Characterization of pyrolytic products from the thermochemical conversion of textile waste
Bibliographic record
Abstract
Abstract The rapid expansion of the textile industry has led to a significant increase in textile waste generation, posing challenges for municipal waste management systems. Textile waste, composed of natural and synthetic polymers, presents an opportunity for valorization through thermochemical conversion. This study investigates the pyrolysis of cotton, untreated denim, and treated denim waste in a fixed‐bed reactor at 500°C to produce value‐added chemicals such as furan, sugars, and ketones. Prior to pyrolysis, the textile wastes were chopped, cleaned, sun‐dried, and, in the case of treated denim, denim waste is chemically treated with nitric acid to remove dyes. Characterization of the feedstock revealed high volatile matter content (85% in cotton, 83% in treated denim, and 81% in denim) and low moisture content (~6%), with denim exhibiting higher ash content due to dye residues. Post‐pyrolysis analysis indicated that cotton waste predominantly yielded furan (40%), sugars (35%), and ketones (15%), whereas treated denim produced sugars (40%), furan (30%), and ketones (25%). Untreated denim primarily yielded ketones (70%) and sugars (25%). The resulting chars were characterized using BET surface area analysis, SEM, FTIR, and XRD. Treated denim char exhibited the highest surface area (368.4 m 2 /g), followed by cotton char (231.4 m 2 /g), while untreated denim char had the lowest (17.0 m 2 /g). SEM analysis showed significant fibre fragmentation in cotton and treated denim chars, whereas untreated denim fibres remained relatively intact. FTIR analysis suggested the presence of residual functional groups, indicating incomplete conversion, while XRD confirmed dye retention in denim char. The findings highlight the potential of textile waste pyrolysis for sustainable waste management and chemical 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 | 0.001 | 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".