Unlocking the Green Economy for Aeroderivative Gas Turbines
Bibliographic record
Abstract
Abstract If existing gas turbine engines are to remain as the primary choice source of power for supplying short term peaking power capacity in an emergency, then they will need to be capable of directly using a alternative carbon neutral fuel supply. It is important that the fuel can be stored locally to ensure operation of the gas turbine can be provided without reliance upon supplies through distribution network infrastructure or stored hydrogen. Alternative carbon neutral fuels such as synthetic electro or biomass manufactured from hydrogen with nitrogen or CO2 to produce respectively; nitrofuel (Ammonia) or carbofuel (Methanol). Both fuels are renewable and compatible with existing carbon supply chain infrastructure as they can be similarly transported and stored as liquids with similar properties. Digital technologies can help accelerate the uptake of carbon neutral solutions by operators by assisting them to make greener choices, from the data and information presented to them, promoting the use of their assets demonstrating their contribution and responsibilities to managing the environment. Whilst progress in adopting digital technology has been slow, it is by linking the investment to decarbonization that could then be considered as a value adder rather than a regulatory requirement. The paper discusses the program of work to develop a bundle of digital services whilst decarbonizing aeroderivative gas turbine applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".