Wide Web.Includes bibliographical references and index
Gas Concentration Prediction Based on the Measured Data of a Coal Mine Rescue Robot
The coal mine environment is complex and dangerous after gas accident; then a timely and effective rescue and relief work is necessary. Hence prediction of gas concentration in front of coal mine rescue robot is an important significance to ensure that the coal mine rescue robot carries out the exploration and search and rescue mission. In this paper, a gray neural network is proposed to predict the gas concentration 10 meters in front of the coal mine rescue robot based on the gas concentration, temperature, and wind speed of the current position and 1 meter in front. Subsequently the quantum genetic algorithm optimization gray neural network parameters of the gas concentration prediction method are proposed to get more accurate prediction of the gas concentration in the roadway. Experimental results show that a gray neural network optimized by the quantum genetic algorithm is more accurate for predicting the gas concentration. The overall prediction error is 9.12%, and the largest forecasting error is 11.36%; compared with gray neural network, the gas concentration prediction error increases by 55.23%. This means that the proposed method can better allow the coal mine rescue robot to accurately predict the gas concentration in the coal mine roadway
X-ray monitoring of classical novae in the central region of M 31. II. Autumn and winter 2007/2008 and 2008/2009
[Abridged] Classical novae (CNe) represent the major class of supersoft X-ray
sources (SSSs) in the central region of our neighbouring galaxy M 31. We
performed a dedicated monitoring of the M 31 central region with XMM-Newton and
Chandra between Nov 2007 and Feb 2008 and between Nov 2008 and Feb 2009
respectively, in order to find SSS counterparts of CNe, determine the duration
of their SSS phase and derive physical outburst parameters. We systematically
searched our data for X-ray counterparts of CNe and determined their X-ray
light curves and spectral properties. We detected in total 17 X-ray
counterparts of CNe in M 31, only four of which were known previously. These
latter sources are still active 12.5, 11.0, 7.4 and 4.8 years after the optical
outburst. From the 17 X-ray counterparts 13 were classified as SSSs. Four novae
displayed short SSS phases (< 100 d). Based on these results and previous
studies we compiled a catalogue of all novae with SSS counterparts in M 31
known so far. We used this catalogue to derive correlations between the
following X-ray and optical nova parameters: turn-on time, turn-off time,
effective temperature (X-ray), t2 decay time and expansion velocity of the
ejected envelope (optical). Furthermore, we found a first hint for the
existence of a difference between SSS parameters of novae associated with the
stellar populations of the M 31 bulge and disk. Additionally, we conducted a
Monte Carlo Markov Chain simulation on the intrinsic fraction of novae with SSS
phase. This simulation showed that the relatively high fraction of novae
without detected SSS emission might be explained by the inevitably incomplete
coverage with X-ray observations in combination with a large fraction of novae
with short SSS states, as expected from the WD mass distribution. In order to
verify our results with an increased sample further monitoring observations are
needed.Comment: 31 pages, 23 figures, 10 tables; submitted to A&
DEVELOPMENT OF UNIVARIATE AND MULTIVARIATE FORECASTING MODELS FOR METHANE GAS EMISSIONS IN UNDERGROUND COAL MINES
Methane gas management continues to be a challenge concerning underground coal mine safety and productivity worldwide despite the extraordinary effort of the mining industry, governmental agencies, and academia to develop new technologies to monitor and control methane gas emissions more efficiently. The risk of hazardous methane gas concentrations in underground environments cannot be underestimated. Statistical data for the last 100 years indicate that around 80% of the accidents and 90% of the fatalities in the underground coal mining industry in the US were related to methane gas explosions.
Modern underground mine operations monitor and evaluate atmospheric parameters such as barometric pressure, temperature, gas concentrations, and ventilation parameters (e.g., fan performance and airflow) by means of Automated Atmospheric Monitoring Systems, which use sensors that collect a massive amount of data implemented by mine operators to make decisions concerning mine safety and operate ventilation systems more effectively. In addition, however, some of these data can be statistically studied to develop forecast models to help improve the safety and health parameters of underground coal mining operations.
The research presented in this dissertation investigates potential correlations between methane gas concentrations and independent variables such as barometric pressure and coal production rate to build reliable forecasting models capable of predicting future concentrations of methane gas, mainly based on time series data collected by the Atmospheric Monitoring System of three active underground coal mining operations in the eastern US and weather data retrieved from public weather stations in the proximity of the case studies. The mine and weather data were stored and pre-processed using an Atmospheric Monitoring Analysis and Database Management system explicitly designed to manage Atmospheric Monitoring Systems data. Furthermore, various statistical techniques were implemented to assess the potential association (e.g., autocorrelation and cross-correlation) between methane gas concentration time series and the independent variables. Such associations were employed to develop univariate and multivariate forecasting models for methane gas emissions in underground coal mines. Finally, the optimal model is selected using the Akaike Information Criterion, and the results obtained from the different forecast approaches (univariate and multivariate) are compared using cross-validation metrics to determine the best model.
It was concluded that the ARIMA, VAR, and ARIMAX methane gas forecasting methodologies proposed in this research can accurately predict methane gas concentrations in underground coal mines operations. The methane gas forecasted from the models matched the validation data consistently, and their linear correlation was positive and strong in most cases. In addition, the 95% confidence interval consistently captured the forecast and validation data
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