Use of Cotton Apparel Waste as an Energy Source for Biomass Boilers: A Feasibility Study
Bibliographic record
Abstract
The steady growth of the Sri Lankan apparel manufacturing sector over the last three decades has resulted in generating large amount of solid apparel waste. Currently, it is a massive environmental and financial burden of the sector. As a solution, few apparel manufacturers have initiated using apparel waste, specifically the cotton apparel waste for biomass boilers. The apparel sector as one of the major thermal energy consumers, they consider it as a possible solution not only for the apparel waste disposal issue, but also for the challenge of getting continuous supply of firewood for the boilers. However, the promotion of such a solution throughout the apparel industry is impractical without a feasibility study in terms of social, environmental, financial, legal and technical aspects over its mechanism. Therefore, this research focused to identify the feasibility of using cotton apparel waste for biomass boilers as an energy source. Towards this aim, a qualitative research approach was followed involving the case study strategy. Basically, two cases were selected and analysed the feasibility of using cotton waste for boilers under pre-determined feasibility criteria in detail. Data collection for the case study was done through a document survey and expert interviews. Findings revealed the entire feasibility of environmental, financial, legal and technical aspects and in overall it can be concluded that the use of cotton apparel waste for biomass boilers as an energy source is feasible. Accordingly, this study provides insights into making decisions on managing both waste disposal and heat energy requirement issues of the apparel factories.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".