Incorporation of Biosolids as Water Replacement in a Two-Step Renewable Hydrocarbon Process: Hydrolysis of Brown Grease with Biosolids
Bibliographic record
Abstract
ABSTRACT: The accumulating volumes of biosolids in lagoons worldwide have intensified the need to develop innovative wastewater treatment strategies. Here, we provide proof-of-concept for the incorporation of biosolids into the hydrolysis step of a two-step thermal conversion of lipids for production of renewable hydrocarbons, which can be utilized as renewable fuels. Brown grease was hydrolysed with biosolids or water at 260-280 °C for 60 min at a mass ratio of 1:1 feed to water or biosolids. The feedstock and products were characterized using various analytical techniques to compare the performance of biosolids to water. The results indicated that there was no significant difference in the degree of hydrolysis of brown grease when biosolids was used as water replacement. The fatty acids composition after hydrolysis when biosolids was used as a water replacement also remained largely unchanged. Hydrolysis of brown grease with biosolids could be achieved at pH ranging from 3.3 to 8.9, and at a lower than previously established temperature. Significantly, the rapid settling of solid material in biosolids observed after thermal hydrolysis of brown grease may reduce the necessity of biosolids settling lagoons. Thus, incorporation of biosolids into a lipid hydrolysis-pyrolysis process may simultaneously benefit the biofuel and waste management sectors.
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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) | 0.000 | 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 | 0.001 | 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".