3D geometallurgical characterization of coal mine waste rock piles for their reprocessing purpose
Bibliographic record
Abstract
Abstract Jerada coal mining generates extensive coal mine waste rock (CMWR) piles rich in valuable minerals, posing environmental challenges and economic opportunities. This study examines reprocessing feasibility through 3D geometallurgical characterization. Sampling used down the hole hammer drilling technique (DTH) and drone surveys for topographical precision. Over 620 samples from (T01, T02, T08) underwent comprehensive analyses including particle size distribution, x-ray fluorescence (XRF), total sulfur/carbon analysis (S/C), and inductively coupled plasma mass spectrometry (ICP-MS) for physical–chemical characterization. Mineralogical aspects were explored via optical microscopy (OM), X-ray diffraction (XRD), scanning electron microscopy (SEM), electron probe microanalysis (EPMA), and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Quantitative mineral evaluation by scanning electron microscope (QEMSCAN) provided mineral insights. Chemical data was used in a 3D block model to quantify residual coal. Results for the three examined CMWR piles (T01, T02, and T08) showed varying D80 from 160 to 300 µm, notable carbon content averaged 12.5 wt% (T01), 16 wt% (T02), and 8.5 wt% (T08). Sulfur presence exceeded 1 wt% in T08, and potential environmental concerns due to iron sulfides. Anthracite liberation was below 30 wt%. 3D modeling estimated a total volume of 7 Mm3, mainly from T08, equaling 11.2 Mt. With its high carbon content and substantial tonnages, re-exploitation or alternative applications could minimize these CMWR piles environmental impact.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".