Predictive Emission Monitoring Model for LM1600 Gas Turbines Based on Neural Network Architecture Trained on Field Measurements and CFD Data
Bibliographic record
Abstract
A Predictive Emission Monitoring (PEM) model has been developed based on an optimized Neural Network (NN) architecture which takes 8 fundamental parameters as input variables. The model predicts both NO and NOx as output variables. The NN is initially trained using a combination of two sets of data: a) measured data at various loads from an LM1600 gas turbine installed at one of the compressor stations on TransCanada Transmission system in Alberta, Canada, b) data generated by a Computational Fluid Dynamics (CFD) at different operating conditions covering the range of the engine operating parameters spanned over one year. The predictions of NOx by CFD employed the ‘flamelet’ model and a set of 8 reactions including the Zeldovich mechanism for thermal NOx along with an empirical correlation for prompt NOx formation. It was found that a Multi Layer Perceptron type Neural Network with two hidden layers was the optimum architecture for predicting NO levels with a maximum absolute error of around 7%, mean absolute error of 2.3% and standard deviation of 1.97%. The model is easy to implement on the station PLC. A set of one year data consisting of 2804 cases was submitted to the above optimized NN architecture with varying ambient temperature from –29.9 °C to 35.7 °C and output power from 570 kW to 16.955 MW. This gave consistent contours of NO levels. As expected, NN architecture shows that NO increases with increasing power or ambient temperature.
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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 | 0.000 | 0.001 |
| 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".