A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
Bibliographic record
Abstract
End of life packaging is nowadays one of the major environmental problems due to its short usage time, the low biodegradability, and the big volume occupied. In this context, gasification is one of the most promising chemical recycling techniques. Some non-recyclable or non-compostable waste gasification plants are already operating such as Enerkem Alberta Biofuels in Canada or the Sierra’s FastOx Pathfinder in California. In this review, we have examined works about plastic gasification from the last fifteen years with a specific focus on polyolefin (PP, PE), plastics mix, and co-gasification of plastic with biomass. For each of these, the best operating conditions were investigated. A very in-depth section was dedicated to supercritical water gasification (SCWG). The most used reactors in gasification processes are fluidized bed reactors together with air or steam as gasifying agents. Tar removal is commonly performed using olivine, dolomite, or nickel based catalysts. SCWG has numerous advantages including the inhibition of tar and coke formation and can be used to remove microplastics from the marine environment. In co-gasification of plastic material with coal or biomass, synergistic effects are observed between the raw materials, which improve the performance of the process, allowing to obtain higher gas yields and a syngas with a high energy content.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".