Comparative evaluation of grape seed and Kusum oil methyl esters as renewable fuels using alkaline catalysts
Bibliographic record
Abstract
Abstract The present study focuses on the production of biodiesel through the transesterification process and investigates the physicochemical properties of grape seed and Kusum oils methyl ester. Optimization of key process parameters, including molar ratio, catalyst concentration, reaction time, and temperature, was conducted for both oils. The effects of these factors on biodiesel production and conversion efficiency were analyzed. A 3 × 3 × 3 completely randomized design asymmetrical factorial approach was used to optimize reaction conditions. A total of 54 experiments were conducted to assess the effect of various parameters on ester recovery efficiency and kinematic viscosity. For grape seed oil methyl ester, optimal conditions were determined to be 0.5 wt.% KOH catalyst, 4:1 molar ratio, a reaction temperature of 60°C, and a reaction time of 60 min, resulting in a yield of 99% grape seed oil methyl ester with a viscosity of 4.25 cSt. In contrast, the optimal conditions for Kusum oil methyl ester included an 8:1 molar ratio, 1.5 wt.% KOH catalyst, a reaction temperature of 60°C, and 60‐min reaction time, achieving 95.58% yield of Kusum oil methyl ester with a viscosity of 9.53 cSt. The results indicate that grape seed oil methyl ester is a superior choice compared to Kusum oil methyl ester in terms of biodiesel yield and kinematic viscosity. The physicochemical properties of both esters, including kinematic viscosity, density, flash and fire points, cloud and pour points, and calorific value, met the ASTM D6751 and EN 14214 standards, confirming their suitability as alternative fuels for diesel engines.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".