Sergei Sabanov

Conventional methods for determining shale oil content are costly and time-consuming when large numbers of samples must be analyzed. This study developed and evaluated a general regression neural network (GRNN) to predict shale oil content using a compiled global dataset comprising 94 oil-shale observations. Following predictor-redundancy analysis, eight variables were retained as model inputs: analytical moisture, ash, total sulfur, and the elemental composition of kerogen (C, H, S, N, and O). The selected NeuralTools (NT) GRNN yielded R² = 0.950, RMSE = 2.02 percentage points, and MAE = 1.30 percentage points for the 75 training observations. For the 19-observation hold-out testing subset, the corresponding values were R² = 0.871, RMSE = 2.35 percentage points, and MAE = 1.95 percentage points. Repeated 10-fold cross-validation using an independently implemented standard GRNN produced a pooled out-of-fold (R²) of 0.595, RMSE of 5.47 percentage points, and MAE of 3.81 percentage points, demonstrating sensitivity to data partitioning. Under identical repeated cross-validation partitions, multiple linear regression provided lower mean RMSE and MAE than the independently implemented GRNN. Ash was the dominant predictor in the selected NT model. The results support the use of the GRNN as a preliminary screening and sample-prioritization tool but also demonstrate the importance of robust validation when modeling relatively small and heterogeneous geological datasets.

The auxiliary ventilation system (AVS) is essential for managing airflow and reducing particulate matter (PM) levels in underground mine environments. Despite its importance, prior studies have insufficiently examined the optimal design of dual duct forced (DDF) AVS to improve airflow and PM management during the loading and unloading operations of diesel-powered equipment (DPE). This work addresses the research gap by utilizing a hybrid methodology to assess the effectiveness of four DDF-AVS designs (1–4) under two distinct DPE operating scenarios: (S1) DPE loading beside the working face and (S2) DPE unloading at a temporary dumpsite. The study utilized Ansys-Fluent for numerical simulations and revealed the following conclusions: the airflow field within the drift displays intricate patterns that substantially affect PM transport; S2 presents the greatest potential PM exposure danger to DPE operators, succeeded by S1. Among the designs, AVS 2 and AVS 3 exhibited efficiency by less complicated airflow patterns and optimal PM transposition within the drift. In comparison to AVS 1, the PM dispersion enhanced by 15.66% and 7.83% in S1, whereas in S2, it improved by 27% and 46% under AVS 2 and AVS 3, respectively. This research study offers significant insights for optimizing AVS designs, minimizing PM exposure to miners, and improving the underground mine environment through cleaner production techniques.

Particulate matter (PM) poses significant health and environmental pollution risks in underground mines, particularly during load-haul-dump (LHD) operations. However, previous studies have not fully explored the multiple sources of PM generation and diffusion associated with LHD loader mucking ore in a polymetallic underground mine. This study addresses the research gap by adopting both field experimentation and numerical simulation to assess PM propagation under three distinct LHD loader operating conditions: (Condition 1) loading at the working face, (Condition 2) dumping inside the temporary dumpsite, and (Condition 3) dumping onto an underground mine truck (UMT). Ansys-Fluent and regression analysis tools were used to accomplish the results. The findings revealed complex airflow patterns comprising backflow, vortex, unsteady, and steady flow regions. Condition 2 exposed the LHD operator to the highest PM concentration, followed by Condition 1, while Condition 3 resulted in higher PM exposure for the UMT operator compared to the LHD operator. Furthermore, the study analyzed the PM diffusion rate under four volumetric airflows and proposed the optimal volumetric airflow for mitigating miners’ PM exposure. These findings have significant implications for optimizing mining operations schedules and the development of strategies to mitigate miners’ exposure to elevated PM concentrations.

Most oil shale deposits in Kazakhstan were estimated without detailed calculations of the grade and tonnages and included low confidence categories, i.e., Inferred, Off-Balance, and Non-Economic oil shales, on which cannot be given any oil reserves. An analysis of Kazakhstan oil shale deposits in accordance with resource estimation practices for consideration of potential shale oil tonnages has been produced. The developed methodology considers extraction and processing recoveries of conventional and unconventional mining methods. The methodology uses Monte Carlo modeling to estimate a range of oil content and oil recoveries and uses the event tree analysis to demonstrate how the initial oil shale material tonnages and grades go through various fault and success branches, considering probabilities distributions and estimating potential shale oil tones at the end. As a result, this estimation methodology has been validated by high-ranked resource category oil shale deposits, which demonstrated the range of potential shale oil in the range of 10.7–16.8 Mt at the 50% confidence level. The results will be used for further consideration in financial-economic feasibility studies, which must take into account operational and capital expenses, product sale prices and market, and social-environmental aspects.

Particulate matter (PM) in the context of underground mining results from various operations such as rock drilling and blasting, ore loading, hauling, crushing, dumping, and from diesel exhaust gases as well. These operations result in the formation of fine particles that can accumulate in the lungs of mineworkers. The lung deposited surface area (LDSA) concentration is a variant solution to evaluate potential health impacts. The aim of this study is to analyse PM and LDSA concentrations in the operational workings of the oil shale underground mine. Experimental measurements were carried out by a direct-reading real-time PM monitor, Dusttrak DRX, and a multimetric fine particle detector, Naneous Partector 2, during the loading and dumping processes using the diesel engine loader. Consequently, the analysis was conducted on PM, LDSA, particle surface area concentration (SA), average particle diameter (d), particle number concentration (PNC), and particle mass (PM_0.3), producing a few valuable correlation factors. Averaged LDSA was around 1433 μm²/cm³ and reached maximum peaks of 2140 μm²/cm³ during the loading, which was mostly related to diesel exhaust emissions, and within the dumping 730 μm²/cm³ and 1840 μm²/cm³, respectively. At the same time, average PM₁ was about 300 μg/ m³ during the loading, but within the dumping peaks, it reached up to 10,900 μg/ m³. During the loading phase, particle diameter ranged from 30 to 90 nm, while during the dumping phase peaks, it varied from 90 to 160 nm. On this basis, a relationship between PNC and particle diameter has been produced to demonstrate an approximate split between diesel particulate matter (DPM) and oil shale dust diameters. This study offers important data on PM and LDSA concentration that can be used for estimating potential exposure to miners at various working operations in the oil shale underground mines, and will be used for air quality control in accordance with establishing toxic aerosol health effects.

This work analyses issues associated with the deteriorated blasting efficiency by the use of emulsion explosives in underground oil shale mines that utilise room and pillar mining methods. The problems were connected with the reduction in entry advance, shattering of pillar walls and falling of the roof. The aim of this study is to improve blasting efficiency that can guarantee a sufficient entry advance and secure stable conditions of the pillars and the immediate roof. The improved blasting pattern was successfully tested under in-situ conditions. It was demonstrated that the entry advance and the stability of the immediate roof and pillar walls were significantly enhanced as a result.

The amount if oil shale resources throughout the world has been roughly estimated in accordance with various resource estimation methods. However, in some instances, detailed and comprehensive supporting methodologies for the estimation of commercial shale oil reserves have not been presented. The goal of this study is to develop a comprehensive method for the modified estimation of oil shale mineable reserves for shale oil projects. The methodology characterises oil shale according to its calorific value, oil content, conditional organic mass, and ash content by utilising a Monte Carlo simulation. Based on the results of the case study, the developed method proposes considering the relationships of the in situ oil shale grade and tonnage material (oil shale + limestone) to the oil retorting feed material grade and tonnage by taking into account the retorting plant oil recovery. For this purpose, a Monte Carlo stochastic modelling algorithm was developed. Based on the data analysis, a modifying factor to convert mineral reserves to petroleum reserves was produced. The results of this study are useful for feasibility studies that estimate oil shale reserves in relation to justifying their utilisation fields. Some oil shale deposits have good potential for development but need to be re-estimated in accordance with the most sophisticated extraction and processing technologies.