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EN
A rockburst is a common engineering geological hazard. In order to predict rockburst potential in kimberlite at an underground diamond mine, a decision tree method was employed. Based on two fundamental premises of rockburst occurrence, σθ, σc, σt, WET are determined as indicators of rockburst, which are also partition attributes of the decision tree. 132 training samples (with 24 incomplete samples) were obtained from real rockburst cases from all over the world to build the decision tree. The decision tree based on 108 complete samples was built with an accuracy of 73% for 15 validation samples while another decision tree based on 132 samples (with 24 groups of incomplete data) shows an accuracy of 93% for validation samples. Hence, the second decision tree was employed for kimberlite burst prediction. 12 samples from lab tests and a numerical model were used as test samples. The results indicate a moderate burst liability which matches real situations at the diamond mind in question.
2
Content available remote Evaluation of burst liability in kimberlite using support vector machine
EN
Due to the complex mechanisms of rockburst, there is no current effective method to reliably predict these events. A statistical learning method, support vector machine (SVM), is employed in this paper for kimberlite burst prediction. Four indicators σθ,σc,σt,WET are chosen as input indices for the SVM, which is trained using 108 groups of rockburst cases from around the world. Data uniformization is used to avoid negative impact of differing dimensions across the original data. Parameter optimization is embedded in the training process of the SVM to achieve optimized predictive ability. After training and optimization, the SVM reaches an accuracy of 95% in rock burst prediction for validation samples. The constructed SVM is then employed in kimberlite burst liability evaluation. The model indicated a moderate burst risk, which matches observed instances of rockburst at a diamond mine in north Canada. The SVM method ignores the focus on rockburst mechanisms, instead relying on representative indicators to develop a predictive model through self-learning. The prediction results show an excellent accuracy, which means this method has a potential application in rockburst prediction.
EN
All of the uranium mines in Canada are located in the Athabasca Basin, and the bulk of Canadian uranium production comes from two underground mines: McArthur River Mine and Rabbit Lake Mine. The third undeveloped Canadian uranium mine, the Cigar Lake Mine is expected to supply almost 15% of the world's uranium production. One of the most important considerations when designing an underground uranium mine is the design of an effective ventilation system which would be able to dilute radon concentration below the allowable exposure limits or prevent the influx of radon into the mine openings. This paper discusses the design and development of ventilation systems at underground high grade uranium mines. The paper talks about pros and cons of the currently used ventilation systems at uranium mines in Northern Saskatchewan based on the 16 years perspective gained by the authors on these projects.
PL
Wszystkie kopalnie uranu w Kanadzie są położone w Zagłębiu Athabasca, a większość kanadyjskiej produkcji uranu pochodzi z dwóch podziemnych kopalni: McArthur River Mine i Rabbit Lake Mine. Trzecia niezagospodarowana jeszcze kanadyjska kopalnia uranu Cigar Lake Mine ma w przyszłości dostarczać niemal 15% światowej produkcji uranu. Jednym z najistotniejszych zagadnień w trakcie projektowania podziemnej kopalni uranu jest zaprojektowanie efektywnego systemu wentylacyjnego, który byłby w stanie obniżyć stężenie radonu poniżej dopuszczalnych norm lub zapobiec napływowi radonu do wyrobisk udostępniających. Niniejszy artykuł omawia projektowanie i rozwój systemów wentylacji w podziemnych kopalniach rud o wysokiej zawartości uranu. Artykuł przedstawia także argumenty za i przeciw w odniesieniu do stosowanych obecnie systemów wentylacji w kopalniach uranu w Northern Saskatchewan na podstawie 16-letnich doświadczeń zdobytych przez autorów w trakcie realizacji tych projektów.
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