Evaluation of microalgal alternative jet fuel using the AHP method with an emphasis on the environmental and economic criteria
Bibliographic record
Abstract
The negative environmental impact of the aviation industry, related mainly to the gaseous emissions from turbine exhausts, is increasing with the increased demand on travel. In addition to the adverse environmental effects, the currently used aviation fuel is posing economic burdens on the air transport sector, with the increase in crude oil prices. Therefore, the aviation industry is investigating the potential of substituting the currently used aviation fuel with alternative fuels—mainly those derived from biofuels. Of all the available sources of biofuels, numerous studies indicate that those derived from algae seem to be the most promising, in terms of providing a viable and sustainable alternative to fossil fuels. This study explores the feasibility of microalgal jet fuel, taking into consideration technological, environmental, and economic aspects, using the analytic hierarchy process (AHP). Two scenarios are explored, with one stressing on the environmental importance and the second on the economic importance of the alternative jet fuel. The results indicate that microalgal derived jet fuel can only compete with conventional jet fuel, when giving the environmental criterion the higher weight. © 2012 American Institute of Chemical Engineers Environ Prog, 32: 721–733, 2013
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".