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Haque, N., & Norgate, T. (2014). The greenhouse gas footprint of in-situ leaching of uranium, gold and copper in Australia. Journal of Cleaner Production, 84, 382–390.
Abstract: In-situ leaching (ISL) is a chemical method for recovering useful minerals and metals directly from underground ore bodies which is also referred to as ‘solution mining’. ISL is commonly used for uranium mining, accounting for about 45% of global production. The main benefits are claimed to be a lower environmental impact in terms of visual disturbances, emissions, lower energy use, cost compared with conventional open-cut or underground mining methods, and potential utilisation of lower grade resources. However, there is a lack of reported studies on the assessment of the environmental impacts of ISL, particularly greenhouse gas (GHG) emissions using life cycle assessment (LCA) methodology. The SimaPro LCA software was used to estimate the GHG footprint of the ISL of uranium, gold and copper. The total GHG emissions were estimated to be 38.0 kg CO2-e/kg U3O8 concentrate (yellowcake), 29 t CO2-e/kg gold, and 4.78 kg CO2-e/kg Cu. The GHG footprint of ISL uranium was significantly lower than that of conventional mining, however, the footprints of copper and gold were not much less compared with conventional mining methods. This is due to the lower ore grade of ISL deposits and recovery compared with high ore grades and recovery of conventional technology. Additionally, the use of large amount of electricity for pumping in case of ISL contributes to this result. The electricity consumed in pumping leaching solutions was by far the greatest contributor to the well-field related activities associated with ISL of uranium, gold and copper. The main strategy to reduce the GHG footprint of ISL mining should be to use electricity derived from low emission sources. In particular, renewable sources such as solar would be suitable for ISL as these operations are typically in remote locations with smaller deposits compared with conventional mining sites.
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Singh, A., Patel, S., Bhadani, V., Kumar, V., & Gaurav, K. (2024). AutoML-GWL: Automated machine learning model for the prediction of groundwater level. Engineering Applications of Artificial Intelligence, 127, 107405.
Abstract: 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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Emparanza, A. R., Kampmann, R., Caso, F. D., Morales, C., & Nanni, A. (2022). Durability assessment of GFRP rebars in marine environments. Construction and Building Materials, 329, 127028.
Abstract: Technologies developed over the last two decades have facilitated the use of glass fiber reinforced polymer (GFRP) bars as internal reinforcement for concrete structures, specially in coastal environments, mainly due to their corrosion resistance. To-date, most durability studies have focused on a single mechanical parameter (tensile strength) and a single aging environment (exposure to high alkalinity). However, knowledge gaps exists in understanding how other mechanical parameters and relevant conditioning environments may affect the durability of GFRP bars. To this end, this study assesses the durability for different physio-mechanical properties of GFRP rebars, post exposure to accelerated conditioning in seawater. Six different GFRP rebar types were submerged in seawater tanks, at various temperatures (23°C, 40°C and 60°C) for different time periods (60, 120, 210 and 365 days). In total six different physio-mechanical properties were assessed, including: tensile strength, E-modulus, transverse and horizontal shear strength, micro-structural composition and lastly, bond strength. It was inferred that rebars with high moisture absorption resulted in poor durability, in that it affected mainly the tensile strength. Based on the Arrhenius model, at 23°C all the rebars that met the acceptance criteria by ASTM D7957 are expected to retain 85% of the tensile strength capacity.
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Marteleto, T. de P., Abreu, A. E. S. de, Barbosa, M. B., Yoshinaga-Pereira, S., Bertolo, R. A., & Enzweiler, J. (2024). Groundwater apparent ages and isotopic composition in Crystalline, Diabase and Tubarão aquifers contact area in Campinas, Southeastern Brazil. Journal of South American Earth Sciences, 135, 104783.
Abstract: This study refines the hydrogeological conceptual model of an area with three interconnected aquifers, namely the Crystalline Aquifer System (CAS – igneous and metamorphic rocks), which is in contact with the Tubarão Aquifer System (TAS – sedimentary rocks) and the Diabase Aquifer System (DAS – diabase rocks). The detailed investigation involved geophysical logging and hydraulic and hydrodynamic characterization with straddle packers in a local tubular well, in which groundwater presents high uranium concentrations. Hydrogeochemical and isotope (δ2H, δ18O, 3H, δ13C, 14C) analysis in this well and in other three neighboring wells, with lower U concentrations, showed that ancient and modern waters (3H from <0.8 to 1.12 TU, 14C from 69.43 to 78.72 pMC) mix within the aquifer. During groundwater pumping, vertical fractures in the diabase aquifer possibly induce water mixing and recharge of the deeper levels of the aquifers from shallow layers. The high [U] are related to ancient waters from a confined aquifer hosted in CAS that reaches the wells through hydraulically active fractures located deeper than 159 m depth. Groundwater apparent ages do not increase systematically with depth, revealing a complex circulation model for CAS. The results obtained from the other wells, which are all located on drainage lineaments, reveal that one extracts modern water from DAS and TAS, another one extracts modern and ancient water from DAS and CAS, and the third extracts only ancient water from CAS, confirming the complexity of the local hydrogeology. Regarding regional groundwater management, the study revealed the need to characterize the sources of groundwater in each well, in order to protect modern waters from anthropogenic contamination and to protect ancient groundwater from overexploitation, as CAS hosts groundwaters recharged thousands of years ago or more.
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Pastukhov, A. M., Rychkov, V. N., Smirnov, A. L., Skripchenko, S. Y., & Poponin, N. A. (2014). Purification of in situ leaching solution for uranium mining by removing solids from suspension. Minerals Engineering, 55, 1–4.
Abstract: This study investigated the process of in situ leaching (ISL) method of uranium mining, and the removal of solid particles from the leaching solution. Investigations were carried out for 4months. The content of firm suspensions in the productive solutions arriving from the well field was up to standard of 3–5mg/l. After keeping in a settler of productive solutions within one hour concentration of suspensions decreases to 2–2.5mg/l. To increase the life of the wells requires more fine purification of the ISL solutions. The best results can be obtained but using filtration. Bag filters were used in experiments carried out at the extraction site. All samples of polypropylene bag filter was produced by the Tamfelt Corporation. The best results were obtained for fabrics S-51M03-L2K4 (pore size 3μm). After three month of trials following indicators of wells work were fixed: on the trial cell decrease in intake capacity did not occur; on the other cells of well field injectability of holes for the same period of time decreased for 15–40%. The results illustrated the high efficiency of this method, which allows injection wells to reach a constant intake capacity, making it possible for technological cells to achieve a constant productivity and balance. Purification of solutions allows to reduce acidulation term of new technological cells from 3–4 to 1.5–2months.
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