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Record W7162018767 · doi:10.82308/36663

The effects of microwave irradiation on kimberlite and associated rocks: Basis for the development of microwave-assisted rock breakage technology

2023· dissertation· en· W7162018767 on OpenAlexaboutno aff
Samir Deyab

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldChemistry
TopicMicrowave-Assisted Synthesis and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsKimberliteExcavationRock mass classificationBreakageRock mechanicsCountry rockGeomechanicsUnderground mining (soft rock)

Abstract

fetched live from OpenAlex

Finding an efficient non-explosive rock breakage method is an ongoing challenge in mining and civil engineering applications and is required for hard rock underground excavation. The explosive/ blasting techniques employed over many years have caused numerous problems, including excessive noise, dust, pollution, vibrations, and potential damage to nearby structures. As the future of underground hard rock mining is continuous mining techniques, drill-and-blast have the disadvantage of time for the fumed gasses to be cleared. Continuous mechanical excavation in hard rocks has low production rates and high operating costs, and field trials have shown that the lifespan of the cutting tools is short, reducing the efficiency of the operation. New approaches are needed to consider safety, efficiency, and economics. This thesis is part of an overall research project which has been ongoing for the past several years in the Geomechanics Laboratory at McGill University on the application of microwave irradiation to facilitate rock breakage for excavations and mineral processing. It tackles the problem of rock breaking by exploring how hard rocks might be preconditioned and weakened prior to impact by a mechanical excavator. This project investigates the effect of microwave irradiation on the mechanical strength of specific rock samples such as kimberlite, and associated rocks such as granite, limestone and basalt for comparison. Given the lack or limited knowledge on kimberlite as well as its effect under microwave irradiation.,different properties of these rock samples, such as dielectric properties, physical properties (i.e., mineral composition, rock quality designation, specific gravity, porosity, specific heat capacity and moisture content), abrasivity, were measured during the project to evaluate the heating behaviors, as well as mechanical properties of rocks subjected to microwave irradiation, were investigated. Mechanical strength tests were conducted to evaluate the effect of microwave irradiations on the strength of rock samples. The rock samples for the first time were treated for 4–360s in a multi-mode and single-mode microwave cavity at power levels from 2 to 15 kW. Also, in this study, calorimetry is performed to measure and analyze heat absorption, and thermal images are studied to determine the temperature contour on the sample surface.The unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) were significantly reduced by microwave irradiation. The tests showed that Cerchar Abrasivity Index (CAI) was not affected by microwave irradiation. Furthermore, the Percentage of the change of Pulse sound velocity (PSV) increased with microwave power level and exposure time as a result of the microcracks within the samples. The mode I fracture toughness (KIC) test results showed that KIC decreases when exposure time increases and when the samples are cut prior to treatment. It was also observed in the study that energy absorption by the samples was more when the samples were close to the horn of the microwave. Microwave treatment is found to be a promising strategy that combines microwave irradiation and mechanical techniques. The aim of potentially implementing microwave-based methods is to facilitate continuous mining and to improve the production rate while reducing the costs of fracture

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.247
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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