| |
Records |
Links  |
|
Author |
Saini, K.; Singh, P.; Bajwa, B.S. |
|
| |
Title |
Comparative statistical analysis of carcinogenic and non-carcinogenic effects of uranium in groundwater samples from different regions of Punjab, India |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Applied Radiation and Isotopes |
Abbreviated Journal |
|
|
| |
Volume |
118 |
Issue |
|
Pages |
196-202 |
|
| |
Keywords |
Carcinogenic, Groundwater, LED fluorimeter, Uranium |
|
| |
Abstract |
LED flourimeter has been used for microanalysis of uranium concentration in groundwater samples collected from six districts of South West (SW), West (W) and North East (NE) Punjab, India. Average value of uranium content in water samples of SW Punjab is observed to be higher than WHO, USEPA recommended safe limit of 30µgl−1 as well as AERB proposed limit of 60µgl−1. Whereas, for W and NE region of Punjab, average level of uranium concentration was within AERB recommended limit of 60µgl−1. Average value observed in SW Punjab is around 3–4 times the value observed in W Punjab, whereas its value is more than 17 times the average value observed in NE region of Punjab. Statistical analysis of carcinogenic as well as non carcinogenic risks due to uranium have been evaluated for each studied district. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0969-8043 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
THL @ christoph.kuells @ saini_comparative_2016 |
Serial |
130 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Silva, M.L. da; Bonotto, D.M. |
|
| |
Title |
Uranium isotopes in groundwater occurring at Amazonas State, Brazil |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
Applied Radiation and Isotopes |
Abbreviated Journal |
|
|
| |
Volume |
97 |
Issue |
|
Pages |
24-33 |
|
| |
Keywords |
Amazon area, Dissolved uranium, Groundwater, Tube wells, U/U activity ratio |
|
| |
Abstract |
This paper reports the behavior of the dissolved U-isotopes 238U and 234U in groundwater providing from 15 cities in Amazonas State, Brazil. The isotope dilution technique accompanied by alpha spectrometry were utilized for acquiring the U content and 234U/238U activity ratio (AR) data, 0.01–1.4µgL−1 and 1.0–3.5, respectively. These results suggest that the water is circulating in a reducing environment and leaching strata containing minerals with low uranium concentration. A tendency to increasing ARs values following the groundwater flow direction is identified in Manaus city. The AR also increases according to the SW–NE directions: Uarini→Tefé; Manacapuru→Manaus; Presidente Figueiredo→São Sebastião do Uatumã; and Boa Vista do Ramos→Parintins. Such trends are possibly related to several factors, among them the increasing acid character of the waters. The waters analyzed are used for human consumption and the highest dissolved U content is much lower than the maximum established by the World Health Organization. Therefore, in view of this radiological parameter they can be used for drinking purposes. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0969-8043 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
THL @ christoph.kuells @ silva_uranium_2015 |
Serial |
140 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Haque, N.; Norgate, T. |
|
| |
Title |
The greenhouse gas footprint of in-situ leaching of uranium, gold and copper in Australia |
Type |
Journal Article |
| |
Year |
2014 |
Publication |
Journal of Cleaner Production |
Abbreviated Journal |
|
|
| |
Volume |
84 |
Issue |
|
Pages |
382-390 |
|
| |
Keywords |
Copper, GHG emission, Gold, In-situ leaching, LCA, Uranium |
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0959-6526 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
THL @ christoph.kuells @ haque_greenhouse_2014 |
Serial |
208 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Singh, A.; Patel, S.; Bhadani, V.; Kumar, V.; Gaurav, K. |
|
| |
Title |
AutoML-GWL: Automated machine learning model for the prediction of groundwater level |
Type |
Journal Article |
| |
Year |
2024 |
Publication |
Engineering Applications of Artificial Intelligence |
Abbreviated Journal |
|
|
| |
Volume |
127 |
Issue |
|
Pages |
107405 |
|
| |
Keywords |
AutoML, Bayesian optimisation, Groundwater, Machine learning |
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0952-1976 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
THL @ christoph.kuells @ singh_automl-gwl_2024 |
Serial |
168 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Emparanza, A.R.; Kampmann, R.; Caso, F.D.; Morales, C.; Nanni, A. |
|
| |
Title |
Durability assessment of GFRP rebars in marine environments |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Construction and Building Materials |
Abbreviated Journal |
|
|
| |
Volume |
329 |
Issue |
|
Pages |
127028 |
|
| |
Keywords |
Composite FRP rebar, Durability, Service life, Marine structures, Reinforced concrete |
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0950-0618 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
THL @ christoph.kuells @ Ruizemparanza2022127028 |
Serial |
83 |
|
| Permanent link to this record |
