Enhanced biodiesel production with improved oxidation stability by water addition to supercritical methanolysis
Bibliographic record
Abstract
Abstract Biodiesel has been established as a potential alternative fuel for petroleum diesel. However, one of the main uncertainties about biodiesel is its susceptibility to oxidation. In the present study, biodiesel has been synthesized from high‐acid value waste cooking oil (WCO) using supercritical methanolysis. The influence of supercritical reaction conditions on enhancing biodiesel's oxidation stability and yield has been extensively studied. Five independent reaction variables have been investigated, including methanol to oil (M:O) molar ratio (3–40), temperature (235–275°C), pressure (65–145 bar), time (5–30 min), and water content (0–8 vol.%). The oxidation stability has been analyzed via PetroOxy commercial devices based on the ASTM D7545‐14 methods and compared to EN14214 standards. Response surface methodology (RSM) via central composite design (CCD) has been employed to evaluate the influence of the process variables and to develop empirical models representing the reaction. Interestingly, it has been observed that water content in the feedstock would be an advantage to increase both the yield and the oxidation stability of biodiesel. An analysis of variance (ANOVA) has been used to investigate the adequacy of the predicted model at a 95% confidence level. The developed optimum conditions have achieved a yield of 99.8% and 26.21 min (oxidation stability) at 10:1 M:O molar ratio at 245°C, 125 bar, and 6 vol.% water content within 16.7 min reaction time. The predicted optimal conditions have been validated experimentally with 0.8%–0.9% relative error for both responses.
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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 | 0.000 | 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) | 0.000 | 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".