Bibliographic record
Abstract
Coal is highly heterogeneous in nature, and for this reason, several analytical techniques are needed for its characterization so as to accurately predict its behavior during conversion processes such as combustion, gasification, or liquefaction. Conventional analyses such as proximate analysis, ash analysis, and ash fusion temperatures assume coal as a homogeneous material and provide only bulk properties. The performance correlations based on these analyses are unable to describe adequately the impact of coal quality on conversion efficiencies and plant performance. A number of advanced bulk analytical techniques, such as FTIR and 13 C NMR, provide information on the organic structure of coal. Chemical fractionation technique provides information on the inorganic matter present in coal in a form other than mineral grains. Bulk analysis techniques such as XRD and SIROQUANT provide information on the types of minerals present in coal. Thermomechanical analysis (TMA) an advanced bulk analytical technique provides detailed thermal behavior of ash relevant to power-plant operations. Several advanced characterization techniques have emerged recently which consider pulverized coal as a heterogeneous material made up of individual particles and are able to examine these coal particles in much greater detail. An automated reflectogram (AR) technique provides a variation of reflectivity a measure of heterogeneity in the organic part. A computer-controlled scanning electron microscopy (CCSEM) analysis technique has been developed over the last 25 years to provide much more detailed information on mineral matter in coal and mineral−coal associations in pulverized coal. The paper discusses the details of these techniques and how the analysis from these techniques is used in modeling procedures to provide a better understanding of coal conversion behavior.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".