Evaluation and Prediction of Energy Content of Municipal Solid Waste: A review
Bibliographic record
Abstract
Abstract Researches in the literature have unveiled the potential of resource and energy recovery from waste, it can therefore no longer be regarded as trash. This study reviews the literature to evaluate and analyses studies which estimated the experimental heating value of waste and the theoretical energy potential recoverable from waste through thermochemical and biochemical routes at different case studies. It was observed in this study that most developing countries are not exploiting the full potential of energy recoverable from waste. Models developed to predict the energy content of municipal solid waste (MSW) based on the elemental analysis, proximate analysis and physical composition were evaluated. A comparative analysis of the energy prediction models was also done. Artificial neural network (ANN) and multiple linear regressions found more applications in energy prediction. Energy prediction based on ultimate analysis using the elemental composition of the waste was predominant and are the most accurate; while proximate analysis based predictions were the least. The prediction accuracy of ANN is greater than the linear regression in the forecast of the energy content of MSW. However, a major limitation in the use of these modelling techniques was identified. Most of the generalized models may not capture the peculiarity of the waste generated at a particular place or municipality and therefore may not be very accurate for specific applications at some municipalities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".