Advancing the application of bio-oils by co-processing with petroleum intermediates: A review
Bibliographic record
Abstract
Crude bio-oils, as sustainable and renewable energy sources generated from thermochemical conversion of forest, agriculture, waste and algae biomass feedstocks, have attracted particular attention to partially and even completely replace the fossil fuels over the past decades. However, due to their undesirable qualities such as high oxygen content, thermal instability, and high corrosivity, further upgrading is required for the direct application of bio-oils for petrol engines or thermal power plants. Various upgrading pathways, including emulsification , hydrotreating , supercritical fluid treatment, and co-processing are being investigated by different international research groups to produce marketable drop-in renewable transportation biofuels. Among them, co-processing bio-oils with petroleum streams in existing refineries is recognized as a more promising solution compared to other conventional upgrading methods because of less capital investment and higher fuel productivity. This work reviewed the up-to-date research activities in bio-oil co-processing including process scale-up, focusing more on the most recent work about pyrolysis oils co-processing in the fluid catalytic cracking (FCC) unit and its industrial implementation. The significant knowledge gaps in the co-processing are also outlined for future investigations.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".