Determination of Acid Number of Biodiesel and Biodiesel Blends
Bibliographic record
Abstract
Abstract Due to an increase in the commercial use of biodiesel and biodiesel blends, both ASTM D 6751 and EN 14214 include the acid number (AN) as an important quality parameter. It was found that determination of AN of biodiesel and biodiesel blends using the ASTM D 974 results in large values of repeatability (up to 73.41%) and larger percentage error (up to 42.88%). Therefore, ASTM D 974 has been modified using a lower concentration of base (0.02 M KOH instead of 0.1 M KOH) as well as reducing the amount of toxic titration solvent from 100 mL to only 10 mL. This makes the modified ASTM D 974 as a green analytical method which uses a reduced amount of toxic solvent. This modified method significantly reduced the maximum percentage error from 42.88 to 5.92%. The application of this modified ASTM D 974 for the determination of AN of biodiesel and biodiesel blends was studied. The accuracy of this modified ASTM D 974 for biodiesel (B100) was measured to be within 3.51% over the AN range of 0.313–0.525 mg KOH/g and maximum repeatability was decreased from 8.37 to 2.75% within this AN range which is far below the ASTM D 974 stated repeatability specifications. For B20, B10, B5, B2, and B1, the most accurate values were measured at AN values of 0.177, 0.067, 0.072, 0.126, and 0.096 mg KOH/g, respectively. Excellent linearity values of R2 for calculated and experimentally determined AN were obtained. The difference between the experimental and the calculated AN for all biodiesel and biodiesel blend samples was within ± 0.018 mg KOH/g. This extensive study has demonstrated that this modified ASTM D 974 is a reliable method for the determination of AN and could be used for establishing the specifications of AN for biodiesel and biodiesel blends ranging from B1 to B20 in quality standards.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".