Thermodynamic Assessment of Hybrid PSOFC/GT Systems for Mechanical/Electric Power Generation
Bibliographic record
Abstract
Hybrid PSOFC/GT cycles consisting of pressurized solid oxide fuel cells integrated into gas turbine cycles are emerging as a major new power generation concept. These hybrid cycles can potentially offer thermal efficiencies exceeding 70% along with significant reductions in greenhouse gas and NOX emissions. This paper considers the PSOFC/GT cycle in terms of electrical and mechanical power generation with particular focus on gas pipeline companies interested in diversifying their assets into distributed electric generation or lowering pollutant emissions while more efficiently transporting natural gas. By replacing the conventional GT combustion chamber with an internally reformed PSOFC, electrical power is generated as a by-product while hot gases exiting the fuel cell are diverted into the gas turbine for mechanical power. A simple one-dimensional thermodynamic model of a generic PSOFC/GT cycle has shown that overall thermal efficiencies of 65% are attainable, whilst almost tripling the specific work (i.e. energy per unit mass of air). The main finding of this paper is that the amount of electric power generated ranges from 60–80% of the total power available depending on factors such as the system pressure ratio and degree of supplementary firing before the gas turbine. Ultimately, the best cycle should be based on the “balance of plant”, which considers factors such as life cycle cost analysis, business and market focus, and environmental emission issues.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".