Browsing by Author "Sarver, Emily A."
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- Advanced Computing and Sensing to Improve Mine Fire Characterization and ResponseBarros 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.
- Advancing respirable coal mine dust source apportionment: a preliminary laboratory exploration of optical microscopy as a novel monitoring toolSanta, Nestor; Sarver, Emily A. (2024-04-16)Exposure to respirable coal mine dust (RCMD) can cause chronic and debilitating lung diseases. Real-time monitoring capabilities are sought which can enable a better understanding of dust components and sources. In many underground mines, RCMD includes three primary components which can be loosely associated with three major dust sources: coal dust from the coal seam itself, silicates from the surrounding rock strata, and carbonates from the inert ‘rock dust’ products that are applied to mitigate explosion hazards. A monitor which can reliably partition RCMD between these three components could thus allow source apportionment. And tracking silicates, specifically, could be valuable since the most serious health risks are typically associated with this component-particularly if abundant in crystalline silica. Envisioning a monitoring concept based on field microscopy, and following up on prior research using polarized light, the aim of the current study was to build and test a model to classify respirable-sized particles as either coal, silicates, or carbonates. For model development, composite dust samples were generated in the laboratory by successively depositing dust from high-purity materials onto a sticky transparent substrate, and imaging after each deposition event such that the identity of each particle was known a priori. Model testing followed a similar approach, except that real geologic materials were used as the source for each dust component. Results showed that the model had an overall accuracy of 86.5%, indicating that a field-microscopy based monitor could support RCMD source apportionment and silicates tracking in some coal mines.
- Analytical and Numerical Techniques for the Optimal Design of Mineral Separation CircuitsNoble, Christopher Aaron (Virginia Tech, 2013-06-13)The design of mineral processing circuits is a complex, open-ended process. While several tools and methodologies are available, extensive data collection accompanied with trial-and-error simulation are often the predominant technical measures utilized throughout the process. Unfortunately, this approach often produces sub-optimal solutions, while squandering time and financial resources. This work proposes several new and refined methodologies intended to assist during all stages of circuit design. First, an algorithm has been developed to automatically determine circuit analytical solutions from a user-defined circuit configuration. This analytical solution may then be used to rank circuits by traditional derivative-based linear circuit analysis or one of several newly proposed objective functions, including a yield indicator (the yield score) or a value-based indicator (the moment of inertia). Second, this work presents a four-reactor flotation model which considers both process kinetics and machine carrying capacity. The simulator is suitable for scaling laboratory data to predict full-scale performance. By first using circuit analysis to reduce the number of design alternatives, experimental and simulation efforts may be focused to those configurations which have the best likelihood of enhanced performance while meeting secondary process objectives. Finally, this work verifies the circuit analysis methodology through a virtual experimental analysis of 17 circuit configurations. A hypothetical electrostatic separator was implemented into a dynamic physics-based discrete element modeling environment. The virtual experiment was used to quantify the selectivity of each circuit configuration, and the final results validate the initial circuit analysis projections.
- 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 DustJaramillo 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.
- Applications of Thermal and Laser-Based Methods for Monitoring Airborne Particulates in Coal MinesPhillips, 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.
- Approaches and Barriers to Incorporating Sustainable Development Into Coal Mine DesignCraynon, John Raymond (Virginia Tech, 2011-07-27)It is widely recognized that coal is and will continue to be a crucial element in a modern, balanced energy portfolio, providing a bridge to the future as an important low-cost and secure energy solution to sustainability challenges. The designer of coal mining operations needs to simultaneously consider legal, environmental, and sustainability goals, along with traditional mining engineering parameters, as integral parts of the design process. However, traditional coal mining planning seldom considers key “sustainability factors” such as societal impacts; dislocation of towns and residences; physical and economic impact on neighboring communities and individuals; infrastructure concerns; post-mining land use habitat disruption and reconstruction; and long-term community benefit. This work demonstrates the advantage of using a systems engineering approach based on the premise that systems can only be optimized if all factors are considered at one time. Utilizing systems engineering and optimization approaches allows for the incorporation of regulatory and sustainability factors into coal mine design. Graphical approaches, based on the use of GIS tools, are shown as examples of the development of models for the positive and negative impacts of coal mining operations. However, this work also revealed that there are significant challenges inherent in optimizing the design of large-scale surface coal mining operations in Appalachia. Regulatory and permitting programs in the United States, which give conflicting and ill-defined responsibilities to a variety of federal and state agencies, often focus on single parameters, rather than the full suite of desirable outcomes for sustainability, and serve as barriers to innovation. Sustainable development requires a delicate balance between competing economic, environmental and social interests. In the context of coal mining in the U.S., the current regulatory frameworks and policy-guidance vehicles impede this balance. To address this problem, and thus effectively and efficiently provide energy resources while protecting the communities and environments, the U.S. will likely need to fundamentally restructure regulatory programs. Ideally, revisions should be based upon the key concepts of public ecology and allow for a systems engineering approach to coal mine design.
- Bayesian Methods for Mineral Processing OperationsKoermer, 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.
- Characteristic Analysis of Acid Mine Drainage Precipitates for the Optimization of Rare Earth Extraction ProcessesSaber, Scott William (Virginia Tech, 2018-10-25)Acid mine drainage (AMD) forms when sulfur bearing rocks such as pyrite, are exposed to air and water. The oxidation of these minerals leads to the generation of sulfuric acid, which in turn mobilizes metals such as iron, aluminum, manganese, and others. If left untreated, AMD can cause severe harm to the surrounding ecosystem. By law, mining companies are required to treat AMD, often by oxidizing the contaminated water, raising the pH with a chemical additive, and precipitating the metals out of solution. Recent studies at West Virginia University and Virginia Tech have shown that AMD and the treatment precipitates (AMDp) are enriched in rare earth elements (REEs). Given the importance of REEs to modern technology, as well as potential supply restrictions, subsequent research has attempted to identify promising methods to extract and recovery REEs from AMD and AMDp. Prior studies have shown that the physical characteristics of AMDp can vary considerably from site to site, and a robust processes scheme must account for any site-specific disparities. To better understand the inherent variability of AMDp, a scientific study was commissioned to investigate a standard method of characterizing AMDp for the optimization of rare earth extraction processes. The tests developed in this work define the total acid dose needed to dissolve AMDp at various target pH points. Through the course of the study, over 150 unique AMDp samples were evaluated, and comparative analyses were conducted on samples from different sites as well as replicate samples from the same sites. The resultant dataset was analyzed using an empirical model, and a statistical analysis was conducted to correlate the model parameters and other AMDp physical properties. Relationships between elemental assays, moisture, and fitting parameters of the empirical models were found. These results ultimately led to a recommendation for future treatment of AMD and prospective sites.
- Characteristics of respirable dust in eight Appalachian coal mines: A dataset including particle size and mineralogy distributions, and metal and trace element mass concentrationsSarver, Emily A.; Keles, Cigdem; Rezaee, Mohammad (Elsevier, 2019-08-01)Respirable dust samples were collected in several key locations of eight underground coal mines in central and northern Appalachia. In total, there were 76 unique sampling events (i.e., specific location in a specific mine). Here, we present data from each event describing particle size and mineralogy class distributions across the ∼100–10,000nm size range, which were determined using SEM-EDX; and estimated mass concentrations of potentially bioaccessible and total acid-soluble metals and trace elements, which were determined using sequential digestions with digestate analysis by ICP-MS. Discussion of this dataset is included in a companion research article “Beyond conventional metrics: Comprehensive characterization of respirable coal mine dust” Sarver et al., 2019.
- Characterization of Particulates from Australian Underground Coal MinesLaBranche, Nikky; Keles, Cigdem; Sarver, Emily A.; Johnstone, Kelly; Cliff, David (MDPI, 2021-04-23)The re-identification of coal workers’ pneumoconiosis in Queensland in 2015 has prompted improvements in exposure monitoring and health surveillance in Australia. The potential consequences of excessive exposure to respirable dust may depend upon the size, shape and mineralogical classes of the dust. Technology has now advanced to the point that the dust characteristics can be explored in detail. This research collected respirable dust samples from four operating underground coal mines in Australia for characterization analysis using scanning electron microscopy (SEM) with energy dispersive X-ray (EDX). The research found multiple mineralogical classes present with their own particle size distributions. The variation between mines appears to have had a larger effect on particle size distribution than the differences in mining processes within individual mines. This may be due to variations in the geologic conditions, seam variation or mining conditions.
- Comparison of respirable coal mine dust constituents estimated using FTIR, TGA, and SEM-EDXPokhrel, Nishan; Agioutanti, el E.; Keles, Cigdem; Afrouz, Setareh; Sarver, Emily A. (Springer, 2022-02-24)Since the mid-1990s, there has been a resurgence of severe lung disease among US coal miners. This has prompted efforts to better characterize and monitor respirable dust exposures—especially with respect to mineral constituents sourced from rock strata surrounding the coal, which is believed to play a central role in many cases of disease. Recently, a rapid analysis method for silica (quartz) mass has been developed using direct-on-filter Fourier transform infrared (FTIR) spectroscopy. It can concurrently provide an estimate of kaolinite, presumably a primary silicate mineral in many coal mines. Other methods, including thermogravimetric analysis (TGA) and scanning electron microscopy with energy-dispersive X-ray (SEM–EDX), can also be used to estimate respirable coal mine dust constituents. However, there have been few efforts to compare results across multiple methods. Here, FTIR, TGA, and SEM–EDX were used to analyze 93 sets of respirable dust samples collected in 16 underground coal mines across the USA.
- A Computer-Controlled SEM-EDX Routine for Characterizing Respirable Coal Mine DustJohann-Essex, Victoria; Keles, Cigdem; Sarver, Emily A. (MDPI, 2017-01-23)A recent resurgence in coal workers’ pneumoconiosis (or “black lung”) and concerns over other related respiratory illnesses have highlighted the need to elucidate characteristics of airborne particulates in occupational environments. A better understanding of particle size, aspect ratio, or chemical composition may offer new insights regarding causal factors of such illnesses. Scanning electron microscopy analysis using energy dispersive X-ray (SEM-EDX) can be used to estimate these particle characteristics. If conducted manually, such work can be very time intensive, limiting the number of particles that can be analyzed. Moreover, potential exists for user bias in interpretation of EDX spectra. A computer-controlled (CC) routine, on the other hand, can allow similar analysis at a much faster rate, increasing total particle counts and reproducibility of results. This paper describes a CCSEM-EDX routine specifically developed for analysis of respirable dust samples from coal mines. The routine is verified based on reliability of results obtained on samples of known materials, and reproducibility of results obtained on a set of 10 dust samples collected in the field. The characteristics of the field samples are also discussed with respect to mine occupational environments.
- Considerations for an Automated SEM-EDX Routine for Characterizing Respirable Coal Mine DustJohann, Victoria Anne; Sarver, Emily A. (2015)Respirable dust in coal mining environments has long been a concern for occupational health. Over the past several decades, much effort has been devoted to reducing dust exposures in these environments, and rates of coal workers’ pneumoconiosis (CWP) have dropped significantly. However, in some regions, including parts of Central Appalachia it appears that incidence of CWP has recently been on the rise. This trend is yet unexplained, but a possible factor might be changes in specific dust characteristics, such as particle composition, size or shape. Prior work in our research group has developed a standardized methodology for analyzing coal mine dust particles on polycarbonate filter media using scanning electron microscopy with energy dispersive x-ray (SEM-EDX). While the method allows individual particles to be characterized, it is very time-intensive because the instrument user must interrogate each particle manually; this limits the number of particles that can practically be characterized per sample. Moreover, results may be somewhat user-dependent since classification of particle composition involves some interpretation of EDX spectra. To overcome these problems, we aim to automate the current SEM-EDX method. The ability to analyze more particles without user bias should increase reproducibility of results as well as statistical confidence (i.e., in applying characteristics of the analyzed particles to the entire dust sample.) Some challenges do exist in creating an automated routine, which are primarily related to ensuring that the available software is programmed to differentiate individual particles from anomalies on the sample filter media, select and measure an appropriate number of particles across a sufficient surface area of the filter, and classify particle compositions similarly to a trained SEM-EDX user following a manual method. This paper discusses the benefits and challenges of an automated routine for coal mine dust characterization, and progress to date toward this effort.
- Considerations for TGA of Respirable Coal Mine Dust SamplesScaggs, Meredith; Sarver, Emily A.; Keles, Cigdem (2015)Respirable dust in coal mining environments has long been a concern for occupational health. Over the past several decades, much effort has been devoted to reducing dust exposures in these environments, and rates of coal workers’ pneumoconiosis (CWP) have dropped significantly. However, in some regions, including parts of Central Appalachia it appears that incidence of CWP has recently been on the rise. This trend is yet unexplained, but a possible factor might be changes in specific dust characteristics, such as particle composition, size or shape. Prior work in our research group has developed a standardized methodology for analyzing coal mine dust particles on polycarbonate filter media using scanning electron microscopy with energy dispersive x-ray (SEM-EDX). While the method allows individual particles to be characterized, it is very time-intensive because the instrument user must interrogate each particle manually; this limits the number of particles that can practically be characterized per sample. Moreover, results may be somewhat user-dependent since classification of particle composition involves some interpretation of EDX spectra. Respirable dust in underground coal mines has long been associated with occupational Jung diseases, particularly coal workers' pneumoconiosis (CWP) and silicosis. Regular dust sampling is required for assessing occupational exposures , and compliance with federal regulations is determined! on the basis of total respirable dust concentration and crystalline silica content by mass. In light of continued incidence of CWP amongst coal miners, additional information is needed to determine what role specific dust characteristics might play in health outcomes . While particle-level analysis is ideal, current time requirements and costs make this simply unfeasible for large numbers of samples. However, opportunities do exist for gleaning additional information from bulk analysis (i.e., beyond total mass and silica content) using relatively quick and inexpensive methods. Thermogravimetric analysis (TGA) may be a particularly attractive option. It involves precise sample weight measurement in a temperature controlled environment, such that weight changes over specific temperature ranges can be correlated to cheruical changes of particular sample constituents. In principle, TGA offers the ability to determine the coal and total mineral mass fractions in respirable dust samples. Such analysis could conceivably be combined with standard methods currently used to measure total mass and silica content. Under some circumstances , TGA might also be extended to provide information on specific dust constituents of interest (such as calcite). In this paper, we consider the benefits and challenges of TGA of respirable coal mine dust samples, and provide preliminary results and observations from ongoing research on this topic.
- Continuous DPM Monitoring in Underground Mine Environments: Demonstration of Potential Options in the Laboratory and FieldBarrett, Chelsea A. (Virginia Tech, 2018-03-26)Diesel particulate matter (DPM) is the solid portion of diesel exhaust. DPM occurs primarily in the submicron range, and poses a number of respiratory and other health hazards including cardiovascular and pulmonary disease. Underground miners typically have the highest DPM exposures compared to other occupations. This is because many mines are characterized by confined work spaces and large diesel equipment fleets. Exposures can be a particularly high hazard in large opening mines where ventilation can be challenging. As such, DPM monitoring is critical to protecting miner health and informing a range of engineering decisions. DPM is primarily composed of two components, elemental carbon (EC) and organic carbon (OC), which are often summed to report total carbon (TC). The ratio of EC to OC, and presence of a number of other minor constituents such as sorbed metals, can vary with many factors such as engine operating conditions, maintenance, fuel types and additives, and the level and type of exhaust after-treatments used. Given its complexity, DPM cannot be measured directly, and either TC or EC are generally used as a surrogate. Currently, the Mining Safety and Health Administration (MSHA) limits personal exposures of underground metal/non-metal miners to 160 µg TC/m3 on an 8-hr time weighted average basis. Compliance is demonstrated by collecting full-shift personal filter samples, which are later analyzed using the NIOSH 5040 Standard Method. For engineering purposes, area samples can also be collected and analyzed. The typical lag time between sample collection and reporting of results is on the order of weeks, and this presents a real problem for identifying and remediating conditions that led to overexposures or high DPM in area samples. The handheld FLIR Airtec monitor was developed to provide real-time DPM data and allow immediate decision making. The monitor works on a laser extinction principle to measure EC, the black component of DPM, as mass accumulates on a filter. The Airtec has proven useful for personal monitoring and short-term DPM surveying. However, capabilities are needed for continuous, long-term monitoring. Continuous DPM monitoring would be highly valuable for applications such as design and operation of ventilation on demand systems, or engineering studies of new ventilation, exhaust treatment or other DPM controls. The work presented in this thesis considers three continuous monitors, two of which are already commercially available: Magee Scientific's AE33 black carbon (BC) Aethalometer and Sunset Laboratory's Semi-Continuous OCEC Field Analyzer. The third monitor, called the Airwatch, is still in development. The AE33 and Airwatch effectively operate on the same principle as the Airtec, but include a self-advancing filter tape to allow autonomous operation over relatively long periods of time. The OCEC field monitor is essentially a field version of the laboratory analyzer used for traditional 5040 Method analysis. The AE33 has been briefly demonstrated in mine environments in a couple of other studies, but further testing is needed. The current prototype of the Airwatch and the OCEC field monitor have never been mine-tested. Two separate studies are reported here. The first is a field study in an underground stone mine that tested the Airwatch prototype and AE33 head-to-head under relatively high DPM conditions. Results demonstrated that both instruments could track general trends, but that further work was needed to identify and resolve issues associated with use of both instruments in high-DPM environments – and with basic design elements of the Airwatch. Additionally, the need to calibrate the monitors' output data to the standard measure of EC (i.e., 5040 Method EC) was made clear. In the second study, laboratory testing was conducted under very controlled conditions to meet this need, and another round of field testing was also done. The second study also included the OCEC field monitor. The laboratory tests yielded data to allow interpretation of the AE33 and Airwatch results with respect to 5040 EC. These tests also shed light on the current range EC concentrations over which these monitors can provide reliable data – which is indeed a primary range of interest for mines. As expected, the OCEC field monitor was shown to produce lab-grade results across a wide range of concentrations. The field testing in the second study demonstrated that all three monitors could operate autonomously in a mine environment over extended periods of time (i.e., weeks to months). Overall, it can be concluded that the AE33 and OCEC field monitor represent off-the-shelf options for DPM monitoring in mines, and the Airwatch might be another option if fully developed in the future. Selection of a particular monitoring tool should include careful consideration of specific factors including data quality needs, conditions in the intended monitoring location(s), and general user friendliness of the monitor.
- Critical Elements Recovery from Acid Mine DrainageLi, Qi (Virginia Tech, 2024-02-13)The rapid development of advanced technologies has led to an increase in demand for critical elements that are essential in the manufacturing of high-tech products. Among these critical elements, manganese (Mn), cobalt (Co), and nickel (Ni) are used in the production of batteries, electronics, and other advanced applications. The demand for these elements has been growing exponentially in recent years, driven by the rise of electric vehicles, renewable energy, and other emerging technologies. However, the United States is heavily dependent on foreign sources of critical minerals and on foreign supply chains, resulting in the potential for strategic vulnerabilities to both economy and military. To address this problem and reduce the Nation's vulnerability to disruptions in the supply of critical minerals, it is important to develop critical minerals recycling technologies. A systematic study was conducted to develop a process for producing high-purity Mn, Co, and Ni products from an acid mine drainage (AMD). As major contaminants, Fe and Al in the solution were sequentially precipitated and eliminated by elevating the pH. After that, a pre-concentrated slurry containing Mn, Co, Ni, and Zn was obtained by collecting the precipitates formed in the pH range of 6.50 to 10.00. The pre-concentrated slurry was redissolved for further purification. Sodium sulfide was added into the redissolved solution to precipitate Co, Ni, and Zn selectively while retaining Mn in the solution. Almost 100% of Co, Ni, and Zn but only around 15% of Mn were precipitated using a sulfur-to-metal molar ratio of 1 at pH 4.00. The sulfide precipitate was calcined and then completely dissolved. The critical elements existing in the dissolved solution were efficiently separated using a two-stage solvent extraction process. Ultimately, Co and Ni products with almost 94% and 100% purity were obtained by sulfide and alkaline precipitation, respectively. AMD also contains rare earth elements (REEs), which can be recovered through selective chemical precipitation. REE removal improved at pH 4.0 after converting ferrous to ferric ions with H2O2. Aluminum species in the solution hindered REE adsorption on ferric precipitates, and ferrous ions reduced REE adsorption on aluminum precipitates at lower pH, but at higher pH, REE removal increased due to ferrous ion precipitation. Various tests and analyses were conducted to understand the partitioning mechanisms of REE during the precipitation process of AMD. Sulfide precipitation is crucial to separate Mn from other elements, but the presence of contaminants like Fe and Al can affect sulfide precipitation efficiency. The effects of Al3+ iii and Fe2+ on the precipitation characteristics of four valuable metals, including Mn2+, Ni2+, Co2+, and Zn2+, were investigated by conducting solution chemistry calculations, sulfide precipitation tests, and mineralogy characterizations. It was found that the ability of the valuable metals to form sulfide precipitates followed an order of Zn2+ > Ni2+ > Co2+ > Mn2+. The sulfide precipitate of Zn2+ was the most stable and did not re-dissolve under the acidic condition (pH 4.00 ± 0.05). In addition, the sulfide precipitation of Zn2+ was barely affected by the contaminant metal ions. However, in the presence of Al3+, the precipitation recoveries of Mn2+, Ni2+, and Co2+ in a solution containing all the valuable metals were noticeably reduced due to simultaneous hydrolysis and competitive adsorption. The precipitation recoveries of Ni2+ and Co2+ in solutions containing individual valuable metals also reduced when Fe2+ was present, primarily due to competitive precipitation. However, the recovery of Mn2+ was enhanced due to the formation of ferrous sulfide precipitate, providing abundant active adsorption sites for Mn species. In the solution containing all the valuable metals, Fe2+ promoted the recoveries of the valuable metals due to the higher concentration of Na2S and the formation of ferrous sulfide precipitate.
- Demonstration of Direct-on-filter FTIR to Estimate Silica, Kaolinite, and Calcite Mineral Fraction in Respirable Coal Mine Dust SamplesPokhrel, Nishan (Virginia Tech, 2021-09-09)Respirable coal mine dust (RCMD) has long been recognized as an occupational health hazard. In addition to coal, RCMD can contain minerals such as crystalline silica (i.e., most often present as quartz). There has been a resurgence of lung diseases among US coal miners since the late-1990s which has emphasized the need for better quartz monitoring, and better dust characterization in general. Quartz monitoring in coal mines has traditionally used infrared (IR) spectroscopy-based analytical methods such as the MSHA Method P7 that require significant sample preparation and must be performed in a centralized lab. There are generally thus days to weeks between dust sample collection and reporting of results, which can prevent the prompt mitigation efforts to better control dust and reduce exposures. Recently, a rapid analysis method for quartz has been developed by the US National Institute for Occupational Safety and Health (NIOSH) using direct-on-filter (DOF) Fourier Transform Infrared (FTIR) spectroscopy. The method has been demonstrated in a number of NIOSH-led studies using both laboratory and field samples, and the results show very good accuracy relative to the Method P7 reference. However, it has heretofore not been widely used by others or compared to results from other non-IR analytical methods. Moreover, while FTIR can allow the measurement of additional analytes, this has not yet been a focus of DOF FTIR for RCMD analysis. Analytes such as kaolinite and calcite could be of particular interest in the context of RCMD source apportionment. In this thesis, the DOF FTIR method is used to estimate silica, kaolinite, and calcite mineral fraction in RCMD samples collected in 16 coal mines, and in the laboratory using dust source materials from those same mines. The results are compared to results from other dust characterization methods such as mass-based thermogravimetric analysis (TGA) and particle-based scanning electron microscopy with energy dispersive X-ray (SEM-EDX). Results indicate the usefulness of the DOF FTIR method, and comparison suggests the presence of significant non-carbonate minerals other than silica and kaolinite in the coal mine dust. The results also show that SEM-EDX frequently indicates more mineral content (primarily other aluminosilicates), than that is predicted by either FTIR or the TGA. Additionally, by focusing mainly on calcite (generally sourced from limestone-based rock dust used in coal mines to prevent coal dust explosion), the second part of this study explores basic source apportionment by analyzing mine samples and samples of major dust source materials (such as run-of-mine coal, rock strata, and rock dust products). Results show that calcite can serve as a suitable proxy for rock dust in coal mine dust, and the results are consistent with expectations surrounding the contribution of dust from different mine locations and sample sources. Additionally, the DOF FTIR also showed good agreement with the TGA and SEM-EDX.
- Demonstration of Optical Microscopy and Image Processing to Classify Respirable Coal Mine Dust ParticlesSanta, Nestor (Virginia Tech, 2021)Inhalation of respirable coal mine dust (RCMD) can lead to chronic lung diseases, including coal worker’s pneumoconiosis (CWP) and more severe forms such as progressive massive fibrosis. After the Federal Coal Mine Health and Safety Act was passed in 1969, limits on exposure to respirable dust were set, and the prevalence of CWP abruptly decreased. However, during the last two decades, a resurgence of the disease has been reported. Many authors have argued that the increasing numbers might be related to mining practices, including the extraction of thinner coal seams, characteristics of the mineral deposits, and more powerful cutting machines. Dust particles in coal mines are usually associated with three main sources: Coal particles are produced when the coal seam is being actively extracted. Silica and silicates are generated while cutting the rock strata surrounding the coal or during roof-bolting activities. Finally, rock dust application is the primary source of highly pure carbonates. Timely information about dust composition would allow the identification of potential dust sources and pursue efforts to control dust exposure efficiently. However, this information needs to be provided promptly since dust levels are dynamically changing through the shift. Currently, monitoring technologies such as the continuous personal dust monitor allow real-time measurements, but they are limited to total dust concentration and provide no information about dust composition. More recently, the National Institute for Occupational Safety and Health (NIOSH) has been developing an end-of-shift silica monitor. Still, technologies that offer information on dust composition in a semi-continuous manner are needed. In this work, a new monitoring concept is explored that has the potential to provide near real time data on RCMD constituents. The possible use of a portable optical microscopy (OM) combined with image processing techniques is explored as the basis for a novel RCDM monitoring device. The use of OM in different fields and the rapid development of automated image analysis reveals a clear opportunity that has not been yet exploited for mine dust monitoring applications. This thesis research consisted of two primary studies. The first was an analysis of lab-generated respirable dust samples containing the main mineralogical classes in RCMD (i.e., coal, silica, kaolinite as a proxy for silicate minerals, and a real rock dust product). Samples were imaged using a polarizing microscope and analyzed using an image processing routine to identify and classify particles based on optical characteristics. Specifically, birefringence of particles was exploited to separate coal particles form mineral particles. This is an exciting result since even such a basic fractionation of RCMD would be valuable to track changing conditions at the mine production face and enable rapid decision making. The second study was conducted to explore subclassification of the mineral fraction. A model was built to explore multiple particle features, including particle size, shape, color, texture, and optical properties. However, a simple stepwise method that uses birefringence for separating coal particles first and then classifying silica particles proved most effective. One particular challenge to the silica classification was determined to be the particle loading density. Future work to further enhance the output of the algorithm and next steps were depicted. This thesis research demonstrated that OM and image processing can be used to separate mineral and coal fractions. Subclassification of silica and other minerals using optical properties such as birefringence of particles alone was successful, but showed less accuracy. A robust sampling method that accounts for particle loading density and a more complex model with additional differentiating features might enhance the results. This approach should be considered as a potential candidate for the development of new RCMD monitoring technologies. This tool could enable better tracking of dust conditions and thus better decision-making regarding ventilation, dust controls, and operator position to reduce exposure hazards.
- Demonstration of Optical Microscopy and Image Processing to Classify Respirable Coal Mine Dust ParticlesSanta, Nestor; Keles, Cigdem; Saylor, J. R.; Sarver, Emily A. (MDPI, 2021-08-02)Respirable coal mine dust represents a serious health hazard for miners. Monitoring methods are needed that enable fractionation of dust into its primary components, and that do so in real time. Near the production face, a simple capability to monitor the coal versus mineral dust fractions would be highly valuable for tracking changes in dust sources—and supporting timely responses in terms of dust controls or other interventions to reduce exposures. In this work, the premise of dust monitoring with polarized light microscopy was explored. Using images of coal and representative mineral particles (kaolinite, crystalline silica, and limestone rock dust), a model was built to exploit birefringence of the mineral particles and effectively separate them from the coal. The model showed >95% accuracy on a test dataset with known particles. For composite samples containing both coal and minerals, the model also showed a very good agreement with results from the scanning electron microscopy classification, which was used as a reference method. Results could further the concept of a “cell phone microscope” type monitor for semi-continuous measurements in coal mines.
- 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 systemsUnderwood, 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.