Kinetic comparison of two basic heterogenous catalysts obtained from sustainable resources for transesterification of waste cooking oil
Bibliographic record
Abstract
Alkaline earth metal oxides are appropriate catalysts for biodiesel production and among them, CaO and MgO are known for possessing the best efficiency. In this study, catalysts synthesized from economical and sustainable resources were used for biodiesel production. More specifically, waste mussel shells and demineralized (DM) water treatment precipitates as calcium and magnesium carbonate sources, were converted into calcium and magnesium oxides at temperatures above 900 oC. Methanol and waste cooking oil were reacted in a 250 mL two-necked flask at 24:1 and 22.5:1 ratios in presence of 12 and 9.08 wt% of mussel shell-based and DM water treatment precipitates-based catalysts, respectively. The effects of temperature (328, 333, 338, 343 and 348 K) and time (1, 3, 5, 7 and 8 h) at a stirrer speed of 350 rpm on the conversion of the oil into biodiesel were investigated. The results obtained indicated a pseudo-first order kinetics for the transesterification reaction using both catalysts. The activation energies in the presence of the DM water treatment precipitates and mussel shell catalysts were measured at 77.09 and 79.83 kJ.mol-1, respectively. Accordingly, the DM water treatment precipitates catalyst resulted in a faster reaction due to its lower activation energy value. Moreover, the catalysts were reused five times and the results obtained showed that the methanol-driven extraction of CaO contained in the DM water treatment precipitates catalyst was lower than the waste mussel shell catalyst proving the higher stability of the new heterogeneous catalyst i.e. the calcinated DM water treatment precipitates.
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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) | 0.001 | 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".