Predictive Compressor Wash Optimization for Economic Operation of Gas Turbine
Bibliographic record
Abstract
Gas turbine engines (GTEs) are widely used for power generation, ranging from stationary power plants to airplane propulsion systems. Compressor fouling is the dominant degradation mode in gas turbines that leads to economic losses due to power deficit and extra fuel consumption. Washing of the compressor removes the fouling matter and retrieves the performance, while causing a variety of costs including loss of production during service time. In this paper, the effect of fouling and washing on the revenue of the power plant is studied, and a general solution for the optimum time between washes of the compressor under variable fouling rates and demand power is presented and analyzed. The framework calculates the savings achievable with optimization of time between washes during a service period. The methodology is utilized to optimize total costs of fouling and washing and analyze the effects and sensitivities to different technical and economic factors. As a case study, it is applied to a sample set of cumulative gas turbine operating data for a time-between-overhauls and the potential saving has been estimated. The results show considerable saving potential through optimization of time between washes.
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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 | 0.000 |
| 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 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".