Toward sustainable propylene: A comparison of current and future production pathways
Bibliographic record
Abstract
Propylene, a fundamental chemical, has witnessed a significant surge in demand in recent decades, establishing itself as the second most primary intermediate compound after ethylene. Propylene manufacturing currently depends on non-renewable resources, specifically naphtha or propane from fossil sources. The conventional methods are economically feasible and mature; however, they emit greenhouse gases and consume non-renewable resources. Therefore, it is necessary to transition to more sustainable production methods. This review aims to provide and analyze many possible routes for the production of propylene using sustainable resources. The categorization of these pathways is determined by the raw material employed for the manufacture of propylene. Out of the several paths considered, bio-propane dehydrogenation stands out as a viable option for producing propylene in the future. Furthermore, this study examines and reports on the analysis of catalyst selection, the design of operating conditions, and the yield and selectivity of propylene in each pathway. Zeolite-based catalysts, particularly ZSM-5, exhibit remarkable selectivity in propylene synthesis across several processes. To fully comprehend the sustainability and feasibility of these paths, this research also reviews environmental impact and techno-economic metrics of several established propylene production methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".