Performance Evaluation of H-25 Gas Turbine 96 Performance Evaluation of H-25 Gas Turbine — with Low-NOx Combustor Installed at SaskPower’s Queen Elizabeth Power
Bibliographic record
Abstract
OVERVIEW: As concerns about global environmental problems grow, electric power plants that utilize high efficiency gas-turbine generators are being actively sought after. Accordingly, at SaskPower’s Queen Elizabeth Power Station in Canada, Hitachi, Ltd. has implemented a re-powering system that combines Hitachi’s H-25 gas-turbine generators with the plant’s existing steam-turbine generators. This re-powering system consists of H-25 generators combined with OTSG (once-through steam generator: also known as once-through boiler). The H-25 gas-turbine generator incorporates a low-NOx emission combustor that meets the demand for low environmental impact. The combustor is based on low-NOx emission technology that Hitachi has accumulated during the development of low-NOx emission combustors for large-scale gas turbines. And it was shown during the commissioning that the low-NOx emission H-25 gas-turbine generators installed at the Queen Elizabeth Power Station produce NOx emission at the level of 25 ppm (at 15 % O2). In addition, the performance of the turbines was demonstrated to be satisfactory; that is, trouble-free operation down to-30°C is possible, and generated gross output power and thermal efficiency easily meet the design specifications. It is thus considered that the H-25 gas-turbine generator incorporating a high-thermal-efficiency low-NOx emission combustor will be an effective response to the growing demands for environmental-protection measures—such as prevention of global warming and reduction of NOx emissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".