Green Methanol Demonstrated as an Alternative Fuel to Decarbonise Gas Turbines
Bibliographic record
Abstract
Abstract Power generation from gas turbines is responsible for up to 79% of the CO2 emissions generated by the UK Oil and Gas (O&G) industry. Thus, decarbonizing gas turbines is key to reducing emissions in the industry. Siemens Energy, in partnership with The Net Zero Technology Centre (NZTC) and Rolls Wood Group (RWG), successfully demonstrated an SGT-A20 gas turbine running on 100% bio-methanol, reducing the overall CO2 footprint by 60%. The demonstration test was a key milestone in Phase 1 of the Alternative Fuel Gas Turbine project funded by the Scottish government and industry partners. The SGT-A20 bio-methanol test demonstrated the engine operating at various conditions including start-up, shut-down, idle to full power, and transient maneuvers. The engine performed as expected within the standard operating limits. The test also confirmed a reduction in NOx emissions of approximately 80%, a 15% reduction in CO, and elimination of visible smoke in the gas turbine exhaust compared to kerosene fuel.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".