Browsing by Author "Westman, Erik Christian"
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- Application of Measurement While Drilling Data for Mine Blast Optimization Utilizing Machine Learning Techniques with Iron Ore Mine DataArnold, 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.
- Correlations between the Mineralogy and Recovery Behavior of Rare Earth Elements (REEs) in Coal WasteJi, Bin (Virginia Tech, 2023-01-12)Many literatures have been published recently regarding the recovery of REEs from coal-related materials, such as coal waste, acid mine drainage, and coal combustion ash. The recovery of REEs from coal waste has been investigated by the author in recent years, and it was found that after calcination at 600 ℃ for 2 h, a significant improvement in REE recovery can be achieved. In order to reveal the mechanisms of the enhanced REE recovery after calcination, coal waste samples from two different seams, i.e., Western Kentucky No. 13 and Fire Clay, were selected to investigate the modes of occurrence of REEs. Scanning electron microscopy- and transmission electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS and TEM-EDS) analyses were conducted to investigate the mineralogy of REEs in two coal waste samples. Totally, 49 and 50 REE-bearing particles were found from the SEM specimens of Western Kentucky No. 13 and Fire Clay coal waste samples, respectively. Based on the elemental composition analyses and TEM-EDS characterization, it was found that apatite, monazite, and crandallite-group minerals were the major light REE (LREE) carriers, while the heavy REEs (HREE) primarily occurred in zircon and xenotime in these two coal waste samples. Further analyzing the REE content and number of REE-bearing particles, it was confirmed that monazite, xenotime, and crandallite-group minerals were the dominant contributors to the total REE (TREE) contents in both materials. In addition to the mineralogy of REEs, the morphology of REE-bearing particles was also investigated. The SEM images suggested that the particle size of most REE-bearing particles was less than 5 μm. Moreover, not only completely liberated particles, but particles encapsulated by the host minerals present in the two coal waste samples. To identify the changes of mineralogy of REEs after recovery, the leaching solid residues of the raw and calcined coal waste samples were also characterized by SEM-EDS analysis. After REE recovery, the same REE mineralogical results were observed from the leaching residues of the raw coal waste samples. However, as for the calcined samples, the crandallite-group minerals disappeared. These results suggested that the crandallite-group minerals were decomposed into easy-to-leach forms after calcination at 600 ℃, thus leading to the improved REE recovery. Moreover, the number of REE-bearing particles (N) found from per area of the calcined leaching residue was confirmed to be larger compared to that of the raw ones. A combination analysis of these results indicated that two mechanisms of the enhanced REE recovery after calcination can be confirmed: (1) decomposing the crandallite-group minerals into more soluble species; and (2) promoting the liberation of the REE-bearing particles encapsulated in the host minerals. The thermal decomposition of crandallite-group minerals was mainly responsible for the enhanced REE recovery from coal waste. However, as a result of the complex isomorphic substitutions and association characteristics, it is difficult to collect a pure endmember of crandallite-group mineral for characterization. Therefore, florencite-(Ce) was synthesized in this study. X-ray diffraction (XRD), SEM-EDS, TEM, thermogravimetric and differential thermal analyses (TGA-DTA), and acid leaching tests were conducted on the synthesized product. The results showed that the variation in Ce leaching recovery corresponded to the phase transformation of florencite. The gradual transformation of florencite from a crystalline mineral into an amorphous phase resulted in the increases in the solubility of Ce. In addition, the thermal transformation of florencite was an independent reaction, which was not interfered by the host materials, such as kaolinite and coal waste.
- Optimization of Quarry Operations and Maintenance SchedulesGeorge, Brennan Kelly (Virginia Tech, 2023-06-28)New technologies such as the Internet of Things are providing newer insights into the health, performance, and utilization of mining equipment through the collection of real-time data with sensors. In this study, data is utilized from multiple quarries and a surface coal mine collected through the software CAT Productivity and CAT MineStar Edge to analyze the performance of loaders and haul trucks. This data consists of performance metrics such as truck and loader cycle time, payload per loader bucket, total truck payload, truck plan distance, and loader dipper count. This study uses data analysis and machine learning techniques to analyze the performance of loaders and haul trucks in the mining operations used in the scope of this study. Data analysis of cycle time and payload show promising results such that there is an optimum cycle time for multiple loaders between 30-40 seconds that show a high average production. Furthermore, the distribution of production variables is analyzed across each set of loaders to compare the performance. The Caterpillar 992K machine in the rock quarries data set seemed to be the highest-yielding machine while the two Caterpillar 993K machines performed similarly in the surface coal mine data set. The Neural Network algorithm created a model that predicted the loader from the performance metrics with 90.26% accuracy using the CAT Productivity data set, while the Random Forest algorithm achieved a 79.82% accuracy using the CAT MineStar Edge data set. Furthermore, the use of preventative maintenance is investigated in the process of replacing Ground Engaging Tools on loader buckets to determine if maintenance was effective. Additionally, data analysis is applied to Ground Engagement Tools maintenance to identify key preventative maintenance schedules to minimize production impact from equipment downtime and unnecessary maintenance. Production efficiency is compared before and after maintenance on Ground Engaging Tools and concluded that there was no material change in the average production of the mine based on that analysis. The insights gained from this study can inform future research and decision-making and improve operational efficiency.
- Optimizing Pillar Design for Improved Stability and Enhanced Production in Underground Stone MinesSoni, Aman (Virginia Tech, 2022-06-27)"Safety is a value, not just a Priority" Geomechanically stable underground excavations require continuous assessment of rock mass behavior for maximizing safety. Optimizing pillar design is essential for preventing hazardous incidents and improving production in room-and-pillar mines. Maintaining regional and global stability is complicated for underground carbonate or stone deposits, where extensive fracture networks and groundwater flow become leading factors for generating unsteady ground conditions including karsts. A sudden encounter with karst cavities during mine advance may lead to safety issues, including ground collapse and outflow of unconsolidated sediments and groundwater. The presence of these eroded zones in pillars may cause their failure and poses a risk to the lives of miners apart from disrupting the pre-planned mining operations. A pervasive presence of joints and fractures plays a primary role in promoting structurally controlled failures in stone mines, which accelerates upon interaction with the karst cavities. The prevalent empirical and analytical approaches for pillar design ignore the geotechnical complexities such as the spatial density of discontinuities, karst voids, and deviation from the design during short-range mine planning. With the increasing market demand for limestone products, mining organizations, as well as enforcement agencies, are investing in research for increasing the efficiency of extracting valuable resources. While economical productivity is essential, preventing risks and ensuring the safety of miners remains the cardinal objective of mining operations. According to the Mine Safety and Health Administration (MSHA), since 2000, about 31% of occupational fatalities at all underground mines in the United States are caused due to ground collapse, which rises to 39% for underground stone mines. The objective of this study is to provide a reliable and methodological approach for pillar design in underground room-and-pillar hard rock mines for safe and efficient ore recovery. The numerical modeling techniques, implemented for a case study stone mine, could provide a pragmatic framework to assess the effect of karsts on rock mass behavior, and design future pillars detected with voids. The research uses data acquired from using remote sensing techniques, such as LiDAR and Ground-penetrating Radar surveys, to map the excavation characteristics. Discontinuum modeling was valuable for analyzing the pillar strength in the presence of discontinuities and cavities, as well as estimating a safe design standard. Discrete Fracture Networks, created using statistical information from discontinuity mapping, were employed to simulate the joints pervading the rock mass. This proposed research includes the calibration of rock mass properties to translate the effect of discontinuities to continuum models. Continuum modeling proved effective in analyzing regional stability along with characterizing the redistributed stress regime by imitating the excavation sequence. The results from pillar-scale and local-scale analyses are converged to optimize pillar design on a global scale and estimate the feasibility of secondary recovery in stone mines with a dominating discontinuity network and karst terrane. Stochastic analysis using finite volume modeling helped evaluate the performance of modified pillars to assist production while maintaining safety standards. The proposed research is valuable for improving future design parameters, excavation practices, and maintaining a balance between an approach towards increased safety while enhancing production.
- Preliminary Design of an Improved Load Measuring Device for Underground Mining Standing SupportsStables, Brandon Shane (Virginia Tech, 2021-11-30)Standing support is often used in conjunction with underground retreat mining. Knowledge of the load-displacement behavior of a standing support and loading induced by the mine opening is critical proper support selection. The NIOSH STOP database contains load-displacement laboratory test data for most commonly used standing supports. Hydraulic load cells currently used to measure in-situ loading of standing supports have exhibited leakage under load, producing irregularities within the dataset. An improved hydraulic load cell eliminates leakage and produces more consistent data.