Verification of a Neural Network Based Predictive Emission Monitoring Module for an RB211-24C Gas Turbine
Bibliographic record
Abstract
This paper presents a verification of a Predictive Emission Monitoring (PEM) model developed for a non-DLE RB211-24C gas turbine used at a natural gas compressor station on the TransCanada Pipeline System in Alberta, Canada. The basis and methodology of the PEM model is first described, and its predictions were compared to recent Continuous Emission Monitoring (CEM) data obtained at different engine load conditions varying from 10 to 19 MW (site condition). The PEM model is based on an optimized Neural Network (NN) architecture which takes 6 fundamental engine parameters as input variables. The model predicts NOx (dry) as an output variable. The NN was trained using CEM measurements comprising four sets of actual emission data collected over four different dates in four different seasons during 2000, and at different operating conditions covering the range of the engine operating parameters. The PEM model was then implemented in the station Compressor Equipment Health Monitoring (CEHM) system and NOx predictions were reported online on a minutely basis for several months and NOx emission trends were captured and analyzed. Comparison between predictions and stack measurements shows a fairly good agreement between the PEM and CEM data within ±10 ppm (dry).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".