Energy and exergy analyses of power generation via an integrated biomass post-firing combined-cycle
Bibliographic record
Abstract
Biomass energy recently has received much attention due to its renewability and relatively low environment impact, both of which suggest it has good prospects as are placement for fossil fuels in the future. Furthermore, biomass gasification reduces problems associated with direct burning of biomass, and the producer gas from the gasification process can be utilised in various power generation systems. In this article, a biomass post-firing combined cycle is proposed and energy and exergy analyses are reported for the cycle. The cycle energy and exergy efficiencies are both determined peak at specific compressor pressure ratio, and increasing the compressor pressure ratio reduces the mass of air per mass of steam in the cycle and, correspondingly, the gas turbine size. With increasing compressor pressure ratio and decreasing gas turbine inlet temperature, the quantity of natural gas required relative to biomass is observed to decrease, while the exergy loss and exergy destruction rates are seen to increase. Furthermore, as the gas inlet temperature to the heat recovery steam generator rises, the exergy destruction rate increases and the exergy loss rate decreases. The highest exergy efficiency is exhibited by the gas turbine and the lowest by the combustor and the condenser.
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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) | 0.002 | 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".