Vegetable Oil Deacidification by Methanol Heterogeneously Catalyzed Esterification in (Monophasic Liquid)/Solid Batch and Continuous Reactors
Bibliographic record
Abstract
The removal of free fatty acids (FFA) in vegetable oils is an important pretreatment in the production of biodiesel, in particular, when the starting materials are low-cost feedstocks. Heterogeneously catalyzed esterification with methanol transforms FFA in fatty acid methyl ester (FAME), decreasing the oil acidity and producing biodiesel simultaneously. The equilibrium of this reaction shifts toward the desired product when increasing the methanol content, but at the same time, a double-liquid-phase system forms when the methanol content is higher than 6–8 wt %. The presence of a double liquid phase can be an important drawback in the reactor. A detailed study about the optimization of the methanol quantity is presented, both using a batch and a packed-bed reactor (PBR) at different temperatures (between 60 and 105 °C) using Amberlyst 46 (ion-exchange resin) as the heterogeneous catalyst. The deacidification of sunflower oil in a monophasic liquid system leads to satisfactory results (final FFA lower than 0.5 wt %) for both of the reactors. The experimental results demonstrate that the excess of methanol is not convenient in terms of both slower reaction rates and mass of reactant used. The stability of Amberlyst 46 in the PBR was positively verified after 600 h of work.
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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".