Developing machine learning models to predict methane and nitrogen oxideengine-out emissions from a heavy-duty natural-gas engine
Bibliographic record
Abstract
Abstract: Heavy-duty engine manufacturers must comply with challenging and more stringent emission and greenhouse gas (GHG) regulations. Predicting engine emission behavior in system-level models with reasonable accuracy is advantageous for engine and powertrain development. Machine learning (ML) models are promising alongside 3D physics-based and one-dimensional models. In this study, five different ML models are trained using experimental engine data for emission prediction of methane (CH4) and nitrogen oxides (NOx). The models are compared with an existing phenomenological engine model (GT-Power). The ML models include linear regression, Ridge regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The results show that the RF model outperforms other models and a one-dimensional model regarding NOx emission prediction. The results of RF NOx and CH4 emission prediction in the test set fit with 80% accuracy (±20 error margin). Also, 95% of test data points have less than 10% error compared to real experimental data.
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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 | 0.001 |
| 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".