Effects of Engine Wash Frequency on GT Degradation in Natural Gas Compressor Stations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gas Turbine (GT), like other prime movers, undergoes wear and tear over time which results in performance drop as far as available power and efficiency is concerned. In addition to routine wear and tear, the engine also undergoes corrosion, fouling etc. due to the impurities it breathes in. It is standard procedure to ‘wash’ the engine from time to time to revive it. However, it is important to establish a correct schedule for the wash to ensure optimal maintenance procedure. This calls for accurate prediction of the performance degradation of the engine over time. In this paper, a methodology is presented to predict the performance degradation in a GE LM2500+ Gas Turbine engine used at one of TransCanada’s pipeline system, Canada. Evaluation of 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 is presented and correlated to the frequency and span of offline engine washes. Other components, such as the high pressure turbine and the power turbine are also evaluated. Although the results presented are for a specific engine type, the general framework of the model could be used for other engines as well to quantify degradation over time of other components within the GT engine. This model combines Gas Path Analysis (GPA) to evaluate the thermodynamic parameters over the engine cycle followed by parameter estimation based on Error-in-Variable Model (EVM) to filter the data of possible noise due to instrumentation errors. The model helps quantify the degradation in the engine performance over time and also indicates the effectiveness of each engine wash. The analysis will lead to better scheduling of the engine wash thereby optimizing operational costs as well as engine overhaul time.
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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 | 0.000 |
| 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 it