Perspectives on Thermochemical Recycling of End-of-Life Plastic Wastes to Alternative Fuels
Bibliographic record
Abstract
Due to its resistance to natural degradation and decomposition, plastic debris perseveres in the environment for centuries. As a lucrative material for packing industries and consumer products, plastics have become one of the major components of municipal solid waste today. The recycling of plastics is becoming difficult due to a lack of resource recovery facilities and a lack of efficient technologies to separate plastics from mixed solid waste streams. This has made oceans the hotspot for the dispersion and accumulation of plastic residues beyond landfills. This article reviews the sources, geographical occurrence, characteristics and recyclability of different types of plastic waste. This article presents a comprehensive summary of promising thermochemical technologies, such as pyrolysis, liquefaction and gasification, for the conversion of single-use plastic wastes to clean fuels. The operating principles, drivers and barriers for plastic-to-fuel technologies via pyrolysis (non-catalytic, catalytic, microwave and plasma), as well as liquefaction and gasification, are thoroughly discussed. Thermochemical co-processing of plastics with other organic waste biomass to produce high-quality fuel and energy products is also elaborated upon. Through this state-of-the-art review, it is suggested that, by investing in the research and development of thermochemical recycling technologies, one of the most pragmatic issues today, i.e., plastics waste management, can be sustainably addressed with a greater worldwide impact.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".