A comprehensive review on the implementation of the biorefinery concept in biodiesel production plants
Bibliographic record
Abstract
Biodiesel is a promising alternative to petroleum diesel and its production from various generations of feedstocks by using different technologies has been constantly growing globally. However, in spite of such large scale of production, serious considerations should be taken into account to ensure the long-term sustainability of biodiesel production. This issue becomes more of concern given the fact that some generations of feedstocks used for biodiesel production are in clear conflict with food security. The concept of biorefinery has been at the center of attention with an aim to address these challenges by promoting an integral use of biomass to allow the production of multiple products along with biodiesel. Such implementation has been extensively studied over the last years and is expected to lead to economic, environmental, and social advantages over individual processes. The current review first presented an overview on biodiesel, its different feedstocks, and production technologies. Subsequently, the biorefinery concept and its correct implementation was technically discussed. Biodiesel production under the biorefinery scheme was also presented. Finally, techno-economic analysis of biodiesel production under the biorefinery concept by considering palm oil-based biorefinery as case study was investigated.
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.
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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".