Energy from waste/gas turbine hybrid combined cycles and their potential for GHG emissions reduction
Bibliographic record
Abstract
Municipal solid waste (MSW) is produced in a substantial amount with minimal fluctuations throughout the year. The analysis of carbon neutrality of MSW on life cycle basis shows that about 33% of the total CO2emissions from incineration of the waste are of fossil origin, which implies that MSW is 67% carbon-neutral. The waste constitutes a "renewable biofuel" energy resource and energy from waste (EfW) can result in net CO2emissions reduction. In this paper, we explore an exciting approach to extracting energy from MSW efficiently - EfW/gas turbine hybrid combined cycles. This approach innovates by delivering better performance with respect to energy efficiency and CO2mitigation. In the combined cycles, the topping cycle consists of a gas turbine, while the bottoming cycle is a steam cycle or air cycle where the low quality fuel-waste is utilized. This paper assesses the viability of EfW/gas turbine hybrid combined cycles and analyses their thermodynamic advantages with the help of computer simulations. It was shown that the combined cycles could offer significantly higher efficiency of energy conversion and practical solutions to handling the waste. Also, the potential for net CO2emissions reduction resulting from the hybrid combined cycles was evaluated.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".