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Dai, Jiajia

University of British Columbia

Traditional geotechnical and geometallurgical tests are laboratory based, have strict sample requirements, and depend on the preparation of samples; due to these characteristics, traditional tests cannot realize rapid and real-time ore strength and hardness monitoring. Meanwhile, various sensors have been applied in mining engineering in exploration and operation, proving their mineralogical feature identification capabilities. The influence of mineralogy on ore strength and hardness has been widely discussed and defined as one of the major factors related to rock strength. To evaluate the capability and applicability of sensor scanning for ore hardness prediction and improve the efficiency and immediacy of geotechnical and geometallurgical tests, this thesis conducted a laboratory-scale investigation of X-ray transmission (XRT) scanning and a high-resolution strength testing response to copper-gold porphyry half core samples. The laboratory procedure included manual mineral identification, XRT scanning and strength measurement. The qualitative analysis results of XRT and high-resolution strength data showed that both the XRT and the high-resolution strength tester had apparent responses to major mineral veins and mineral assemblages. Metal-bearing minerals (e.g., sulfide, iron oxide) showed high energy attenuation values and high relative densities by XRT, while quartz gave low values on these two parameters by XRT. The high-resolution tester response was related to the Mohs hardness of minerals. Generally, soft minerals decrease the strength, while hard minerals increase the strength. The quantitative analysis between XRT and high-resolution strength data revealed that it was not possible to correlate with a simple mathematical equation. Two separate characteristic values of XRT and high-resolution strength were calculated to study their response to surface mineral composition, collected by micro X-ray fluorescence scanning, μXRF. Results showed that both XRT and strength data had a strong correlation with several minerals. A multiple variable linear regression model was developed to relate μXRF to XRT responses. However, the regression of μXRF to strength data was found not reliable. According to current results, mineral textures affect ore strength, and XRT has the potential to scan and indicate changes in ore strength, but it was not possible to predict strength using either the XRT or μXRF scans.

Text

2024-04-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/84337

Mining Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/