Process for the Physical Segregation of Minerals

With highly heterogeneous groups or streams of minerals, physical segregation using online quality measurements is an economically important first stage of the mineral beneficiation process. Segregation enables high quality fractions of the stream to bypass processing, such as cleaning operations, thereby reducing the associated costs and avoiding the yield losses inherent in any downstream separation process. The present invention includes various methods for reliably segregating a mineral stream into at least one fraction meeting desired quality specifications while at the same time maximizing yield of that fraction

Arctic Energy Technology Development Laboratory

The Arctic Energy Technology Development Laboratory was created by the University of Alaska Fairbanks in response to a congressionally mandated funding opportunity through the U.S. Department of Energy (DOE), specifically to encourage research partnerships between the university, the Alaskan energy industry, and the DOE. The enabling legislation permitted research in a broad variety of topics particularly of interest to Alaska, including providing more efficient and economical electrical power generation in rural villages, as well as research in coal, oil, and gas. The contract was managed as a cooperative research agreement, with active project monitoring and management from the DOE. In the eight years of this partnership, approximately 30 projects were funded and completed. These projects, which were selected using an industry panel of Alaskan energy industry engineers and managers, cover a wide range of topics, such as diesel engine efficiency, fuel cells, coal combustion, methane gas hydrates, heavy oil recovery, and water issues associated with ice road construction in the oil fields of the North Slope. Each project was managed as a separate DOE contract, and the final technical report for each completed project is included with this final report. The intent of this process was to address the energy research needs of Alaska and to develop research capability at the university. As such, the intent from the beginning of this process was to encourage development of partnerships and skills that would permit a transition to direct competitive funding opportunities managed from funding sources. This project has succeeded at both the individual project level and at the institutional development level, as many of the researchers at the university are currently submitting proposals to funding agencies, with some success

Moving Bed Gasification of Low Rank Alaska Coal

This paper presents process simulation of moving bed gasifier using low rank, subbituminous Usibelli coal from Alaska. All the processes occurring in a moving bed gasifier, drying, devolatilization, gasification, and combustion, are included in this model. The model, developed in Aspen Plus, is used to predict the effect of various operating parameters including pressure, oxygen to coal, and steam to coal ratio on the product gas composition. The results obtained from the simulation were compared with experimental data in the literature. The predicted composition of the product gas was in general agreement with the established results. Carbon conversion increased with increasing oxygen-coal ratio and decreased with increasing steam-coal ratio. Steam to coal ratio and oxygen to coal ratios impacted produced syngas composition, while pressure did not have a large impact on the product syngas composition. A nonslagging moving bed gasifier would have to be limited to an oxygen-coal ratio of 0.26 to operate below the ash softening temperature. Slagging moving bed gasifiers, not limited by operating temperature, could achieve carbon conversion efficiency of 99.5% at oxygen-coal ratio of 0.33. The model is useful for predicting performance of the Usibelli coal in a moving bed gasifier using different operating parameters

The Importance of Specific Phrases in Automatically Classifying Mine Accident Narratives Using Natural Language Processing

The mining industry is diligent about reporting on safety incidents. However, these reports are not necessarily analyzed holistically to gain deep insights. Previously, it was demonstrated that mine accident narratives at a partner mine site could be automatically classified using natural language processing (NLP)-based random forest (RF) models developed, using narratives from the United States Mine Safety and Health Administration (MSHA) database. Classification of narratives is important from a holistic perspective as it affects safety intervention strategies. This paper continued the work to improve the RF classification performance in the category “caught in”. In this context, three approaches were presented in the paper. At first, two new methods were developed, named, the similarity score (SS) method and the accident-specific expert choice vocabulary (ASECV) method. The SS method focused on words or phrases that occurred most frequently, while the ASECV, a heuristic approach, focused on a narrow set of phrases. The two methods were tested with a series of experiments (iterations) on the MSHA narratives of accident category “caught in”. The SS method was not very successful due to its high false positive rates. The ASECV method, on the other hand, had low false positive rates. As a third approach (the “stacking” method), when a highly successful incidence (iteration) from ASECV method was applied in combination with the previously developed RF model (by stacking), the overall predictability of the combined model improved from 71% to 73.28%. Thus, the research showed that some phrases are key to describing particular (“caught in” in this case) types of accidents

Low-Rank Coal Grinding Performance Versus Power Plant Performance

The intent of this project was to demonstrate that Alaskan low-rank coal, which is high in volatile content, need not be ground as fine as bituminous coal (typically low in volatile content) for optimum combustion in power plants. The grind or particle size distribution (PSD), which is quantified by percentage of pulverized coal passing 74 microns (200 mesh), affects the pulverizer throughput in power plants. The finer the grind, the lower the throughput. For a power plant to maintain combustion levels, throughput needs to be high. The problem of particle size is compounded for Alaskan coal since it has a low Hardgrove grindability index (HGI); that is, it is difficult to grind. If the thesis of this project is demonstrated, then Alaskan coal need not be ground to the industry standard, thereby alleviating somewhat the low HGI issue (and, hopefully, furthering the salability of Alaskan coal). This project studied the relationship between PSD and power plant efficiency, emissions, and mill power consumption for low-rank high-volatile-content Alaskan coal. The emissions studied were CO, CO{sub 2}, NO{sub x}, SO{sub 2}, and Hg (only two tests). The tested PSD range was 42 to 81 percent passing 76 microns. Within the tested range, there was very little correlation between PSD and power plant efficiency, CO, NO{sub x}, and SO{sub 2}. Hg emissions were very low and, therefore, did not allow comparison between grind sizes. Mill power consumption was lower for coarser grinds

Relationship between Particle Size Distribution of Low-Rank Pulverized Coal and Power Plant Performance

The impact of particle size distribution (PSD) of pulverized, low rank high volatile content Alaska coal on combustion related power plant performance was studied in a series of field scale tests. Performance was gauged through efficiency (ratio of megawatt generated to energy consumed as coal), emissions (SO2, NOx, CO), and carbon content of ash (fly ash and bottom ash). The study revealed that the tested coal could be burned at a grind as coarse as 50% passing 76 microns, with no deleterious impact on power generation and emissions. The PSD’s tested in this study were in the range of 41 to 81 percent passing 76 microns. There was negligible correlation between PSD and the followings factors: efficiency, SO2, NOx, and CO. Additionally, two tests where stack mercury (Hg) data was collected, did not demonstrate any real difference in Hg emissions with PSD. The results from the field tests positively impacts pulverized coal power plants that burn low rank high volatile content coals (such as Powder River Basin coal). These plants can potentially reduce in-plant load by grinding the coal less (without impacting plant performance on emissions and efficiency) and thereby, increasing their marketability

Gaining Insight from Semi-Variograms into Machine Learning Performance of Rock Domains at a Copper Mine

Machine learning (ML) is increasingly being leveraged by the mining industry to understand how rock properties vary at a mine site. In previously published work, the rock type, granodiorite, was predicted with high accuracy by the random forest (RF) ML method at the Erdenet copper mine in Mongolia. As a result of the optimistic results (86% overall success rate), this paper extended the research to determine if ML would be successful in modeling rock domains. Rock domains are groups of rocks that occur together. There were two additional goals. One was to determine if the variograms could predict or help understand how ML methods would perform on the data. The second was to determine if 2D modeling would perform well given the disseminated nature of the deposit. ML methods, multilayer perceptron (MLP), k-nearest neighborhood (KNN) and RF, were applied to model six rock domains, D0–D5, in 2D and 3D. Modeling performance was poor in 2D. Prediction performance accuracy was high in 3D for the domains D1 (92–94%), D2 (94–96%) and D4 (85–98%). Note that the domains D1 and D2 together constituted about 80% of the samples. Conclusions drawn in this paper are based on the results of 3D modeling since 2D modeling performance was poor. Prediction performance appeared to depend on two factors. It was better for a domain when the domain was not a minuscule proportion of the sample. It was also better for domains whose indicator semi-variogram (ISV) range was high. For example, though D4 only contributed 15% of the samples, the range was high. MLP did not perform as well as KNN and RF, with RF performing the best. The hyperparameters of KNN and RF suggested that performance was best when only a small number of samples were used to make a prediction. One overall summary conclusion is that the two most important domains, D1 and D2, could be predicted with high accuracy using ML. The second summary conclusion is that semi-variograms can provide insight into ML performance

Introduction to the Special Issue “Advances in Computational Intelligence Applications in the Mining Industry”

This is an exciting time for the mining industry, as it is on the cusp of a change in efficiency as it gets better at leveraging data [...