Exergetic Performance Analysis of a Gas Turbine Cycle Integrated With Solid Oxide Fuel Cells
Bibliographic record
Abstract
This paper deals with an exergetic performance analysis of a gas turbine cycle integrated with SOFCs with internal reforming. As the efficiency of a gas turbine cycle is mainly defined by the maximum temperature at the turbine inlet, this temperature is fixed at 1573 K for the analysis. In the cycle considered, the high-temperature gaseous flow from the turbine heats the input flows of natural gas and air, and is used to generate pressurized steam which is mixed with natural gas at the SOFC stack inlet to facilitate its conversion. The application of SOFCs provides the opportunity to reduce the exergy losses of the most irreversible process in the system: fuel combustion. Depending on the SOFC stack efficiency, the energy efficiency of the combined cycle reaches 70–80% which compares well to the efficiencies of 54–55% typical of conventional combined power generation cycles. Parametric studies are also undertaken to investigate how energy and exergy efficiencies of the integrated system change with variations in operating conditions. An increase in the efficiency of SOFCs is attained by increasing the fuel cell active area. Achieving the highest efficiency of the SOFC stack leads to a significant and non-proportional increase in the stack size and cost, and simultaneously to a decrease in steam generation, reducing the steam/methane ratio at the anode inlet and increasing the possibility of catalyst coking. Accounting for these factors, likely operating conditions of the SOFC stack in combination with a gas turbine cycle are presented.
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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) | 0.001 | 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) | 0.001 | 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".