Conversion of Sustainable Oil into Jet Fuel Using Low Pressure Green Hydrogen
Bibliographic record
Abstract
The ever-increasing demand for aviation fuel has strategically placed sustainable aviation fuel (SAF) as a key solution to decarbonise the aviation industry towards net zero emission.The sector currently contributes over 1bn tonnes of CO2 per annum, over 2% of the entire global amount.Sustainable oil conversion into synthetic fuel by catalytic deoxygenation (DO) is one of the distinctive research topics in biorefinery to achieve SAF.The process focuses to convert oxygenates present in sustainable oil into hydrocarbon, which is then upgraded and refined further to form drop-in fuel i.e., gasoline, aviation fuel or diesel.The present study revolves around the creation of highly active sulfided catalyst for the purpose of removing oxygenates in sustainable oil derived from lipid-based material using either low pressure molecular hydrogen (H2) or in-situ green H2 production from limonene dehydrogenation.Unlike typical catalyst synthesis method, a unique catalyst synthesis process was utilised, involving a single-step calcination of thiomolybdate salts under inert atmosphere to form a catalyst composition that consist of bulk molybdenum disulfides (MoS2) and structural carbon.Subsequently, the catalyst was analysed using extensive characterization techniques to understand the chemical composition and catalyst morphology.Initial assessment to convert model compound (fatty acids) in batch reactor system indicated that the catalyst exhibited remarkable HDO conversion under low pressure hydrogen test conditions, outperforming conventional refinery catalyst.Additionally, the study highlighted the importance of bulk catalyst over supported catalyst for deoxygenation reactions of oxygenated compound, as supported catalyst inherently limits number of active sites available for the reactions especially at the designated test conditions.The capability of the catalyst was further proven with the complete conversion of lipid materials at low pressure hydrogen, highlighting promising potential of the catalyst in real world application.
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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) | 0.000 | 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 | 0.001 |
| 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".