Browsing by Author "Ripepi, Nino S."

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    Advanced Computing and Sensing to Improve Mine Fire Characterization and Response
    Barros Daza, Manuel Julian (Virginia Tech, 2022-01-13)
    After fire is discovered in an underground coal mine, a decision must be made to mitigate fire consequences. The decision should be made based on existing conditions, with the goal of increasing the probability of fire extinguishing without compromising the health and safety of the firefighting personnel. However, the determination of fire conditions can be difficult due to coarse in-situ measurements, fire hazards, and the large domains of interest. Additionally, CFD and network models used for predicting fire conditions are computationally expensive with long simulation processing times for informing real-time decision making. A new generalized procedure to design artificial neural networks (ANNs) capable of making predictions of fire conditions, performing hazard/risk assessment, and providing useful information to the firefighters is presented and applied to different underground coal mine fire scenarios. The feed-forward ANNs were developed to classify fires so as to provide the best firefighting decision and determine useful information in real time, such as response time and fire size. The networks were trained to make predictions on different mine locations and to use only available and measurable information in underground coal mines as inputs. The data used for training and testing the networks was generated using high-fidelity CFD and network fire simulations. Additionally, this research presents the applicability of optical fiber sensing technology for continuous, distributed, and real-time sensing. This new technology could be used for collection of input parameters during ongoing fires, leading to improvement of the prediction performance of the ANNs developed. Finally, a new approach to simulate firefighting foam flow through gob areas is proposed and tested using experimental results obtained from a scaled down experimental setup.
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    Application of a TGA Method to Estimate Coal, Carbonate, and Non-carbonate Mineral Fractions as a Proxy for the Major Sources of Respirable Coal Mine Dust
    Jaramillo Taborda, Maria Lizeth (Virginia Tech, 2021-11-16)
    Inhalation of respirable dust in coal mines is a serious occupational health hazard which can lead to the development of chronic and irreversible lung diseases, such as Coal Worker's Pneumoconiosis (CWP) and Progressive Massive fibrosis (PMF). After the passage of the Federal Coal Mine Health and Safety Act (CMHSA) in the late 1960's the prevalence of CWP among US coal miners decreased. However, since the late 1990's a resurgence of lung diseases has been reported, particularly in central Appalachia. On the other hand, dust monitoring data suggest that concentrations of respirable coal mine dust (RCMD) and crystalline silica have been on a downward trend. This contradiction has prompted keen interest in detailed characterization of RCMD to shed light on dust constituents-and their sources. Such information might help miners understand where and under what conditions specific sources contribute to RCMD, and how dust controls and monitoring could be enhanced to mitigate the exposure to respirable hazards. Respirable dust particles generated in coal mines are generally associated with three primary sources: the coal strata that is mined and generates mostly coal particles that could contribute for lung diseases, the rock strata that is cut along with the coal and generates most of the respirable silica and silicates, and the rock dust products that are the main source of carbonates which could produce respiratory irritations. Thermogravimetric Analysis (TGA) is one of many analytical tools that might be used for dust characterization. Its primary benefit is that it can be used to apportion the total sample mass into three mass fractions (i.e., coal, carbonates, non-carbonates) which should be roughly associated with the primary dust sources (i.e., coal strata, rock dust products, rock strata) in many coal mines. This thesis consists of two main chapters: Chapter 1, outlines the research motivation, recaps the efforts to establish a standard TGA method for RCMD, and shows results of the validation experiments that were performed in the current work to enable application of the TGA method to a large set of RCMD and laboratory-generated dust samples. In Chapter 2, 46 lab-generated samples from primary dust source materials collected in 15 coal mines, and 129 respirable dust samples from 23 US coal mines are analyzed using the TGA method validated in Chapter 1. Results for both sets of samples are presented and the mine samples are interpreted based on sampling location, mining method and region. Additionally, Chapter 3 summarizes recommendations for future work.
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    Application of Machine Learning and Deep Learning Methods in Geological Carbon Sequestration Across Multiple Spatial Scales
    Wang, Hongsheng (Virginia Tech, 2022-08-24)
    Under current technical levels and industrial systems, geological carbon sequestration (GCS) is a viable solution to maintain and further reduce carbon dioxide (CO2) concentration and ensure energy security simultaneously. The pre-injection formation characterization and post-injection CO2 monitoring, verification, and accounting (MVA) are two critical and challenging tasks to guarantee the sequestration effect. The tasks can be accomplished using core analyses and well-logging technologies, which complement each other to produce the most accurate and sufficient subsurface information for pore-scale and reservoir-scale studies. In recent years, the unprecedented data sources, increasing computational capability, and the developments of machine learning (ML) and deep learning (DL) algorithms provide novel perspectives for expanding the knowledge from data, which can capture highly complex nonlinear relationships between multivariate inputs and outputs. This work applied ML and DL methods to GCS-related studies at pore and reservoir scales, including digital rock physics (DRP) and the well-logging data interpretation and analysis. DRP provides cost-saving and practical core analysis methods, combining high-resolution imaging techniques, such as the three-dimensional (3D) X-ray computed tomography (CT) scanning, with advanced numerical simulations. Image segmentation is a crucial step of the DRP framework, affecting the accuracy of the following analyses and simulations. We proposed a DL-based workflow for boundary and small target segmentation in digital rock images, which aims to overcome the main challenge in X-ray CT image segmentation, partial volume blurring (PVB). The training data and the model architecture are critical factors affecting the performance of supervised learning models. We employed the entropy-based-masking indicator kriging (IK-EBM) to generate high-quality training data. The performance of IK-EBM on segmentation affected by PVB was compared with some commonly used image segmentation methods on the synthetic data with known ground truth. We then trained and tested the UNet++ model with nested architecture and redesigned skip connections. The evaluation metrics include the pixel-wise (i.e. F1 score, boundary-scaled accuracy, and pixel-by-pixel comparison) and physics-based (porosity, permeability, and CO2 blob curvature distributions) accuracies. We also visualized the feature maps and tested the model generalizations. Contact angle (CA) distribution quantifies the rock surface wettability, which regulates the multiphase behaviors in the porous media. We developed a DL-based CA measurement workflow by integrating an unsupervised learning pipeline for image segmentation and an open-source CA measurement tool. The image segmentation pipeline includes the model training of a CNN-based unsupervised DL model, which is constrained by feature similarity and spatial continuity. In addition, the over-segmentation strategy was adopted for model training, and the post-processing was implemented to cluster the model output to the user-desired target. The performance of the proposed pipeline was evaluated using synthetic data with known ground truth regarding the pixel-wise and physics-based evaluation metrics. The resulting CA measurements with the segmentation results as input data were validated using manual CA measurements. The GCS projects in the Illinois Basin are the first large-scale injection into saline aquifers and employed the latest pulsed neutron tool, the pulsed neutron eXtreme (PNX), to monitor the injected CO2 saturation. The well-logging data provide valuable references for the formation evaluation and CO2 monitoring in GCS in saline aquifers at the reservoir scale. In addition, data-driven models based on supervised ML and DL algorithms provide a novel perspective for well-logging data analysis and interpretation. We applied two commonly used ML and DL algorithms, support vector machine regression (SVR) and artificial neural network (ANN), to the well-logging dataset from GCS projects in the Illinois Basin. The dataset includes the conventional well-logging data for mineralogy and porosity interpretation and PNX data for CO2 saturation estimation. The model performance was evaluated using the root mean square error (RMSE) and R2 score between model-predicted and true values. The results showed that all the ML and DL models achieved excellent accuracies and high efficiency. In addition, we ranked the feature importance of PNX data in the CO2 saturation estimation models using the permutation importance algorithm, and the formation sigma, pressure, and temperature are the three most significant factors in CO2 saturation estimation models. The major challenge for the CO2 storage field projects is the large-scale real-time data processing, including the pore-scale core and reservoir-scale well-logging data. Compared with the traditional data processing methods, ML and DL methods achieved accuracy and efficiency simultaneously. This work developed ML and DL-based workflows and models for X-ray CT image segmentation and well-logging data interpretations based on the available datasets. The performance of data-driven surrogate models has been validated regarding comprehensive evaluation metrics. The findings fill the knowledge gap regarding formation evaluation and fluid behavior simulation across multiple scales, ensuring sequestration security and effect. In addition, the developed ML and DL workflows and models provide efficient and reliable tools for massive GCS-related data processing, which can be widely used in future GCS projects.
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    Application of Measurement While Drilling Data for Mine Blast Optimization Utilizing Machine Learning Techniques with Iron Ore Mine Data
    Arnold, Joshua Ryan (Virginia Tech, 2024-01-10)
    Drilling and blasting procedures are a critical part of mine planning activities and improvements in this stage can lead to better productivity downstream and lower costs. One potential improvement would be better understanding the characteristics of the rock for blast design purposes. The distribution of material properties within a rock mass is very unpredictable so to more accurately determine its characteristics a controlled drilling environment is needed. Many mines possess the capacity to record Measurement While Drilling (MWD) data but don't utilize it. This project investigates and analyzes MWD data from an anonymous iron ore mine. Machine learning was used to analyze the MWD data for the sake of improving blast optimization and productivity and has been used to successfully implement MWD data in other studies. Based on previous work, it has been demonstrated that the utilization of MWD data can assist with developing a better understanding of rock mass properties and other variables of importance during the drill, blast, and mine planning processes. This report investigates using MWD data to classify and predict lithology and utilize regression modeling to identify potential soft spots within blast patterns for blast optimization. The MWD data of six blast patterns from an anonymous mine underwent data processing and then were modeled. The lithology was able to be approximately classified with new information of potentially revealed bed boundaries and blast pattern soft spots.
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    Applications and Development of Intelligent UAVs for the Resource Industries
    Bishop, Richard Edwin (Virginia Tech, 2022-04-21)
    Drones have become an integral part of the digital transformation currently sweeping the mining industry; particularly in surface operations, where they allow operators to model the terrain quickly and effortlessly with GPS localization and advanced mission planning software. Recently, the usage of drones has expanded to underground mines, with advancements in drone autonomy in GPS-denied environments. Developments in lidar technology and Simultaneous Localization and Mapping (SLAM) algorithms are enabling UAVs to function safely underground where they can be used to map workings and digitally reconstruct them into 3D point clouds for a wide variety of applications. Underground mines can be expansive with inaccessible and dangerous areas preventing safe access for traditional inspections, mapping and monitoring. In addition, abandoned mines and historic mines being reopened may lack reliable maps of sufficient detail. The underground mine environment presents a multitude of unique challenges that must be addressed for reliable drone flights. This work covers the development of drones for GPS-denied underground mines, in addition to several case studies where drone-based lidar and photogrammetry were used to capture 3D point clouds of underground mines, and the associated applications of mine digitization, such as geotechnical analysis and pillar strength analysis. This research also features an applied use case of custom drones built to detect methane leaks at natural gas production and distribution sites.
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    Applications of Close-Range Terrestrial 3D Photogrammetry to Improve Safety in Underground Stone Mines
    Bishop, Richard (Virginia Tech, 2020-05-22)
    The underground limestone mining industry is a small, but growing segment of the U.S. crushed stone industry. However, its fatality rate has been amongst the highest of the mining sector in recent years due to ground control issues related to ground collapses. It is therefore important to improve the engineering design, monitoring and visualization of ground control by utilizing new technologies that can help an underground limestone company maintain a safe and productive operation. Photogrammetry and laser scanning are remote sensing technologies that are useful tools for collecting three-dimensional spatial data with high levels of precision for many types of mining applications. Due to the reality of budget constraints for many underground stone mining operations, this research concentrates on photogrammetry as a more accessible technology for the average operation. Despite the challenging lighting conditions and size of underground limestone mines that has previous hindered photogrammetric surveys in these environments, over 13,000 photographic images were taken over a 3-year period in active mines to compile these models. This research summarizes that work and highlights the many applications of terrestrial close-range photogrammetry, including practical methodologies for implementing the techniques in working operations to better visualize hazards and pragmatic approaches for geotechnical analysis, improved engineering design and monitoring.
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    Applications of Thermal and Laser-Based Methods for Monitoring Airborne Particulates in Coal Mines
    Phillips, Kent Thomas (Virginia Tech, 2017-09-22)
    The purpose of this thesis is to examine applications of thermal and laser-based methods to monitor airborne particulates in underground coal mines. Specifically, coal and mixed mineral mine dust, as well as, diesel particulate matter (DPM). These particulates have historically, and continue to have, significant health impacts on underground miners. Chapters 1 and 2 of this thesis concentrate on using a novel method of thermogravimetric analysis (TGA) to characterize respirable coal and mixed mineral mine dust and presents the results of this method being applied to samples collected in Appalachia coal mines. Appalachia has been a geographic "hotspot" for the rise in occupational lung disease amongst underground coal miners, which began in 1990's after decades of steady decline. This has led researchers to propose there could be something unique about the respirable dust composition in Appalachia coal mines, which resulted in the surge of lung disease cases; however, the knowledge base regarding the actual composition of respirable coal mine dust is limited. The results of this thesis show that most of the mass fraction of respirable Appalachia coal mine dust is not coal, but rather carbonates and non-carbonate minerals (i.e. silica and silicates). These findings are significant as many researchers now suspect silica and silicates to be the true culprit in the occupational lung disease of coal miners. DPM presents an additional occupational health hazard to underground coal miners where diesel equipment is used and is difficult to monitor due to its complex nature. In underground metal/non-metal mines, airborne DPM is regulated and monitored using carbon surrogates. However, due to the potential interference from coal-sourced carbon, DPM in coal mines is monitored only by taking samples at the tailpipe of each piece of equipment. This thesis aims to investigate the potential for a laser-based instrument, the FLIR Airtec, to be used in underground coal mines. In particular, what effect the coal dust will have on the instrument, as it measures DPM by way of elemental carbon (EC). The results of this study show that while the Airtec will not over-estimate coal-sourced EC, there could be some sampling artifacts associated with its operation in coal mines, which may inhibit its effectiveness.
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    The Assessment of Sonic Waves and Tracer Gases as Non-Destructive Testing (NDT) Methods for In-Situ Underground Mine Seals
    Brashear, Kyle Thomas (Virginia Tech, 2014-09-17)
    Since the MINER Act of 2006, the minimum static load of in-situ underground mine seals has been increased from 20-psi to either 50-psi if monitoring is conducted or 120-psi if left unmonitored. These minimum strength requirements in seals must be designed, built, and maintained throughout the lifetime of the seal. Due to this, it has become necessary to assess the effectiveness of non-destructive testing (NDT) technologies to determine seal integrity, which in this case, are explored using sonic waves and tracer gases. Through both small and large scale testing, two NDT methods were evaluated on their abilities to determine integrity of the seal. A sonic wave technique to observe a change in wave velocity to identify faults within the seal material. As a NDT method, tracer gases may be used as a potential indicator of a connection between both sides of the seal material through a series of faults and cracks within the material itself. This paper reviews the history of underground mine seals and discusses the overall assessment of sonic waves and tracer gases to serve as NDT methods for estimating the integrity of these seals.
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    Assessment of the Geological Storage Potential of Carbon Dioxide in the Mid-Atlantic Seaboard: Focus on the Outer Continental Shelf of North Carolina
    Mullendore, Marina Anita Jacqueline (Virginia Tech, 2019-05-02)
    In an effort to mitigate carbon dioxide (CO2) emissions in the atmosphere, the Southeast Offshore Storage Resource Assessment (SOSRA) project has for objective to identify geological targets for CO2 storage in two main areas: the eastern part of the Gulf of Mexico and the Atlantic Ocean subsurface. SOSRA's second objective is to estimate the geological targets' capacity to store up to 30 million metric tons of CO2 each year with an error margin of ±30%. As part of this project, the research presented here focuses on the outer continental shelf of North Carolina and its potential for the deployment of large-scale offshore carbon storage in the near future. To identify geological targets, workflow followed typical early oil and gas exploration protocols: collecting existing datasets, selecting the most applicable datasets for reservoir exploration, and interpreting datasets to build a comprehensive regional geological framework of the subsurface of the outer continental shelf. The geomodel obtained can then be used to conduct static volumetric calculations estimating the storage capacity of each identified target. Numerous uncertainties regarding the geomodel were attributed to the variable coverage and quality of the geological and geophysical data. To address these uncertainties and quantify their potential impact on the storage capacity estimations, dynamic volumetric calculations (reservoir simulations) were conducted. Results have shown that, in this area, both Upper and Lower Cretaceous Formations have the potential to store large amounts of CO2 (in the gigatons range). However, sensitivity analysis highlighted the need to collect more data to refine the geomodel and thereby reduce the uncertainties related to the presence, dimensions and characteristics of potential reservoirs and seals. Reducing these uncertainties could lead to more accurate storage capacity estimations. Adequate injection strategies could then be developed based on robust knowledge of this area, thus increasing the probability of success for carbon capture and storage (CCS) offshore projects in North Carolina's outer continental shelf.
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    Bayesian Methods for Mineral Processing Operations
    Koermer, Scott Carl (Virginia Tech, 2022-06-07)
    Increases in demand have driven the development of complex processing technology for separating mineral resources from exceedingly low grade multi- component resources. Low mineral concentrations and variable feedstocks can make separating signal from noise difficult, while high process complexity and the multi-component nature of a feedstock can make testwork, optimization, and process simulation difficult or infeasible. A prime example of such a scenario is the recovery and separation of rare earth elements (REEs) and other critical minerals from acid mine drainage (AMD) using a solvent extraction (SX) process. In this process the REE concentration found in an AMD source can vary site to site, and season to season. SX processes take a non-trivial amount of time to reach steady state. The separation of numerous individual elements from gangue metals is a high-dimensional problem, and SX simulators can have a prohibitive computation time. Bayesian statistical methods intrinsically quantify uncertainty of model parameters and predictions given a set of data and a prior distribution and model parameter prior distributions. The uncertainty quantification possible with Bayesian methods lend well to statistical simulation, model selection, and sensitivity analysis. Moreover, Bayesian models utilizing Gaussian Process priors can be used for active learning tasks which allow for prediction, optimization, and simulator calibration while reducing data requirements. However, literature on Bayesian methods applied to separations engineering is sparse. The goal of this dissertation is to investigate, illustrate, and test the use of a handful of Bayesian methods applied to process engineering problems. First further details for the background and motivation are provided in the introduction. The literature review provides further information regarding critical minerals, solvent extraction, Bayeisan inference, data reconciliation for separations, and Gaussian process modeling. The body of work contains four chapters containing a mixture of novel applications for Bayesian methods and a novel statistical method derived for the use with the motivating problem. Chapter topics include Bayesian data reconciliation for processes, Bayesian inference for a model intended to aid engineers in deciding if a process has reached steady state, Bayesian optimization of a process with unknown dynamics, and a novel active learning criteria for reducing the computation time required for the Bayesian calibration of simulations to real data. In closing, the utility of a handfull of Bayesian methods are displayed. However, the work presented is not intended to be complete and suggestions for further improvements to the application of Bayesian methods to separations are provided.
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    CO2 Minimum Miscibility Pressure and Recovery Mechanisms in Heterogeneous Low Permeability Reservoirs
    Zhang, Kaiyi (Virginia Tech, 2019-09-16)
    Benefited from the efficiency of hydraulic fracturing and horizon drilling, the production of unconventional oil and gas resources, such as shale gas and tight oil, has grown quickly in 21th century and contributed to the North America oil and gas production. Although the new enhancing oil recover (EOR) technologies and strong demand spike the production of unconventional resources, there are still unknowns in recovery mechanisms and phase behavior in tight rock reservoirs. In such environment, the phase behavior is altered by high capillary pressure owing to the nanoscale pore throats of shale rocks and it may also influence minimum miscibility pressure (MMP), which is an important parameter controlling gas floods for CO2 injection EOR. To investigate this influence, flash calculation is modified with considering capillary pressure and this work implements three different method to calculate MMP: method of characteristics (MOC); multiple mixing cell (MMC); and slim-tube simulation. The results show that CO2 minimum miscibility pressure in nanopore size reservoirs are affected by gas-oil capillary pressure owing to the alternation of key tie lines in displacement. The values of CO2-MMP from three different methods match well. Moreover, in tight rock reservoirs, the heterogeneous pore size distribution, such as the ones seen in fractured reservoirs, may affect the recovery mechanisms and MMP. This work also investigates the effect of pore size heterogeneity on multicomponent multiphase hydrocarbon fluid composition distribution and its subsequent influence on mass transfer through shale nanopores. According to the simulation results, compositional gradient forms in heterogeneous nanopores of tight reservoirs because oil and gas phase compositions depend on the pore size. Considering that permeability is small in tight rocks and shales, we expect that mass transfer within heterogeneous pore size porous media to be diffusion-dominated. Our results imply that there can be a selective matrix-fracture component mass transfer during both primary production and gas injection secondary recovery in fractured shale rocks. Therefore, molecular diffusion should not be neglected from mass transfer equations for simulations of gas injection EOR or primary recovery of heterogeneous shale reservoirs with pore size distribution.
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    A detailed justification for the selection of a novel mine tracer gas and development of protocols for GC-ECD analysis of SPME sampling in static and turbulent conditions for assessment of underground mine ventilation systems
    Underwood, Susanne Whitney (Virginia Tech, 2013-01-24)
    Tracer gas surveys are a powerful means of assessing air quantity in underground mine ventilation circuits.  The execution of a tracer gas style ventilation survey allows for the direct measurement of air quantity in locations where this information is otherwise unattainable.  Such instances include inaccessible regions of the mine or locations of irregular flow.  However, this method of completing a mine ventilation survey is an underused tool in the industry.  This is largely due to the amount of training required to analyze survey results. As well, the survey is relatively slow because of the time required to perform analysis of results and the time required to allow for the total elution of tracer compounds from the ventilation circuit before subsequent tracer releases can be made.  These limitations can be mitigated with the development of a protocol for a novel tracer gas which can be readily implemented with existing technology.  Enhanced tracer gas techniques will significantly improve the flexibility of ventilation surveys.  The most powerful means to improve tracer gas techniques applied to mine ventilation surveys is to alter existing protocols into a method that can be readily applied where tracer surveys already take place. One effective method of enhancing existing tracer gas survey protocols is to simply add a second tracer gas that can be detected on a gas chromatograph -- electron capture detector (GC-ECD) using the same method as with the existing industry standard tracer, sulfur hexafluoride (SF6).  Novel tracer gases that have been successfully implemented in the past called for complex analysis methods requiring special equipment, or were designed for inactive workings.  Experimentation with perfluoromethylcyclohexane (PMCH) and SF6 allowed for ideal chromatographic results.  PMCH is a favorable selection for a novel tracer to work in tandem with SF6 due to its chemical stability, similar physical properties and detection limits to SF6, and its ability to be applied and integrated into an existing system.  Additionally, PMCH has been successfully utilized in other large-scale tracer gas studies. Introduction of a novel tracer gas will make great strides in improving the versatility of underground tracer gas ventilation surveys, but further improvement to the tracer gas technique can be made in simplifying individual steps.  One such step which would benefit from improvement is in sampling.  Solid phase microextraction (SPME) is a sampling method that is designed for rapid sampling at low concentrations which provides precise results with minimal training.  A SPME extracting phase ideal for trace analysis of mine gases was selected and a GC-ECD protocol was established.  The protocol for fiber selection and method optimization when performing trace analysis with SPME is described in detail in this thesis.  Furthermore, the impact of sampling with SPME under varying turbulent conditions is explored, and the ability of SPME to sample multiple trace analytes simultaneously is observed.
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    Development and Application of a Risk-Based Online Body-of-Knowledge for the U.S. Underground Coal Mining Industry: RISKGATE-US COAL
    Restrepo, Julian Alexander (Virginia Tech, 2017-02-16)
    The occurrence of multiple fatality events in the U.S. underground coal mining industry, such as the Upper Big Branch mine explosion, illustrates the need for improved methods of major safety hazard identification and control. While many solutions to reducing the risk of mine disasters have been proposed, including stricter regulation and improved technology, a comprehensive risk management approach has yet to be fully integrated in the U.S. mining industry. Comprehensive risk management systems have been developed and implemented across a multitude of heavy industries, most notably the Australian minerals industry. This research examines the successful application of risk management in these industries, along with barriers towards U.S. implementation of risk management, which include the existence of competing safety models (e.g. behavior-based safety) and compliance regulation which consumes company resources, and limits incentive for beyond compliance safety measures. Steps towards the risk-based approach, including increased regulatory pressure and proactive initiation by high-ranking industry individuals, begin with the development of risk-based knowledge within the U.S. mining community. This research reviews the development of mine safety regulation in the U.S., and identifies regulatory constraints which have affected the diffusion of risk management. The development of a risk-based online platform which could complement the existing safety systems of U.S. underground coal operations, based on the Australian RISKGATE tool, is the central work of this research. This online platform has been developed by the research participants and industry professionals whose total underground coal mining experience exceeds 1,290 years. This joint effort has yielded a body-of-knowledge which may be used as a complementary safety control reference for U.S. mine operators who wish to employ risk management policies and practices at their own operations, or identify gaps within their own safety control systems.
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    Development and Demonstration of a Standard Methodology for Respirable Coal Mine Dust Characterization Using SEM-EDX
    Sellaro, Rachel Mary (Virginia Tech, 2014-07-09)
    The purpose of this thesis is to examine the potential for a more comprehensive method of analysis of coal mine dust. Respirable dust is specifically of interest due to its ability to cause occupational lung disease when miners are overexposed to airborne concentrations. A detailed standard methodology to characterize respirable mine dust is carefully investigated with the use of scanning electron microscopy with energy dispersive x-ray (SEM-EDX). In addition to a thorough description of the developed particle level characterization approach, the method is demonstrated with underground respirable dust samples collected from an underground coal mine in Central Appalachia. Results of this thesis indicate that a comprehensive dust characterization method is possible and can be efficient and effective, when standardized. This analytical approach uses measured compositions, dimensions, and shapes to produce an abundance of data in even a single sample of dust. Verification results show the method is suitable for analysis of respirable particles of common coal mine mineralogy and analysis of many samples in a timely manner. The results obtained from the underground samples in Central Appalachia reveal the quantity of information which can be generated using the developed method. The amount of data which is acquired using the more comprehensive dust characterization method may aid in understanding the health effects of various dust characteristics.
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    Development and Implementation of a Standard Methodology for Respirable Coal Mine Dust Characterization with Thermogravimetric Analysis
    Scaggs, Meredith Lynne (Virginia Tech, 2016-07-20)
    The purpose of this thesis is to examine the potential of a novel method for analysis and characterization of coal mine dust. Respirable dust has long been an industry concern due to the association of overexposure leading to the development occupational lung disease. Recent trends of increased incidence of occupational lung disease in miners, such as silicosis and Coal Workers Pneumoconiosis, has shown there is a need for a greater understanding of the respirable fraction of dust in underground coal mines. This study will examine the development of a comprehensive standard methodology for characterization of respirable dust via thermogravimetric analysis (TGA). This method was verified with laboratory-generated respirable dust samples analogous to those commonly observed in underground coal mines. Results of this study demonstrate the ability of the novel TGA method to characterize dust efficiently and effectively. Analysis of the dust includes the determination of mass fractions of coal and non-coal, as well as mass fractions of coal, carbonate, and non-carbonate minerals for larger respirable dust samples. Characterization occurs through the removal of dust particulates from the filter and analysis with TGA, which continuously measures change in mass with specific temperature regions associated with chemical changes for specific types of dust particulates. Results obtained from the verification samples reveal that this method can provide powerful information that may help to increase the current understanding of the health risks linked with exposure to certain types of dust, specifically those found in underground coal mines.
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    Development and Implementation of an Automated SEM-EDX Routine for Characterizing Respirable Coal Mine Dust
    Johann, Victoria Anne (Virginia Tech, 2016-11-02)
    This thesis describes the development and use of a computer-automated microscopy routine for characterization of respirable dust particles from coal mines. Respirable dust in underground coal mining environments has long been known to pose an occupational health hazard for miners. Typically following years of exposure, coal workers' pneumoconiosis (CWP) and silicosis are the most common disease diagnoses. Although dramatic reductions in CWP and silicosis cases were achieved across the US between about 1970-1999 through a combination of regulatory dust exposure limits, improved ventilation and dust abatement practices, a resurgence in disease incidence has been noted more recently – particularly in parts of Appalachia. To shed light on this alarming trend and allow for better understanding of the role of respirable dust in development of disease, more must be learned about the specific characteristics of dust particles and occupational exposures. This work first sought to develop an automated routine for the characterization of respirable dust using scanning electron microscopy with energy dispersive x-ray (SEM-EDX). SEM-EDX is a powerful tool that allows determination of the size, shape, and chemistry of individual particles, but manual operation of the instrument is very time consuming and has the potential to introduce user bias. The automated method developed here provides for much more efficient analysis – with a data capture rate that is typically 25 times faster than that of the manual method on which it was based – and also eliminates bias between users. Moreover, due to its efficiency and broader coverage of a dust sample, it allows for characterization of a larger and more representative number of particles per sample. The routine was verified using respirable dust samples generated from known materials commonly observed in underground coal mines in the central Appalachian region, as well as field samples collected in this region. This effort demonstrated that particles between about 1-9μm were accurately classified with respect to defined chemical categories, and suggested that analysis of 500 particles across a large area of a sample filter generally provides representative results. The automated SEM-EDX routine was then used to characterize a total of 210 respirable dust samples collected in eight Appalachian coal mines. The mines were located in three distinct regions (i.e., northern, mid-central and south-central Appalachia), which differed in terms of primary mining method, coal seam thickness and mining height, and coal and/or rock mineralogy. Results were analyzed to determine whether number distributions of particle size, aspect ratio, and chemistry classification vary between and within distinct mine regions, and by general sampling location categories (i.e., intake, feeder, production, return). Key findings include: 1) Northern Appalachian mines have relatively higher fractions of coal, carbonate, and heavy mineral particles than the two central Appalachian regions, whereas central Appalachian mines have higher fractions of quartz and alumino-silicate particles. 2) Central Appalachian mines tended to have more mine-to-mine variations in size, shape, and chemistry distributions than northern Appalachian mines. 3) With respect to particle size, samples collected in locations in the production and return categories have the highest percentages of very small particles (i.e., 0.94-2.0μm), followed by the feeder and then the intake locations. 4) With respect to particle shape, samples collected in locations in the production and return categories have higher fractions of particles with moderate (i.e., length is 1.5 to 3x width) to relatively high aspect ratios (i.e., length is greater than 3x width) compared to feeder and intake samples. 5) Samples with relatively high fractions of alumino-silicates have higher fractions of particles with moderate aspect ratios than samples with low alumino-silicate fractions. 6) Samples with relatively high fractions of quartz particles have higher fractions of particles with moderate aspect ratios and higher percentages of very small particles than samples with no identified quartz particles. 7) Samples with high fractions of carbonates have higher percentages of particles with relatively low aspect ratios (i.e., length and width are similar) than samples with no identified carbonate particles.
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    Development of a Wireless Borehole Extensometer for Monitoring Convergence in Underground Mines
    Thomas, William Robert (Virginia Tech, 2015-05-21)
    An extensometer has been developed to continuously monitor roof extension in underground mines. The extensometer is designed to be installed in the MSHA-mandated test holes in the roof and measures the displacement between an anchorage point at the top of the borehole and the hole in the roof of the excavation. Once installed, the extensometer will report displacement through semi-wireless communications network. The extensometer is hard-wired into the permissible MIDAS datalogger, where results can be obtained wirelessly via the MIDAS user interface. Lab tests have indicated that the device produces displacement data. The device was installed in one underground coal mine to review its effectiveness in the field.
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    Development of Potential Remote Coal Mine Fire Response Measures: Use of Multiple Passive Source Tracers and Simulation of High Expansion Foam Flow in Simulated Gob Material
    Watkins, Eric Andrew (Virginia Tech, 2018-06-26)
    This thesis examines potential improvements to current coal mine fire response measures. In the event of a fire scenario, indirect testing and analysis of the exhausting air is needed to characterize changes in the fire. The application of multiple passive source tracers provides improved detail of complex ventilation interactions over an extended period of time. The first work in this thesis details the testing of the passive release rates for three Perfluorocarbon tracer compounds over a 180-day period. The results of this study demonstrate the ability for the permeation plug release vessel design to release Perfluorocarbon tracers at a steady rate. Current response methods for a fire in a coal mine gob consist of injection of inert gas and sealing of the mine openings. Injection of high expansion foam into the gob from the surface has potential to improve extinguishment of the fire and reduce the time needed to bring the mine back to an operational state. The applicability of this method requires computational modeling and field testing. The second part of this thesis determines the Darcy and Forchheimer values for high expansion foam flow in simulated gob material with a lab experiment. The experiment was replicated in the CFD software, OpenFOAM, to validate the methods for calculation of the Darcy and Forchheimer values. The results of this study provide a tested methodology for a future full scale modeling of high expansion foam injection in a coal mine gob.
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    Evaluation and Design of Atmospheric Monitoring Interfaces and Approaches for Improved Health and Safety in Underground Coal Mines
    Dougherty, Heather N. (Virginia Tech, 2018-06-29)
    A majority of underground coal mine disasters in the United States are due to explosions. Current atmospheric monitoring system (AMS) practices in the US could be enhanced to facilitate data sharing and learning of the entire work force. With the inclusion of additional atmospheric monitoring and data collecting, meaningful analysis can be realized and shared with the workforce. AMS data can be utilized to advance the understanding of underground atmospheres for the entire workforce along with adding to the knowledge base for preventative planning. An AMS interface ADAMAS is suggested to facilitate this conglomeration and sharing of the data visually, so that it can be quickly processed and applied in their daily decisions. An emerging sensor technology for underground mining, fiber optics is explored and tested in emergency, or fire and explosion situations. The fiber optic methane sensor performed well in smoke only showing a slow in response time due to soot on the filter. The ADAMAS interface was tested in a large population of underground coal miners. The population varied in age, job, classification, and experience. They all primarily found it to be easy to use and helpful to them. Concerns arose when asked how this will facilitate an improved relationship with regulatory agencies. There is trepidation when it comes to additional atmospheric information sharing, that it may not be used advance understanding of mine atmospheres. The AMS data collected is individual to each mine site but can assist in the understanding of underground atmosphere as a whole. Moving forward, regulatory bodies should use this as a stepping point to consider how this information can be used to advance the field of mine ventilation and also the health and safety of the miner.
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    Experimental, Theoretical, and Numerical Investigations of Geomechanics/Flow Coupling in Energy Georeservoirs
    Li, Zihao (Virginia Tech, 2021-09-01)
    The development of hydrocarbon energy resources from shale, a fine-grained, low-permeability geological formation, has altered the global energy landscape. Constricting pressure exerted on a shale formation has a significant effect on the rock's apparent permeability. Gas flow in low-permeability shales is significantly different from liquid flow due to the Klinkenberg effect caused by gas molecule slip at the nanopore wall surfaces. This has the effect of increasing apparent permeability (i.e., the measured permeability). Optimizing the conductivity of the proppant assembly is another critical component of designing subsurface hydrocarbon production using hydraulic fracturing. Significant fracture conductivity can be achieved at a much lower cost than conventional material costs, according to the optimal partial-monolayer proppant concentration (OPPC) theory. However, hydraulic fracturing performance in unconventional reservoirs is problematic due of the complex geomechanical environment, and the experimental confirmation and investigation of the OPPC theory have been rare in previous studies. In this dissertation, a novel multiphysics shale transport (MPST) model was developed to account for the coupled multiphysics processes of geomechanics, fluid dynamics, and the Klinkenberg effect in shales. Furthermore, A novel multi-physics multi-scale multi-porosity shale gas transport (M3ST) model was developed based on the MPST model research to investigate shale gas transport in both transient and steady states, and a double-exponential empirical model was also developed as a powerful substitute for the M3ST model for fitting laboratory-measured apparent permeability. Additionally, throughout the laboratory experiment of fracture conductivity with proppant, the four visible stages documented the evolution of non-monotonic conductivity and proppant concentration. The laboratory methods and empirical model were then applied to the shale plugs from Central Appalachia to investigate the formation properties there. The benefits of developing these regions wisely include a smaller surface footprint, reduced infrastructure requirements, and lower development costs. The developed MPST, M3ST, double-exponential empirical models and research findings shed light on the role of multiphysics mechanisms, such as geomechanics, fluid dynamics and transport, and the Klinkenberg effect, in shale gas transport across multiple spatial scales in both steady and transient states. The fracture conductivity experiments successfully validate the theory of OPPC and illustrate that proppant embedment is the primary mechanism that causes the competing process between fracture width and fracture permeability and consequently the non-monotonic fracture conductivity evolution as a function of increasing proppant concentration. The laboratory experimental facts and the numerical fittings in this study provided critical insights into the reservoir characterization in Central Appalachia and will benefit the reservoir development using non-aqueous fracturing techniques such as CO2 and advanced proppant technologies in the future.