Depositional characteristics of Ningdong coal under a reducing atmosphere
Bibliographic record
Abstract
Abstract As an important raw coal for gasification, the deposition characteristics of Ningdong coal have a great influence on the long‐term stability of the gasifier. In this work, a one‐dimensional drop‐tube furnace was employed to examine the depositional behaviour of Ningdong coal under a reducing atmosphere at different temperatures and deposition times. The thickness and morphology of the minerals in the deposits were analyzed by Digimizer image analysis software, X‐ray diffraction, and scanning electron microscopy–electron dispersion spectroscopy (SEM–EDS). With the temperature increases from 1050 to 1300°C, the thickness of the deposit at each deposition time monotonously decreases, whereas the deposition rate initially decreases until reaching its minimum at 1100°C before increasing again at higher temperatures. Furthermore, the deposit morphology changes from a grain‐like form into a floccule‐like form as the temperature increases to 1050°C, and the deposition rate increases with deposition time. It was found that Fe‐containing minerals are the dominant factors of deposit formation based on SEM–EDS analysis. The transformation of the mineral matters of the coal ash at each temperature was simulated using FactSage computational thermochemistry software. It was found that the Fe‐containing minerals responsible for deposit formation are clinopyroxenes (FeO) and liquid slag (Fe 2 O 3 ). This work could be used as a theoretical guide to reveal the mechanism of deposit formation with Ningdong coal gasification.
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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 | 0.000 |
| 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 | 0.000 | 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".