Comparative Evaluation of Thermogravimetric and Refractive Index Techniques in Determining Biodiesel Yield
Bibliographic record
Abstract
Biodiesel is a clean and renewable resource that consists of mono-alkyl esters of long chain fatty acid, which could be obtained from the transesterification reaction of vegetable oils and animal fats with alcohols and catalysts. Biodiesel yield has typically been determined using expensive and laborious techniques. The attempt of this study was to examine the potential of quantifying the biodiesel conversion in real time using refractive index in transesterification process of canola oil with methanol and KOH. Biodiesel yields at five different mixing intensities and reaction times were measured using a refractometer. The measured results were then compared with analytical data obtained from thermogravimetric analysis (TGA) technique over a temperature range of 25℃ - 600℃. Experimental results indicated that the FAME conversions at different mixing intensity and reaction time measured from refractometer correlated well to the relative weight losses from TGA method with R2 = 0.93 (p ≤ 0.05); however, the refractometer may over-estimate the biodiesel yield when the reaction rate was too low. Overall, the refractometer technique is cheaper and easier to manage and could provide a reliable prediction of biodiesel yield in real time.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".