The effect of an enzymatic pretreatment on the hydrolysis and size reduction of fat particles in slaughterhouse wastewater
Bibliographic record
Abstract
Abstract The effect of an enzymatic pretreatment, Pancreatic Lipase 250 (PL‐250), on the hydrolysis and size reduction of fat particles in slaughterhouse wastewater was characterised for enzyme doses ranging from 125 to 1000 mg dm−3 and initial particle sizes (Din) varying between 53 and 383 µm. Treatment with PL‐250 significantly reduced the size of pork fat particles in slaughterhouse wastewater. Particle size reduction increased with Din, possibly due to the more filamentous and plate‐like configuration of the larger fat particles, which could be easily broken at weak points. The smaller particles were observed to be denser and more spherical. Size reduction also increased with enzyme concentration, but the benefit of adding more enzyme diminished greatly as enzyme dose was increased. The maximum long‐chain fatty acid (LCFA) concentration in filtered samples was detected after 4–7 h of treatment and ranged from 8.2 to 34.9 mg dm−3. The linear rate of LCFA released in solution during enzymatic pretreatment ranged from 39.4 to 169.9 mg dm−3 d−1, and increased with enzyme concentration up to 500 mg dm−3. At a PL‐250 concentration of 1000 mg dm−3, the LCFA release rate decreased, maybe due to excessive layering of adsorbed enzyme on the fat particles or increased degradation of released LCFAs. The pretreatment appeared to be more efficient with beef than pork fat particles. However, the effect of an enzymatic pretreatment on a downstream anaerobic treatment of slaughterhouse wastewater containing fat particles remains to be tested. © 2001 Society of Chemical Industry
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".