Bunch Ash biomass source for the synthesis of Al2(SiO4)2 magnetic nanocatalyst and as alkali catalyst for the synthesis of biodiesel production
Bibliographic record
Abstract
This work employed the Admixture of oil from winter squash seed oil and duck waste fat for the synthesis of biodiesel using a derived heterogeneous catalyst from burnt Arecaceae kernel empty bunch (BAKEB). The admixture oil was obtained using the gravity ratio method and the properties of the oils were determined. The developed BAKEB was characterized using SEM, FTIR, XRF-FT, BET-adsorption, and qualitative analysis. Transesterification of the admixture oil to biodiesel was carried out in a single transesterification batch reactor, while Process optimization was carried out via RSM-CCD with four constraint variables namely: reaction period, catalyst conc., reaction temperature, and E-OH/OMR, respectively. The spent catalyst was recycled and reused and the quality of the produced biodiesel was compared with the recommended standard. Results showed the admixture oil ratio of 48:52 was sufficient to produce a validated optimum biodiesel yield of 99.42% (wt./wt.) at the reaction time of 55 min, catalyst conc. of 3.00 (%wt.), reaction temperature of 60 °C, and E-OH/OMR of 5.5:1 (vol./vol.), respectively. ANOVA analysis indicated that all variables were mutually significant at p-value<0.0001.The developed BAKEB was found to contain high percentages of Al-K-Na-Ca. The catalyst recyclability test indicated that BAKEB can be refined and reused. The produced biodiesel qualities have fuel properties similar to conventional diesel when compared with ASTM D6751 and EN 14,214. The study concluded that the blending of winter squash seed oil with duck waste fat in the ratio of 48:52 as feedstock for biodiesel synthesis is viable.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".