Study on the slurry ability and combustion behaviour of coal‐bioferment residue of drugs‐slurry
Bibliographic record
Abstract
Abstract Bioferment residue of drugs (BRD) is one kind of harmful and hazardous substance which has complex compositions. Thus, proper treatment of these residues has become an important concern. BRD can be utilized harmlessly and economically by mixing it with coal powder, water, and dispersant to prepare slurry fuel, named as coal‐BRD‐slurry (CBRDS). In this study, the influence of BRD on the slurry ability and combustion behaviour of CBRDS was investigated with coal water slurry (CWS) as reference. Results showed that the dispersant can reduce the slurry viscosity of CBRDS and CWS, and its optimum dosage was 0.6 wt% on the dried coal basis. The fixed‐viscosity concentration of CBRDS decreased with increasing BRD dosage. CBRDS and CWS exhibited shear‐thinning behaviour. The rheological behaviour was fitted by the power law. CBRDS exhibited higher apparent viscosity and stronger shear‐thinning behaviour because of the flocculent structure and well‐developed pores of the BRD. BRD can improve the stability of CWS by decreasing solid‐liquid separation of the slurry. Addition of BRD decreased the ignition temperature and increased the ash residues, suggesting a synergistic effect of CWS and BRD occurred during the combustion of CBRDS.
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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 | 0.000 |
| 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".