Catalytic gasification of lignite with KOH in supercritical water
Bibliographic record
Abstract
Abstract Supercritical water gasification of lignite with KOH was investigated in a batch autoclave on a wide range of conditions, that is residence time (5–30 min), mass ratio of H2O to lignite (0.5–10), temperature (400–600°C) and mass ratio of KOH to lignite (0–20%). Although lignite is difficult to be gasified in supercritical water, the results indicated that gasification efficiency about 47% was achieved at 30 min, 550°C with 10% KOH. Higher mass ratio of H2O to lignite and temperature can lead to higher gas yields and GE. It is also observed that the solid residue was dispersed at low feed concentration and consolidated at high concentration. Besides KOH's improvement of water–gas shift reaction, in this case, it is also observed that KOH also enhances the gasification of aromatics of lignite in supercritical water, which leads to higher gasification efficiency of lignite.
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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) | 0.000 | 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".