A biodiesel production process catalyzed by the leaching of alkaline metal earths in methanol: from a model oil to microalgae lipids
Bibliographic record
Abstract
Abstract BACKGROUND A response surface methodology was used to study the reaction mechanism of strontium oxide (SrO) as a catalyst for a biodiesel production process using a model oil (composed of fatty acid methyl esters (FAME), free fatty acids (FFAs) and triglycerides). The influence of several factors (initial FAME content (0–30 wt%), initial FFAs content (oleic acid 0.20–2.7 wt%), temperature (40–60 °C), methanol to model oil ratio (11–43 wt%), catalyst to model oil ratio (0.5–2.5 wt%) and reaction time (5–30 min)) on the FAME yield, the FAME content, the pH of polar phase and the biodiesel alkalinity was studied. SrO was also compared with potassium hydroxide (KOH) for the conversion of Scenedesmus Obliquus microalgae esterified lipids after a first step of FFAs esterification with sulfuric acid into biodiesel with the following conditions: temperature: 60 °C; reaction time: 22.2 min; catalyst to microalgae ratio: 2.48 wt%; methanol to microalgae lipid ratio: 31.4 wt%. RESULTS With those operating conditions, KOH was able to reach a slightly higher FAME yield (32.6% g FAME g−1 lipid) than SrO (29.0% g FAME g−1 lipid). Moreover, the results showed a strong relationship between the pH of the polar phase (glycerol–methanol–water) and the FAME yield, which indicates that the reaction using alkaline metal earths is mostly catalyzed by a homogeneous reaction. CONCLUSION The fact that alkaline metal earths act as homogeneous catalysts make them less suitable for biodiesel production, because they are not inducing neutral pH, and they increase the risk of corrosion. © 2016 Society of Chemical Industry
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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 | 0.000 | 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 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".