Effect of the main components in gasification wastewater on the surface properties of coal water slurry
Bibliographic record
Abstract
Abstract Coal water slurry is an advanced and efficient clean coal technology; using gasification wastewater to prepare coal water slurry can recycle wastewater and improve energy utilization efficiency. As the complex substances in wastewater have a great influence on the slurry properties, the effects of organic matter, metal ions, and ammonia nitrogen in gasification wastewater on the surface properties of coal water slurry are studied in this paper in order to provide new ideas for slurry mechanism of coal water slurry prepared from wastewater. Results show the following: (a) Compared with ordinary coal water slurry, the concentration of coal water slurry prepared from wastewater with high organic content increased by 2.9%, while the concentration of coal water slurry prepared from wastewater with high ammonia nitrogen content decreased by 2.1%. (b) The contact angles of coal water slurry prepared with phenols, alcohols, and urethane are reduced by 2.8°, 6.3°, and 1.5°, respectively, so organic matter can change the hydrophilicity of coal particles and affect slurryability. (c) Mg 2+ and Ca 2+ have basically no effect on slurry. Fe 3+ reduces the absolute value of Zeta potential by 33.1, and Cu 3+ increases that by 22.8, as they affect the slurryability by changing the surface potential of coal particles and the absorption of additives. (d) Ammonia nitrogen influences the slurryability by changing the pH value of the slurry. The conclusion of the influence mechanism of organic matter, metal ions, and ammonia nitrogen in wastewater on slurryability can provide a technical reference for the selection of suitable wastewater to prepare 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 | 0.000 |
| 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".