Comparative studies of co-conversion of waste activated sludge and lignocellulosic wastes through hydrothermal liquefaction
Bibliographic record
Abstract
The effects of feedstock type on hydrothermal liquefaction were explored through the co-conversion of waste activated sludge and various lignocellulosic biomass sources, including birchwood sawdust, cornstalk, and waste newspaper. This investigation aimed to produce bio-oil under consistent conditions at 310 °C and a 10-min reaction time, with subsequent comparison to results obtained using single feedstocks. The co-feeding of sludge with cornstalk and sawdust demonstrated the highest bio-oil yields at 34.2 % and 33.7 % wt%, respectively. The comprehensive characterization of the bio-oil products revealed that feedstock type influenced elemental composition and, consequently, the higher heating value of the bio-oils. Bio-oils derived from co-feeds exhibited a significant presence of nitrogenous compounds, esters, and fatty acids in contrast to the high percentage of phenolic compounds found in bio-oils from single feedstocks. Furthermore, these co-feed bio-oils displayed lower molecular weights, higher quantities of low boiling point compounds, increased volatile matter content, and reduced fixed carbon content compared to bio-oils produced from single feedstocks. • Co-conversion of wastewater sludge and lignocellulosic biomass was investigated. • A maximum bio-oil yield in the range of 33–34 wt% was obtained using mixed feedstock. • Co-conversion resulted in higher quality bio-oils compared to single feedstock. • By-products of the process can be used for energy generation or as fertilizers.
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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 | 0.001 |
| 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) | 0.001 | 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".