Polypropylene to transportation fuel grade hydrocarbons over γ-alumina catalyst
Bibliographic record
Abstract
• Polypropylene is converted to transportation-grade fuels in high yields (88 wt.%). • Response surface methodology was employed to optimize the process. • Mild conditions were employed and achieved high activity. • Catalyst was reusable and stable in subsequent recycles. Catalytic upgrading of plastics to valuable fuels and chemicals is an attractive route to valorize waste plastics. Herein, catalytic pyrolysis of polypropylene was performed over γ-Al 2 O 3 as a heterogeneous catalyst to produce fuel-grade hydrocarbons. The use of an inexpensive γ-Al 2 O 3 catalyst and mild reaction conditions led to high liquid yield selectively in gasoline-range hydrocarbons which stands out from most of the work reported in the literature for polypropylene pyrolysis. The reaction conditions of pyrolysis were optimized by the Box-Behnken Design approach utilizing the response surface methodology. The highest liquid yield of 88.1 wt.% was obtained at 470 °C temperature, with 2 wt.% of catalysts and 5 h reaction time. The amount of solid carbon was insignificant (0.7 wt.%) and the gas yield was 11.2 wt.%. The γ-Al 2 O 3 showed high efficiency and stability for converting polypropylene to liquid fuels. The catalyst was highly stable, reusable, and showed similar catalytic activity for 3 recycles. These features and the highly selective conversion of PP to gasoline range fuels are crucial for large-scale applications. The GC–MS analysis revealed that the liquid fuel produced mostly contained C8 to C15 hydrocarbons encompassing mostly gasoline and a small fraction of diesel fuel and higher hydrocarbons. The GC–MS data was also supported by SimDist analysis, which exhibited the boiling point ranging from 100 °C to 260 °C for the liquid fuel product. The reaction temperature and time had a significant impact on the liquid yield. The higher temperature favored the formation of the gaseous product of C1-C3 hydrocarbons. The NMR analysis showed that the liquid products mostly contained the highest amount of paraffins followed by olefins and a small fraction of aromatics. The presence of mild acidity in the γ-Al 2 O 3 catalyst and optimum reaction condition provides favorable conditions to produce the highest yield of transportation fuel grade hydrocarbons without over-cracking into gases.
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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 | 0.000 | 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 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".