New heterogeneous process for continuous biodiesel production in microreactors
Bibliographic record
Abstract
ABSTRACT This contribution investigates the transesterification of soybean oil with methanol in the presence of demineralized (DM) water plant sedimentation as a heterogeneous catalyst in a microreactor, which has not been studied in previous works. The catalyst systems were characterized by the X‐ray diffraction (XRD) method. The effects of catalyst concentration, methanol/oil volume ratio, and residence time on the transesterification efficiency were investigated and the purity of methyl ester was optimized using response surface methodology (RSM). The optimal conditions for the transesterification process were as follows: catalyst concentration of 0.0837 g/g, methanol to oil volume ratio of 1:1.89, and residence time of 10 min during which methyl ester purity was measured at ∼93.14 %. However, the purity value obtained by experimental model is equal to 87.06 %. Through the analysis of model and experimental data, mean relative error was obtained as 7.17 %. Experimental results indicated that time for the production of methyl ester with high purity (93.14 %) can be shortened significantly in an optimized microreactor compared to conventional stirred reactors (residence time of only 10 min).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".