Characterisation of Neem and Jatropha Curcas Oils and their Blends with Kerosene for Combustion in Liquid Bio Fuels Cooking Stoves
Bibliographic record
Abstract
The characterisation of raw vegetable oils of neem and jatropha curcas seeds was experimentally carried out in order to obtain requisite data for the design process of liquid bio fuels cooking stoves. Properties of nineteen vegetable oils/kerosene blends including the kerosene sample were also experimentally determined for the purpose of testing the designed and developed bio stoves that utilised these fuel/oils blends as fuels. Results of the characterisation revealed that the kinematic viscosity of jatropha oil (57.6 mm 2 /s) was 36 times more than the viscosity of the kerosene sample (1.6 mm 2 /s). On the other hand, the viscosity of neem oil (62.6 mm 2 /s) was 39 times greater than that of the kerosene sample. In addition, the density of jatropha curcas oil (860 kg/m 3 ) was more than that of the kerosene (760 kg/m 3 ) by 13.16%, while the density of neem oil (890 kg/m 3 ) exceeded that of kerosene sample by 17.11%; all the tests were conducted at 30 o C. Meanwhile, the acid number values of jatropha (1.2 mg KOH/g) and neem (3.1 mg KOH/g) oils did not meet the ASTM D6751 acid number standard specification, and only jatropha curcas oil satisfied the DIN 51605 specification. Blending of the oils with kerosene ensured that all the kerosene/jatropha oil blends met the DIN 51605 specification, however only six kerosene/neem oil blends (10% to 60% concentrations) satisfied the requirement. Moreover, among all the kerosene/oils blends, only the 10% and 20% jatropha and 10% neem oils concentrations in the blends met the ASTM D6751 standard acid number specification.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".