Numerical and Experimental Evaluation of Microwave Treatment of Rocks for Application in Mining Excavation
Bibliographic record
Abstract
Efficiently breaking hard rocks remains a persistent challenge in mining and geotechnical engineering.Traditional explosive-based methods such as blasting, while effective in some cases, bring about a host of issues including but not limited to low productivity rate, noise pollution, dust, vibration, mine collapse, and safety hazards.Consequently, the mining sector seeks to transition from non-continuous explosive-based techniques to continuous mining approaches.However, continuous mining methods suffer from drawbacks such as the short life span of the cutting blades, especially when dealing with hard rocks.Rock pre-conditioning has emerged as a promising solution to address the challenges and facilitate the shift towards continuous mining.Among the various pre-conditioning methods, microwave treatment has achieved significant attention over the past decade.This research endeavours to assess the efficacy of microwave treatment as a rock pre-conditioning method through a combination of experimental and numerical analyses for field applications and introduce potential ways for its improvement.A comprehensive series of experiments, including microwave treatment, calorimetric measurements, thermal imaging, and rock mechanical testing, are conducted using a novel energy efficiency-based approach.Additionally, a fully coupled numerical model is developed to simulate heat transfer and energy absorption behaviours in rocks during microwave treatment and is validated against experiments conducted in this study.The developed numerical model is leveraged to enhance the energy efficiency of microwave treatment experiments.Through the integration of an energy efficiency-focused perspective, this research offers new insights into the potential application of microwave treatment for rock pre-conditioning in field settings.Moreover, the developed numerical model contributes to a deeper understanding of the mechanisms underlying microwave treatment and aids in achieving superior energy efficiency in experimental and potential field outcomes."Madness is like gravity…all it takes is a little push."
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".