Coal and Gangue Active Identification Method Using Microwave Irradiation-Infrared Detection
Bibliographic record
Abstract
In the process of the longwall top coal caving method, automatic distinction between coal and gangue at the working face is one of the most critical factors for the success of the operation. An active coal and gangue identification method using microwave irradiation combined with infrared detection is proposed in this paper. Coal and gangue are irradiated with microwave to actively enhance the external differences between them, and then the quantitative data of the difference are quickly collected by a noncontact infrared thermal imager, to perform identification of coal and gangue. Using theoretical analysis and laboratory experiments, the physical and chemical properties of coal and gangue are analyzed in order to reveal the thermal sensitivity of coal and gangue to microwave irradiation. The influences of the coal and gangue particle size, microwave irradiation time and microwave frequency on the thermal sensitivity to microwave irradiation are investigated. The experimental results show that the average temperature rise in coal is approximately 1.5 times that in gangue material under the same microwave irradiation conditions. This supports the feasibility of this identification method, and provides theoretical and experimental bases for achieving rapid and accurate identification of coal and gangue in top coal caving operations.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".