Comparative Exergoeconomic Analyses of Gas Turbine Steam Injection Cycles with and without Fogging Inlet Cooling
Bibliographic record
Abstract
The results are reported of exergoeconomic analyses of a simple gas turbine cycle without a fogging system (SGT), a simple steam injection gas turbine cycle (STIG), and a steam injection gas turbine cycle with inlet fogging cooler (FSTIG). The results show that (1) a gas-turbine cycle with steam injection and simultaneous cooling has a higher power output than the other considered cycle; (2) at maximum energy efficiency conditions the gas turbine has the highest exergy efficiency of the cycle components and the lowest value of exergy efficiency is calculated for the fog cooler, where the mixing of air and water at greatly different temperatures causes the high exergy destruction; and (3) utilization of the fogging cooler in the steam injection cycle increases the exergy destruction in the combustion chamber. Furthermore, the simple gas turbine cycle is found to be more economic as its relative cost difference, total unit product cost, and exergoeconomic factors are less than those for the two other configurations. However, its efficiency and net power output are notably lower than for the gas turbine with steam injection and/or fog cooling. The total unit product cost is highest for the simple gas turbine with steam injection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".