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Author |
Grozeva, N.G.; Radwan, J.; Beaucaire, C.; Descostes, M. |
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Title |
Reactive transport modeling of U and Ra mobility in roll-front uranium deposits: Parameters influencing 226Ra/238U disequilibria |
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Journal Article |
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Year |
2022 |
Publication |
Journal of Geochemical Exploration |
Abbreviated Journal |
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Volume |
236 |
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106961 |
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Keywords |
Ra/U, Radioactive disequilibria, Radium, Reactive transport modeling, Roll-front uranium deposit |
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Abstract |
Uranium reserve estimates in ore deposits can be significantly impacted by 226Ra/238U disequilibria arising from the differential mobility of uranium and radium during groundwater transport. 1D reactive transport models were developed to investigate the long-term effects of retention processes (UO2(am) precipitation, U(VI) and Ra sorption on smectite, Ra co-precipitation with barite) on the repartitioning of 238U and 226Ra during formation of roll-front type deposits. Analytical solutions to radioactive decay chains were used in complement to examine the influence of geochemical parameters, including fluid 234U/238U activity ratios and α-recoil loss, on 226Ra/238U disequilibria in uranium ores. Model results demonstrate that smectite and barite can produce 226Ra/238U ratios \textgreater1 at low uranium contents and may explain 226Ra/238U disequilibria occurring in altered rock up- and downstream of roll-front deposits. The capacity of these phases to take up Ra and generate 226Ra/238U disequilibria depends on both mineral contents and groundwater compositions, and is thus expected to be site-specific. Simulations of ore deposits that advance downstream with time demonstrate the formation of stronger 226Ra/238U disequilibria, as expected, in the downgradient side or nose of the ore, reflecting both younger mineralization ages and the presence of active uranium precipitation. Whether disequilibria are positive or negative with respect to secular equilibrium, however, depends on the 234U/238U activity ratio in the fluid from which uranium minerals precipitate. Smaller hydraulic conductivities are shown to generate a narrower range in 226Ra/238U activity ratios with distance, and may explain the occurrence of disequilibria in the limb ore that are less pronounced than those in the nose. Furthermore, the ability of α-recoil loss to decrease 226Ra/238U activity ratios at secular equilibrium may account for negative disequilibria in high grade ores. The South Tortkuduk uranium deposits (Kazakhstan) are subsequently used as a case study to identify the processes and parameters that may contribute to 226Ra/238U disequilibria at this site. Variations in multiple parameters, including clay contents, barite contents, and mineralization ages, are found to reproduce measured 226Ra/238U activity ratios in the roll-front ore. Prioritization of these parameters will necessitate field measurements targeting both groundwater fluids and the host rock. Results from this study will ultimately aid geologists in building appropriate hydrogeochemical data sets to more efficiently locate and exploit uranium ore deposits. |
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THL @ christoph.kuells @ grozeva_reactive_2022 |
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180 |
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Zhang, H.; Gao, J.; Xu, L.; Zhang, X. |
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Title |
Case studies of radioactivity of drilling mud for in situ leaching uranium mining in China |
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Journal Article |
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Year |
2022 |
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Journal of Environmental Radioactivity |
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251-252 |
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106982 |
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Drilling mud, Exemption management, In situ leaching, Radioactivity |
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Abstract |
The drilling mud from in situ leaching uranium mining is a type of low-radioactivity waste that contains natural nuclides and other harmful substances. In order to determine whether the drilling mud can meet the requirements of radioactive exemption management standards, field investigations and data simulations were conducted in this study. Two typical uranium mines were selected for onsite investigations. Drilling mud from different layers (i.e., the upper covering layer and ore-bearing layer) and from different stages (e.g., logging stage mud, drilling expansion stage mud, and mixed mud) was sampled. For each sample, the 238U and 226Ra concentrations of the solid components and the U and 226Ra concentrations of the supernatant were analyzed. The results revealed that the highest 238U and 226Ra concentrations of the solid components were 4122 Bq/kg and 4077 Bq/kg, while the 238U and 226Ra concentrations of the mixed drilling mud were all less than 300 Bq/kg. A radioactivity estimation model was established for scenario analysis. Exemption management screening lines of waste drilling mud, which can be used to classify and treat the drilling project according to the deposit’s grade and conditions, were proposed for in situ leaching drilling projects. |
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0265-931x |
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THL @ christoph.kuells @ zhang_case_2022 |
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191 |
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Xie, T.; Lian, B.; Chen, C.; Qian, T.; Liu, X.; Shang, Z.; Li, T.; Wang, R.; Wang, Z.; Zhang, A.; Zhu, J. |
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Leaching behaviour and mechanism of U, 226Ra and 210Pb from uranium tailings at different pH conditions |
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Journal Article |
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Year |
2023 |
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Journal of Environmental Radioactivity |
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270 |
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107300 |
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Leaching experiments, Pb, Ra, U, Uranium tailings |
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A large number of radionuclides remain in uranium tailings, and U, 226Ra and 210Pb leach out with water chemistry, causing potential radioactive contamination to the surrounding environment. In this paper, uranium tailings from a uranium tailings pond in southern China were collected at different depths by means of borehole sampling, mixed and homogenised, and analysed for mineral and chemical composition, microscopic morphology, U, 226Ra and 210Pb fugacity, static leaching and dynamic leaching of U, 226Ra and 210Pb in uranium tailings at different pH conditions. The variation of U, 226Ra and 210Pb concentrations in the leachate under different pH conditions with time was obtained, and the leaching mechanism was analysed. The results showed that the uranium tailings were dominated by quartz, plagioclase and other minerals, of which SiO2 and Al2O3 accounted for 65.45% and 13.32% respectively, and U, 226Ra and 210Pb were mainly present in the residue form. The results of the static leaching experiments show that pH mainly influences the leaching of U, 226Ra and 210Pb by changing their chemical forms and the particle properties of the tailings, and that the lower the pH the more favourable the leaching. The results of dynamic leaching experiments during the experimental cycle showed that the leaching concentration and cumulative release of U, 226Ra and 210Pb in the leach solution were greater at lower pH conditions than at higher pH conditions, and the leaching of U, 226Ra and 210Pb at different pH conditions was mainly from the water-soluble and exchangeable states. The present research results are of great significance for the environmental risk management and control of radioactive contamination in existing uranium tailings ponds, and are conducive to ensuring the long-term safety, stability and sustainability of uranium mining sites. |
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THL @ christoph.kuells @ xie_leaching_2023 |
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200 |
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Hubbard, B.E.; Gallegos, T.J.; Stengel, V.; Hoefen, T.M.; Kokaly, R.F.; Elliott, B. |
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Hyperspectral (VNIR-SWIR) analysis of roll front uranium host rocks and industrial minerals from Karnes and Live Oak Counties, Texas Coastal Plain |
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Journal Article |
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Year |
2024 |
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Journal of Geochemical Exploration |
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257 |
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107370 |
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Critical minerals, Hyperspectral, Industrial minerals, Mine waste, Texas coastal plain, Uranium |
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VNIR-SWIR (400–2500 nm) reflectance measurements were made on the surfaces of various cores, cuttings and sample splits of sedimentary rocks from the Tertiary Jackson Group, and Catahoula, Oakville and Goliad Formations. These rocks vary in composition and texture from mudstone and claystone to sandstone and are known host rocks for roll front uranium occurrences in Karnes and Live Oak Counties, Texas. Spectral reflectance profiles, 569 in total, were reduced to 125 representative spectral signatures, which were analyzed using the U.S. Geological Survey’s (USGS) Material Identification and Characterization Algorithm (MICA). MICA uses an automated continuum-removal procedure together with a least-squares linear regression to determine the fit of observed sample spectral absorption features to those of reference mineral standards in a spectral library. The reference minerals include various clay, mica, carbonate, ferric and ferrous iron minerals and their mixtures. In addition, absorption feature band-depth analysis was done to identify rock surfaces exhibiting absorption features related to uranium and zeolite minerals, which were not included in the command files used to execute MICA. Rocks from each of the four geologic units produced broadly similar spectral signatures as a result of comparable mineral compositions, but there were some notable differences. For example, Ca- and Na-montmorillonite was matched most frequently to the spectral absorption features in 2-μm (∼2000–2500 nm) wavelengths, while goethite occurred often at 1-μm (∼400–1000 nm) wavelengths. The latter is related to limonitic iron-staining in and around oxidized zones of the uranium roll front as described in previous papers. Rocks of the Jackson Group differed from those of the Catahoula, Oakville and Goliad units in that the former exhibited spectral features we interpret as being due to the presence of lignite-bearing mudstone layers. Goliad rocks exhibit spectral features related to dolomite, gypsum, anhydrite, and an unidentified green clay mineral that is possibly glauconite. Jackson Group rocks also exhibit weak but well-resolved absorption features at 964 and 1157 nm related to either or both zeolite minerals clinoptilolite and heulandite. These zeolite minerals and a few spectra exhibiting hydrous silica absorption features are indicative of alteration of volcanic glass in tuffaceous mudstone and claystone layers. A few sample spectra exhibited strong absorption features at around 1135 nm related to the uranium mineral coffinite. Both the 1135 nm coffinite and 1157 nm zeolite absorption features overlap somewhat, potentially making them difficult to distinguish without additional hyperspectral field, laboratory or remote sensing data. The results of this study were compared to mixtures of minerals described for ore, gangue and alteration minerals in deposit models for sandstone-hosted uranium, sedimentary bentonite and sedimentary zeolite. Use of these spectra can help facilitate mapping of both waste materials from the legacy mining of the above commodities, as well as future exploration and resource assessment activities. |
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THL @ christoph.kuells @ hubbard_hyperspectral_2024 |
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178 |
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Singh, A.; Patel, S.; Bhadani, V.; Kumar, V.; Gaurav, K. |
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Title |
AutoML-GWL: Automated machine learning model for the prediction of groundwater level |
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2024 |
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Engineering Applications of Artificial Intelligence |
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127 |
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107405 |
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AutoML, Bayesian optimisation, Groundwater, Machine learning |
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Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management. |
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0952-1976 |
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THL @ christoph.kuells @ singh_automl-gwl_2024 |
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168 |
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