Test results of the OGT2500 Gas Turbine Engine Running on Alternative Fuels: BioOil, Ethanol, BioDiesel and Crude Oil
Bibliographic record
Abstract
This paper describes the results of an on going development program aimed at determining the technical feasibility of utilizing alternative fuels such as bio-mass derived BioOil, Ethanol, Bio-Diesel and bituminous Crude Oil in a 2.5 MW GT2500 industrial Gas Turbine Engine. This gas turbine engine was designed and manufactured by “Zorya-Mashproekt” in the Ukraine and further modified for the alternative fuels application through a join development program between “Zorya-Mashproekt” and Orenda Aerospace Corporation in Canada. The modification of the GT2500 Gas Turbine Engine hot section and combustion system to operate on liquid alternative fuels are described. Also described is an engine hot section online cleaning system and features of the fuel-handling module, which carries out fuel preheating and preprocessing. A test rig equipped with a load bank was designed and built to test the modified GT2500 Gas turbine Engine on different alternative fuels (full speed/full power). Results of the modified GT2500 gas turbine engine operation along with the emissions data are presented. The tests proved the technical feasibility of operating this gas turbine engine on the alternative fuels mentioned above. Based on these results a power generation package with the engine and fuel handling module have been accepted for commercial operation in a pilot plant under construction in West Lorne, Ontario, Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".