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| Author |
Lach, P.; Cathelineau, M.; Brouand, M.; Fiet, N. |
| Title |
In-situ Isotopic and Chemical Study of Pyrite from Chu-Sarysu (Kazakhstan) Roll-front Uranium Deposit |
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Journal Article |
| Year |
2015 |
Publication  |
Procedia Earth and Planetary Science |
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| Volume |
13 |
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207-210 |
| Keywords |
LA-ICP-MS, pyrite, roll-front, SIMS, sulfur isotopy, traces elements, uranium |
| Abstract |
Pyrite is common in roll-front type uranium deposit in Chu-sarysu basin, Kazakhstan. Combined in-situ microstructural, isotopic and chemical analysis of pyrite indicates variation in precipitation conditions and in fluid composition. Broad-scale δ34S heterogeneity indicates a complex multi-facet evolution. First generation authigenic framboïdal aggregates are biogenic as demonstrated by the lowest δ34S values of -48‰ to -28‰. The latest generation pyrites are probably hydrothermal with greater δ34S variation (-30‰ to +12‰). This hydrothermal pyrite commonly displays variable enrichment of several trace elements especially As, Co and Ni. Strong variation in δ34S values and variable trace element enrichment is interpreted in terms of continuous variations in fluid composition. |
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1878-5220 |
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THL @ christoph.kuells @ lach_-situ_2015 |
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182 |
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Hu, K.; Wang, Q.; Tao, G.; Wang, A.; Ding, D. |
| Title |
Experimental Study on Restoration of Polluted Groundwater from in Situ Leaching Uranium Mining with Sulfate Reducing Bacteria and ZVI-SRB |
Type |
Journal Article |
| Year |
2011 |
Publication |
Procedia Earth and Planetary Science |
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| Volume |
2 |
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Pages |
150-155 |
| Keywords |
In situ leaching of uranium, nitrate radical, sulfate radical, Sulfate-Reducing Bacteria (SRB), Zero Valent Iron (ZVI) |
| Abstract |
In the case of in situ leaching of uranium, the primitive geochemical environment for groundwater is changed since leachant is injected into the water beaving uranium deposit. This increases the concentration of uranium and results in the groundwater contamination.Microbial reduction technology by Sulfate reducing bacteria and Zero Valent Iron were employed to treat uranium wastewater. The experiments were conducted to evaluate the influence of anion (sulfate and nitrate) on dealing with uranium wastewater. Experimental results show that the utilization of both SRB system and ZVI – SRB system to process uranium wastewater is affected by sulfate ion and nitrate ion. As the concentration of sulfate radical is lower than 4000mg/L, sulfate-reducing bacteria has no influence on precipitated uranium. However, as the concentration of sulfate is more than 6,000mg/L, uranium removal rate decreases significantly, from 80% to 14.1%. When adding sulfate radical on ZVI – SRB system to process uranium wastewater, its uranium removal rate is higher than SRB system. Low concentration of nitrate contributes to reduction metabolism of SRB. High concentration of nitrate inhibits the growth and metabolism of SRB and affects the treatment efficiency of uranium wastewater. When the concentration of nitrate reaches 1500mg/L, uranium removal rate is less than 0.1%. Nevertheless, as the concentration of nitrate is lower than 1000mg/L, uranium removal rate could reach more than 75%. As existence of nitrate radical, uranium removal rate of SRB by adding ZVI is higher than that without adding. |
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THL @ christoph.kuells @ hu_experimental_2011 |
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202 |
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| Author |
Merembayev, T.; Yunussov, R.; Yedilkhan, A. |
| Title |
Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits |
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Journal Article |
| Year |
2019 |
Publication |
Procedia Computer Science |
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| Volume |
150 |
Issue |
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Pages |
46-52 |
| Keywords |
classification, geophysics logging data, machine learning, stratigraphy, uranium deposit |
| Abstract |
Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words – it is a great tool to describe non-linear phenomena. We tried to use this technique to improve existing process of stratigraphy, and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for stratigraphy boundaries classification based on geophysics logging data for uranium deposit in Kazakhstan. Correct marking of stratigraphy from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting, k nearest neighbour and XGBoost. |
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1877-0509 |
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THL @ christoph.kuells @ merembayev_machine_2019 |
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113 |
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Frey, S.; Külls, C.; Schlosser, C. |
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New Hydrological Age-Dating techniques using cosmogenic radionuclides Beryllium-7 and Sodium-22 |
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Conference Article |
| Year |
2011 |
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Proc. IAEA Conf. Monacco |
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THL @ christoph.kuells @ Frey2011new |
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48 |
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Hamutoko, J.; Mapani, B.; Ellmies, R.; Bittner, A.; Külls, C. |
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A fingerprinting method for the identification of uranium sources in alluvial aquifers: An example from the Khan and Swakop Rivers, Namibia |
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Journal Article |
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2014 |
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Physics and Chemistry of the Earth, Parts A/B/C |
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72 |
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34-42 |
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Pergamon |
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THL @ christoph.kuells @ Hamutoko2014fingerprinting |
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19 |
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