Study on the slurrying mechanism of coal water slurry prepared from coal gasification wastewater
Bibliographic record
Abstract
Abstract The coal gasification process produces a large amount of wastewater which is seriously polluted and difficult to biochemically treat. The regasification of coal water slurry produced from gasification wastewater meets the requirements of clean and efficient use of energy and the concept of circular economy. In this paper, the slurryability of coal water slurry prepared with gasification wastewater was measured, and the influence mechanism of organic matter, metal ions, and ammonia nitrogen components in coal gasification wastewater on slurryability has been studied. Results show that (a) Coal water slurry can be prepared with gasification wastewater, and the composition of wastewater has a great influence on the slurryability. (b) Phenols and alcohols in wastewater are not conducive to slurryability, while urethane in wastewater is beneficial for slurrying. (c) K + and Na + in wastewater have little effect on the slurryablity even in high concentration, while Mg 2+ and Ca 2+ have basically no effect on the slurryability under the concentration range of coal gasification wastewater. However, Fe 3+ has a negative effect and Cu 3+ has a positive effect on the slurryability at low concentrations. (d) Ammonia nitrogen can affect the slurryability of coal water slurry by affecting the pH of the solution. NH 4 OH solution is alkaline, which is conducive to slurrying, while (NH 4 ) 2 SO 4 and NH 4 Cl solutions are acidic, resulting in poor slurryability of coal water slurry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".