Effect of Sludge Concentration and Crude Glycerol Matrix as a Substrate on the Production of Single-Cell Oil by Oleaginous Yeast Yarrowia lipolytica SKY7
Bibliographic record
Abstract
The disposal of excess crude glycerol produced by the booming biodiesel industry and wastewater sludge solid waste has become a severe problem, and alternate routes of use and valorization of these waste byproducts are needed. The use of cheaply available wastewater sludge solids in fermentation media is very much desirable to reduce the cost of production. The strains of Yarrowia lipolytica can assimilate a wide array of waste substrates, such as crude glycerol, waste cooking oil, starch wastewater, and cellulosic. This study optimized the concentration of wastewater sludge solids (5–35 g/L) to be used with crude glycerol in fermentation media to produce microbial oil as feedstock for biodiesel production. The results indicated that 20 g/L of sludge solids with 40 g/L of crude glycerol resulted in highest lipid content of 29.35% in 96 h. Further, assuming wet extraction of lipids, it was found that at least 11.2% or higher lipid content is required for this process to have an overall positive net solid waste reduction. Insignificant inhibition was observed by the crude glycerol used in this study as compared to pure glycerol, which proves it to be an adequate source of carbon substrate for lipid production.
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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) | 0.001 | 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 | 0.001 |
| 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".