Making ore sorting a robust preconcentration process in the hard-rock mining industry: low-frequency electromagnetic analysis of ores using AC energized coils
Bibliographic record
Abstract
Over recent years, both in Canada and globally, the majority of ore deposits at shallow to medium depth are found to be of low sub-economic grade and are required to be excavated at high tonnages. As such, the infrastructure and operating cost associated with fine whole ore processing becomes prohibitive rendering some projects unfeasible. The adoption of a preconcentration process that can be conducted at a coarse size reduces the amount of material that needs to be taken to a subsequent fine processing stage. This will not only make a mining operation more feasible, from the economic point of view, but also environmentally friendlier due to an overall reduction in plant foot-print. In the modular context, preconcentration technology aims at optimizing a mining operation.Thanks to advances in processing technology over recent years, there are now a number of processing methodologies available that are capable of performing separation of ore and waste at a relatively coarse size. Examples include: dense medium separation, jigging, ore sorting, coarse flotation, and magnetic separation. The mineralogy of the ore determines the potential for preconcentration as well as the most suitable processing technology.The work presented in this thesis is in joint collaboration with MineSense Technologies Ltd. a leader in the mining industry that aims at enhancing the sustainability of mining by improving the ore extraction and metal recovery process. The aim is to analyze and improve MineSense’s current technology used in conductivity sorting, a recently employed preconcentration process in the hard-rock mining industry. The present challenge consists of improving the deployed equipment and perform numerical analysis of available ores by potentially developing a software package that mitigates cumbrous manual testing techniques while providing further insight into conductivity sorting. The software will enable the user to model ore bodies of various formations thanks to heuristic algorithms for scattering grain components. The models can then be analyzed using an appropriate EM software to determine the feasibility of recovering the ore body.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".