Drop‐in biofuel production via conventional (lipid/fatty acid) and advanced (biomass) routes. Part I
Bibliographic record
Abstract
Abstract Drop‐in biofuels that are ‘functionally identical to petroleum fuels and fully compatible with existing infrastructure’ are needed for sectors such as aviation where biofuels such as bioethanol/biodiesel cannot be used. The technologies used to produce drop‐in biofuels can be grouped into the four categories: oleochemical, thermochemical, biochemical, and hybrid technologies. Commercial volumes of conventional drop‐in biofuels are currently produced through the oleochemical pathway, to make products such as renewable diesel and biojet fuel. However, the cost, sustainability, and availability of the lipid/fatty acid feedstocks are significant challenges that need to be addressed. In the longer‐term, it is likely that commercial growth in drop‐in biofuels will be based on lignocellulosic feedstocks. However, these technologies have been slow to develop and have been hampered by several technoeconomic challenges. For example, the gasification/Fischer‐Tropsch ( FT ) synthesis route suffers from high capital costs and economies of scale difficulties, while the economical production of high quality syngas remains a significant challenge. Although pyrolysis/hydrothermal liquefaction ( HTL ) based technologies are promising, the upgrading of pyrolysis oils to higher specification fuels has encountered several technical challenges, such as high catalyst cost and short catalyst lifespan. Biochemical routes to drop‐in fuels have the advantage of producing single molecules with simple chemistry. However, the high value of these molecules in other markets such as renewable chemical precursors and fragrances will limit their use for fuel. In the near‐term, (1–5 years) it is likely that, ‘conventional’ drop‐in biofuels will be produced predominantly via the oleochemical route, due to the relative simplicity and maturity of this pathway. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".