Advances in microalgal lipid extraction for biofuel production: a review
Bibliographic record
Abstract
Abstract Algal biomass is an attractive feedstock for sustainable biofuel production because of its high growth rate and the fact that it does not compete with food crops. This review examines progress made in the processing and extraction of microalgal lipids as feedstocks for algae‐derived biofuels. The discussion focuses on lipid extraction processes but also mentions drying, cell disruption, and transesterification processes because of their potential effect on the extraction process, and because of the possibility of performing them simultaneously with extraction. Some of the common themes discussed include the benefits of utilizing wet microalgal biomass, combining process steps (process intensification), and the importance of considering the entire life cycle when assessing the ‘greenness’ of a technology. Lipid extraction technologies will need to be improved for microalgal biofuels to compete effectively with fossil fuels, particularly through the development of energy‐efficient extraction methods and the adaptation of these methods for large‐scale production. © 2018 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".