Recoverable Versus Unrecoverable Degradations of Gas Turbines Employed in Five Natural Gas Compressor Stations
Bibliographic record
Abstract
Gas turbines (GT), like other prime movers, experience wear and tear over time, resulting in decreases in available power and efficiency. Further decreases in power and efficiency can result from erosion and fouling caused by the airborne impurities the engine breathes in. To counteract these decreases in power and efficiency, it is a standard procedure to “wash” the engine from time to time. In compressor stations on gas transmission systems, engine washes are performed off-line and are scheduled in such intervals to optimize the maintenance procedure. This optimization requires accurate prediction of the performance degradation of the engine over time. A previous paper demonstrated a methodology for evaluating various components of the GT gas path, in particular, the air compressor side of the engine since it is most prone to fouling and degradation. This methodology combines gas path analysis (GPA) to evaluate the thermodynamic parameters over the engine cycle followed by parameter estimation based on the Bayesian error-in-variable model (EVM) to filter the data of possible noise due to measurement errors. The methodology quantifies the engine-performance degradation over time, and indicates the effectiveness of each engine wash. In the present paper, the methodology was extended to assess both recoverable and unrecoverable degradations of five GT engines employed on TransCanada's pipeline system in Canada. These engines are: three GE LM2500+, one RR RB211-24G, and one GE LM1600 GTs. Hourly data were collected over the past 4 years, and engine health parameters were extracted to delineate the respective engine degradations. The impacts of engine loading, site air quality conditions, and site elevation on engine-air-compressor isentropic efficiency are compared between the five engines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".