Thermodynamic Analyses of Biomass Gasification Integrated Externally Fired, Post-Firing and Dual-Fuel Combined Cycles
Bibliographic record
Abstract
In the present work, the results are reported of the energy and exergy analyses of three biomass-related processes for electricity generation: the biomass gasification integrated externally fired combined cycle, the biomass gasification integrated dual-fuel combined cycle, and the biomass gasification integrated post-firing combined cycle. The energy efficiency for the biomass gasification integrated post-firing combined cycle is 3% to 6% points higher than for the other cycles. Although the efficiency of the externally fired biomass combined cycle is the lowest, it has an advantage in that it only uses biomass. The energy and exergy efficiencies are maximized for the three configurations at particular values of compressor pressure ratios, and increase with gas turbine inlet temperature. As pressure ratio increases, the mass of air per mass of steam decreases for the biomass gasification integrated post-firing combined cycle, but the pressure ratio has little influence on the ratio of mass of air per mass of steam for the other cycles. The gas turbine exergy efficiency is the highest for the three configurations. The combustion chamber for the dual-fuel cycle exhibits the highest exergy efficiency and that for the post-firing cycle the lowest. Another benefit of the biomass gasification integrated externally fired combined cycle is that it exhibits the highest air preheater and heat recovery steam generator exergy efficiencies.
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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) | 0.001 | 0.001 |
| 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".