Decision analysis for plastic waste gasification considering energy, exergy, and environmental criteria using TOPSIS and grey relational analysis
Bibliographic record
Abstract
Plastic waste is becoming of increasing interest in gasification research because the gasification of plastic waste not only produces a valuable hydrogen-rich syngas but also can help reduce environmental problems caused by these materials. Most studies in the field of plastic waste gasification have only focused on evaluating effects of process parameters and optimizing the process by considering input variables. The present study explores the comparative performance analysis of a wide range of prevalent plastic waste types utilizing multi-criteria decision-making techniques. This study uses the “technique for order preference by similarity to ideal solution” (TOPSIS) and grey relational analysis (GRA) and presents a thorough sensitivity analysis. Low-density polyethylene results in maximum lower heating value of syngas and has a desirable performance from cold gas and exergy efficiencies viewpoints in air gasification. The findings of TOPSIS and GRA techniques show that low-density polyethylene as plastic waste exhibits the best performance in an air gasification process and the results of the sensitivity analysis confirm this. However, the decision making in steam gasification was challenging where TOPSIS and GRA techniques introduced high-density polyethylene and low-density polyethylene as the best candidates, respectively. Again, the findings of the sensitivity analysis confirmed the result. High-density polyethylene exhibits the best performance in steam gasification according to sensitivity analysis via the TOPSIS technique while low-density polyethylene ranked first according to sensitivity analysis of GRA. The findings can contribute to a better understanding of the selection of plastic waste feedstock for air and steam gasification by considering energy, exergy, and environmental factors.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".