Proceedings of the 2017 Coal Operators\u27 Conference

Proceedings of the 2017 Coal Operators\u27 Conference. All papers in these proceedings are peer reviewed. ISBN: 978174128261

Proceedings of the 2009 Coal Operators\u27 Conference

Proceedings of the 2009 Coal Operators\u27 Conference. All papers in these proceedings are peer reviewed. ISBN: 978 1 920806 95 8

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R2), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R2 = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R2 = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253). Copyright © 2023 Zhou, Chen, Chen, Khandelwal, Monjezi and Peng

Study on Energy Accumulation and Dissipation Associated with Coal Burst

Coal burst, which refers to the brittle failure of coal, has been a serious hazard for underground coal mining, particularly at greater depth. Massive energy accumulated in coal could be dissipated almost instantaneously in the form of kinetic energy when the loading stress exceeding the ultimate strength of coal. This thesis qualitatively and quantitatively examines the energy accumulation and dissipation process associated with coal burst through a comprehensive research program of literature review, theoretical analysis and experimental studies. The energy accumulation sources, dissipation forms and its influencing factors of coal burst are reviewed based on the energy conservation law and the static-dynamic loads superposition theory. The burst energy is provided by static loads including gravitational and abutment stress, and dynamic loads including fault slipping and roof weighting. Studies indicated that the main driving energy source of coal burst occurred in Australian coal mines resulted from elastic energy storage that has been accumulated during the loading process of coal

Volume II: Mining Innovation

Contemporary exploitation of natural raw materials by borehole, opencast, underground, seabed, and anthropogenic deposits is closely related to, among others, geomechanics, automation, computer science, and numerical methods. More and more often, individual fields of science coexist and complement each other, contributing to lowering exploitation costs, increasing production, and reduction of the time needed to prepare and exploit the deposit. The continuous development of national economies is related to the increasing demand for energy, metal, rock, and chemical resources. Very often, exploitation is carried out in complex geological and mining conditions, which are accompanied by natural hazards such as rock bursts, methane, coal dust explosion, spontaneous combustion, water, gas, and temperature. In order to conduct a safe and economically justified operation, modern construction materials are being used more and more often in mining to support excavations, both under static and dynamic loads. The individual production stages are supported by specialized computer programs for cutting the deposit as well as for modeling the behavior of the rock mass after excavation in it. Currently, the automation and monitoring of the mining works play a very important role, which will significantly contribute to the improvement of safety conditions. In this Special Issue of Energies, we focus on innovative laboratory, numerical, and industrial research that has a positive impact on the development of safety and exploitation in mining

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

Theory and Practice of Tunnel Engineering

Tunnel construction is expensive when compared to the construction of other engineering structures. As such, there is always the need to develop more sophisticated and effective methods of construction. There are many long and large tunnels with various purposes in the world, especially for highways, railways, water conveyance, and energy production. Tunnels can be designed effectively by means of two and three-dimensional numerical models. Ground–structure interaction is one of the significant factors acting on economic and safe design. This book presents recent data on tunnel engineering to improve the theory and practice of the construction of underground structures. It provides an overview of tunneling technology and includes chapters that address analytical and numerical methods for rock load estimation and design support systems and advances in measurement systems for underground structures. The book discusses the empirical, analytical, and numerical methods of tunneling practice worldwide

Investigation of Geomechanical Behavior of Laminated Rock Mass Through Experimental and Numerical Approach

Roof failure in the Appalachian underground coal fields occurs often in laminated shale. Laminated shale roof in coal mines fails in unique ways, such as cutter failure or delamination failure. Extensive studies have investigated the influential factors that cause laminated roof failure, which include in-situ stress, entry layout, roof span, and roof support. However, cutter failure continues to occur frequently and erratically. This is due to the lack of in-depth understanding of the inherent properties of the laminations, such as bedding plane strength, matrix strength, and bedding plane spacing, which in turn influence the geomechanical behavior of the laminated rock. These inherent properties vary and are therefore the significant factors influencing entry and support design. The objective of this dissertation is to discover the effect of lamination properties on the geomechanical behavior of laminated rocks through experimental and numerical analysis. The experimental approach included the development of synthetic laminated rock (SLR). The SLR included three different cohesive strengths (����). This research conducted biaxial tests and triaxial tests on the cubic laminated rock with a special platen. We analyzed the strength, failure mode, and deformation of laminated specimens with various ���� under varied stress conditions. The experimental results showed that ���� significantly influenced the SLR strength, modulus, and failure modes in biaxial stress conditions. Application of confining stress reduced the damage of SLR specimens and constrained the effect of ���� on SLR behavior. The results from the tests on SLR supported the development of a series of numerical models of underground coal mines with laminated roof. To simulate the laminated roof at different scales, this research used FLAC3D based on the finite difference method (FDM) and PFC3D based on the discrete element method (DEM). Next, this research developed the coal mine entry model with the laminated roof in the PFC program using laboratory data and investigated the effect of bedding plane spacing, bedding plane strength, and support pressure on roof stability. The results from the numerical analysis showed that the roof stability and stress magnitude inside the roof increased with both bedding plane spacing and bedding plane strength, and this effect was sensitive to these two properties. PFC then modeled the delamination process of laminated rock under various stress conditions. The results demonstrated that the delamination of an unconfined laminated rock initiates in the inner section of the bedding. Cutter failure initiated with damages that distributed extensively in the roof. We then developed a panel scale longwall model in PFC3D which was then coupled with FLAC3D for analyzing crack propagation in roof as well as understanding large scale failure behavior. Numerical results from the FLAC3D-PFC3D coupled model showed that the bedding plane strength significantly influenced the roof deformation and also modified the fracturing mechanism of the laminated roof. These effects are sensitive to the extraction activity of the entries and panels. These findings will advance knowledge on laminated roof failure and improve entry and support design

Is carbon monoxide sensing an effective early fire detection option for underground coal mines?

The ability of carbon monoxide (CO) sensing to detect early stage smouldering of fixed plant fires in underground coal mines was recently assessed as part of an ongoing fire detection research project. Experiments were carried out to record the level of CO concurrent at the time of alarm activation of a Video Based Fire Detection (VBFD) system. The tests were carried out under simulated mine conditions within the SIMTARS facility at Redbank, Queensland. The experimental setup initially located the CO sensors in the positions at where they would typically be installed underground. On testing the experimental setup, it was found that the amount of CO produced from simulated overheating conveyor belt bearing housings did not display a reading on the CO sensors. The VBFD system however detected smoke and alarmed on each of the trial tests. To enable the experiments to proceed and a comparison to be made, the CO sensors were moved considerably closer to the weak pyrolysis fire source. The question of CO sensor capability in typical operational mine positions was highlighted as a result of this experiment. Computational Fluid Dynamics (CFD) modelling was used to estimate the fire size required to activate CO sensors under typical mining conditions. This modelling reinforced the limitations in using CO detectors on fixed plant. As such, the study presented here indicates that CO sensing may not be the most effective early fire detection option available, and that further research and development work with VBFD should be undertaken

Comparative analysis of coal fatalities in Australia, South Africa, India, China and USA, 2006-2010

Coal mining (especially underground) is considered one of the most hazardous industries, and as a result considerable focus is applied to eliminating or mitigating hazards through careful mine planning, equipment selection and certification, and development of management systems and procedures. Regulatory agencies have developed in-house methods for reporting, classification and tracking of fatalities and other incidents according to the type of event, often including consideration of different hazard types. Unfortunately, direct comparison of mining safety statistics between countries is confounded by considerable differences in the way that individual countries classify specific fatalities or incidents. This paper presents a comparative analysis of coal mining fatality data in Australia, South Africa, India, China and the United States from 2006 to 2010. Individual classification definitions are compared between the five countries, and methods presented to normalise each country’s hazard definitions and reporting regimes around the RISKGATE framework of seventeen different priority unwanted events (or topics). Fatality data from individual countries is then re-classified according to the different RISKGATE topics, thereby enabling a comparative analysis between all five countries. This paper demonstrates the utility and value of a standard classification approach, and submits the RISKGATE framework as a model for classification that could be applied globally in coal mining. RISKGATE is the largest health and safety project ever funded by the Australian coal industry (http://www.riskgate.org) to build an industry body of knowledge to assist in managing common industry hazards. A comprehensive knowledge base has been captured for risk management of tyres, collisions, fires, isolation, strata underground, ground control open cut, explosions, explosives, manual tasks and slips/trips/falls. This has been extended to outburst, coal burst and bumps, interface displays and controls, tailings dams and inrush