Different feedstocks and operating parameters on catalytic cracking of waste cooking oil using <scp>NiO</scp> ‐ <scp> Fe <sub>3</sub> O <sub>4</sub> </scp> ‐ <scp>ZnO</scp> / <scp>AC</scp>
Bibliographic record
Abstract
Abstract The catalytic cracking of waste cooking oil (WCO) presents a sustainable pathway for renewable fuel production. This study investigates the performance of a newly developed activated carbon (AC) supported trimetallic oxide catalyst, NiO–Fe₃O₄–ZnO/AC, for the efficient conversion of WCO into liquid hydrocarbon, including WCOs from different oil‐bases, effect of operating conditions, and recyclability. The catalyst was synthesized via wet impregnation and tested in a batch reactor using three types of WCO—derived from palm oil, canola oil, and sunflower oil—to evaluate the influence of feedstock fatty acid composition. Key reaction parameters, including temperature, residence time, and catalyst loading, were systematically studied. Under optimal conditions (400 °C, 60 min, 3 wt.% catalyst), the system achieved a liquid yield of 93.17 wt.%, with 63.27% selectivity toward n‐alkanes and minimal coke formation (0.06 wt.%). Notably, the catalyst exhibited both strong deoxygenation capability, and reusability. This work highlights the catalyst's versatility across different feedstocks and establishes its potential for sustainable biofuel production.
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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".