Financial analyses of potential biojet fuel production technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it