Production and Characterization of Biodiesel from Allamanda Cathertica Oil
Bibliographic record
Abstract
The gradual depletion of world petroleum reserves and the impact of environmental pollution due to increasing exhaust emissions have necessitated the urgent need to develop alternative energy resources, such as biodiesel fuel. Vegetable oil is a promising feedstock because it has several advantages; it is renewable and environmental friendly. The present study involves extraction of oil from Allamanda cathertica seed (Allamanda), Azadarachta indica and Jatropha caucus; conversion of the oil into biodiesel and the characterization of the methyl ester. Transesterification of the different feed stocks was conducted using sodium methaoxide (NaMt), sodium ethaoxide(NaEt), potassium methaoxide (PMt) and potassiumethaoxide (PEt) as catalysts, using a range of reaction temperatures (45, 50, 55, 60 and 65OC) and different rates of stirring. Result showed that Allamanda seed produced 54% oil yield using mechanical extraction. The biodiesel yield was 97% using NaMt and NaEt as catalysts, while azadarachta oil gave 95% yield with PEt catalyst alone. Jatropha oil gave a yield of 70% biodiesel with all the catalysts used. The yield of biodiesel from Allamanda oil with respect to temperature were 63, 88, 94, 46 and 20% respectively. Characterization of the biodiesel produced from Allamanda oil compared favorably with the ASTM standards, viscosity 5.4, flash point 115OC, refractive index 1.4756 and energy value 35.0MJ/L. The GCMS analysis of Allamanda methyl ester showed a range of 10 different methyl esters which includes hexadecanoic acid (24%), linoleic acid (14.8%), 13-Decosenoic acid (35.3%), 9-Octadecanoic acid (13.5%). The work concludes that allamanda oil could be a good and alternative feedstock to the edible feedstocks currently in use for biodiesel production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".