Prediction of Porosity and Permeability of Caved Zone in Longwall Gobs

Abstract The porosity and permeability of the caved zone (gob) in a longwall operation impact many ventilation and methane control related issues, such as air leakage into the gob, the onset of spontaneous combustion, methane and air flow patterns in the gob, and the inter action of gob gas ventholes with the mining environment. Despite its importance, the gob is typically inaccessible for performing direct measurements of porosity and permeability. Thus, there has always been debate on the likely values of porosity and permeability of the caved zone and how these values can be predicted. This study demonstrates a predictive approach that combines fractal scaling in porous medium with principles of fluid flow. The approach allows the calculation of porosity and permeability from the size distribution of broken rock material in the gob, which can be determined from image analyzes of gob material using the theories on a completely fragmented porous medium. The virtual fragmented fractal porous medium so generated is exposed to various uniaxial stresses to simulate gob compaction and porosity and permeability changes during this process. The results suggest that the gob porosity and permeability values can be predicted by this approach and the presented models are capable to produce values close to values documented by other researchers

Machine learning and data augmentation approach for identification of rare earth element potential in Indiana Coals, USA

Rare earth elements and yttrium (REYs) are critical elements and valuable commodities due to their limited availability and high demand in a wide range of applications and especially in high-technology products. The increased demand and geopolitical pressures motivate the search for alternative sources of REYs, and coal, coal waste, and coal ash are considered as new sources for these critical elements. This research evaluates the REY potential of coals from Indiana (USA). However, although coal data revealed REY potential, it suffered from sparse samples with complete REY measurements. Therefore, we explore the applicability of machine learning (ML) models and data augmentation techniques to demonstrate their applicability to evaluate REY potential in Indiana, and other areas in coal basins, using selected coal parameters (Al2O3, Fe2O3, C, Ash, S, P, Mo, Zn, and As contents) as covariates (indicators). Due to the relatively small sample size with complete REY data in the Indiana Coal Database, two data augmentation techniques (Random Over-Sampling Examples and Synthetic Minority Over-Sampling Technique) were used. Four machine learning algorithms (linear discriminate analysis, support vector machine, random forest, and artificial neural networks) were applied for modeling REY potential as a classification problem. The results show that application of Synthetic Minority Over-Sampling Technique prior to development of the support vector machine (SVM) models generated the best REY classification with an accuracy of 95%. The encouraging results based on Indiana coal data may suggest that a similar approach can be used for other coal basins for screening the locations with REY potential. Those locations then can be targeted for more detailed geochemical surveys to identify most promising areas and evaluate overall REY resources