Bibliographic record
Abstract
Biodiesel is a renewable energy source produced from natural oils and fats, and is being used as a substitute for petroleum diesel. The aim of this study was to investigate the potential of using spent coffee grounds for biodiesel production and its by-products to produce pelletized fuel, which is expected to help the biodiesel production process achieve zero waste. For this experiment, spent coffee grounds sample was collected from Kaldis coffee, Addis Ababa, Ethiopia. Extraction of the spent coffee grounds oil was then conducted using n-hexane, ether and mixture of isopropanol to hexane ratio (50:50 %vol), and resulted in oil yield of 15.6, 17.5 and 21.5 %w/w respectively. A two-step process was used in biodiesel production with conversion of about 82 %w/w. The biodiesel quality parameters were evaluated using the American Standard for Testing Material (ASTM D 6751). The major fatty acid compositions found by Gas chromatography were linoleic acid (37.6%), palmitic acid (39.8%), oleic (11.7%), and stearic acid (8.6%). In addition, solid waste remaining after oil extraction and glycerin ratio (glycerin content from 20-40%) was evaluated for fuel pellet (19.3-21.6 MJ/Kg) applications. Therefore, the results of this work could offer a new perspective to the production of biofuel from waste materials without growing plants and/or converting food to fuel.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".