Prediction of Compressor Fouling Rate Under Time Varying Operating Conditions
Bibliographic record
Abstract
Performance of the compressors deteriorates due to detrimental effects of fouling on the aerodynamic flow characteristic. The compressors need periodic clean up services to re-gain the designed performance. Apart from the operating time, the ambient and the operating conditions affect the fouling phenomenon making accurate scheduling for predictive maintenance very difficult. In this work, the symptoms of compressor fouling are captured through the evolution of the compressor map in terms of loss of isentropic efficiency and mass flow decrease. Compressor mass flow and the rate of humidity condensation at the inlet of the compressor are identified as the effective factors on the fouling rate. Humidity condensation has a competing effect on fouling rate; increment of the condensed humidity up to a certain level accelerates the fouling rate, while additional mist has an inverse effect. The complex effect of the condensed humidity along with the air mass flow is extracted through training an adaptive neuro-fuzzy inference system. The resulting model reveals how the efficiency and the mass flow of the compressor map vary as a result of fouling development, given the mass flow and the humidity condensation history. The methodology is verified using data from a similar compressor commissioned at a different period.
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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".