Thermochemical process of polypropylene plastic waste recovered from electric and electronic apparatuses-to-clean hydrogen energy
Bibliographic record
Abstract
This study investigates the thermochemical gasification of polypropylene plastic waste recovered from electronic and electrical apparatuses to produce clean hydrogen and syngas. A thermodynamic model was developed to simulate the gasification process using steam as the gasification agent. The model's accuracy was validated by comparing the syngas composition with experimental data from previous studies. The root mean square error (RMSE) of smaller than 2 confirmed the high precision of the model. The study explores the effects of key parameters-process temperature, steam-to-plastic waste ratio (SPWR), and moisture content-on the syngas composition. An increase in process temperature from 950 to 1250 K led to a rise in the molar percentage of H 2 from 64.86 % to 67.62 %, while CH 4 decreased from 4.50 % to nearly 0 %. A similar increase in CO from 21.32 % to 28.35 % was observed, while CO 2 decreased from 9.31 % to 4.02 %. Furthermore, increasing the SPWR from 1 to 3 resulted in a significant increase in H 2 from 58.47 % to 69.44 % and a decrease in CH 4 from 7.77 % to 0.04 %. Simultaneous optimization of temperature and SPWR further enhanced the molar percentage of H 2 by 36 %, from 51.30 % to 69.70 %. These results demonstrate that optimizing the gasification process by controlling temperature and SPWR is crucial for improving hydrogen yield and reducing methane and carbon dioxide emissions, contributing to cleaner energy production.
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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 | 0.000 | 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) | 0.001 | 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 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".