Pyrolysis of high ash gasifiable waste materials—Evaluation of kinetic parameters using thermogravimetric analysis for energy extraction
Bibliographic record
Abstract
Abstract Waste‐to‐energy is the sustainable approach for dealing with municipal solid waste resulting from fast urbanization. Among the different waste‐to‐energy options, pyrolysis and gasification are technically feasible methods which can meet the emission limits and effectively reduce the landfill disposal burden. The current work reports, for the first time, the co‐pyrolysis behaviour of (i) garden waste and low‐density polyethylene (LDPE) and (ii) paper rich refuse‐derived fuel (RDF), which are high ash gasifiable materials, using thermogravimetric analysis. Mixture of garden waste and LDPE shows the thermal behaviour between that of its individual constituents with positive synergy. The kinetic parameters of pyrolysis reaction of garden waste, LDPE, and its mixture are evaluated using model‐free and model‐fitting methods, which shows that the addition of LDPE decreases the activation energy requirement of garden waste. The activation energy for the mixture of 25 wt.% LDPE and 75 wt.% garden waste is approximately 213 kJ/mol. The thermogravimetric analysis of paper rich RDF indicates that the pyrolysis involves multiple stages consisting of decomposition of cellulose, hemi‐cellulose, lignin, and plastics. The activation energy for the RDF under the specified condition is approximately 205 kJ/mol. The findings indicate that the waste materials considered in this study possess significant potential as a feedstock for energy recovery via thermochemical processes such as pyrolysis and gasification.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".