Green Technology for Biodiesel Production From Mesua Ferrea L. Seed Oil
Bibliographic record
Abstract
In this paper, the non-edible Mesua ferrea L. (MFL) seed oil with an acid value 18.8 mg KOH/g of oil, was tranesterified in the presence 1 MPa hydrogen pressure and also without the application of external pressure at 250–275 °C for 1 hour. The heterogeneous 1–5 wt.% Musa balbisiana Colla underground stem (MBCUS) ash catalyst was used during transesterification. The catalyst has a nano-metric dimension and has a versatile composition that consist of several alkali metal oxides, alkaline earth metal oxides, non-transition metal oxides and nonmetal oxides. The resultant biodiesel was separated using rotary vacuum evaporator and purified using molecular sieve and carbon columns. The biodiesel so obtained from MFL seed oil have been characterized as per available methods and found that the fuel properties are in conformity to ASTM and EN standards. 5 wt.% catalyst was found more effective as compared to other composition during ethanolysis. The effect of hydrogen pressure upon tranesterification was found negligible. Ethanol is a renewable liquid because it is made from renewable precursor such as biomass. It is expected that the use of whole renewable precursor during the process will fulfil the demand of 100% Green Technology for biodiesel production.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".