Conversion of end-of-life waste streams to low carbon fuels and materials in a circular economy model
Bibliographic record
Abstract
Problem statement: Post-consumer and industrial wastes involving plastics quickly lose their physicochemical integrity and utility value, hence becoming a global environmental pollutant damaging the entire ecosystem. Heterogeneity and end value have hampered recycling; Only 9 % plastics is recycled globally, with Canada and US recycling 6 and 4 %, respectively. Conventional technologies such as incineration solves mass to volume waste problem but adds to pollution levels. Like other world economies, Canada and US diverts 82 and 73% plastic wastes to landfills. However, landfills have become pollution hubs due to soil and water leachates, and air pollution, thus the need for a paradigm shift to sustainable safe approaches to waste management. The purpose of this study is to investigate the hybrid conversion process of unsorted waste streams and non-hazardous reaction media to produce low carbon fuels (H₂) and materials (char and oil). Method & Experimental procedure: Leveraging on superior water sub-and-supercritical properties, and the negation for the feedstock drying step, competitive hydrothermal conversion technology was investigated at different reaction conditions. Results: Preliminary batch results revealed 0.041g H₂/g feed and 0.005g CH₄/g feed; 88.2 % char yield with improved thermal stability; up to 16.22% liquid yield containing high value components such as phenols, syringols, Guaiacol, cresols, etc. Conclusions & Significance: Hybrid hydrothermal conversion addresses environmentally unsafe and challenging heterogenous wastes by converting them to green high value products in a circular economy model. Further studies on continuous scale, catalysis and various waste feedstocks are underway to fully optimize the operation parameters in preparation for future commercial adoption.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".