Financial analyses of potential biojet fuel production technologies
Bibliographic record
Abstract
Abstract Bio‐based jet fuels are projected by the International Civil Aviation Organization (ICAO) to play a major role in meeting greenhouse gas emissions reduction targets. Recent literature has identified promising pathways for biojet fuel production, including several pathways approved by the ASTM International. Despite the importance of this topic, only a few studies have examined the financial metrics of biojet production, and different assumptions make it difficult to compare results. This paper evaluates and compares the financial viability of six key biojet fuel production pathways using appropriate biomass feedstocks. The pathways were analyzed from a technical and financial perspective, utilizing a common discounted cash flow approach and Monte Carlo analysis, considering internal (e.g. scale‐up to commercial scale) and external (e.g. oil price) uncertainties. The hydroprocessed esters and fatty acids technology with oil feedstock had the most promising financial results, with an internal rate of return of over 26% and a 70% probability of exceeding the minimum attractive rate of return (MARR = 15%) even under the most pessimistic scenario. The next most attractive pathway was catalytic hydrothermolysis, which had favorable financial performance, but only under a scenario that assumed an oil price range of $93 to $140 per barrel. Pyrolysis and gasification with Fischer‐Tropsch synthesis presented high financial risk under an oil price range of $50 to $93 per barrel and low technical development scenarios, whereas the alcohol‐to‐jet and direct‐fermentation‐to‐jet technologies were found to be unlikely to achieve the MARR for any of the scenarios. © 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".