Neutral fat hydrolysis and long‐chain fatty acid oxidation during anaerobic digestion of slaughterhouse wastewater
Bibliographic record
Abstract
Neutral fat hydrolysis and long-chain fatty acid (LCFA) oxidation rates were determined during the digestion of slaughterhouse wastewater in anaerobic sequencing batch reactors operated at 25 degrees C. The experimental substrate consisted of filtered slaughterhouse wastewater supplemented with pork fat particles at various average initial sizes (D(in)) ranging from 60 to 450 microm. At the D(in) tested, there was no significant particle size effect on the first-order hydrolysis rate. The neutral fat hydrolysis rate averaged 0.63 +/- 0.07 d(-1). LCFA oxidation rate was modelled using a Monod-type equation. The maximum substrate utilization rate (kmax) and the half-saturation concentration (Ks) averaged 164 +/- 37 mg LCFA/L/d and 35 +/- 31 mg LCFA/L, respectively. Pork fat particle degradation was mainly controlled by LCFA oxidation rate and, to a lesser extent, by neutral fat hydrolysis rate. Hydrolysis pretreatment of fat-containing wastewaters and sludges should not substantially accelerate their anaerobic treatment. At a D(in) of 450 microm, fat particles were found to inhibit methane production during the initial 20 h of digestion. Inhibition of methane production in the early phase of digestion was the only significant effect of fat particle size on anaerobic digestion of pork slaughterhouse wastewater. Soluble COD could not be used to determine the rate of lipid hydrolysis due to LCFA adsorption on the biomass.
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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 | 0.000 |
| 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 | 0.000 | 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".