Bibliographic record
Abstract
Abstract Everyday people use cooking oil at home and various commercial establishments in the hospitability industry. Particularly hotels and restaurants are generating 0.1 million tons /year of waste cooking oil in India and other countries such as US (0.3–0.4 million tons), EU (0.7–1 million tons), United Kingdom (0.2 million tons), and Canada (0.135 million tons). However, most of the used vegetable oils are still regarded as waste materials and disposed of without any such adequate use, which leads to not only environmental pollution but also an enormous wastage. These used vegetable oils have capabilities to be a potential feedstock for production of bio fuel by transesterification reaction and consequently leads to low cost bio fuel production. The objective of this study is to find an immediate alternative and sustainable energy solution from using waste vegetable oil for replacement of fossil fuel. The present article mainly deals with description of the continuous transesterification process along with optimization of the process parameters. Also it covers the advanced technology that is utilized for the generation of biofuel with design of portable biofuel generation plant with higher efficiency. This process would exhibit several advantages such as, (i) low temperature reaction (50–60°C), (ii) fast reaction with complete process taken less than an hour and (iii) high quality bio fuel and it meets EU standard.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".