Ensemble Machine Learning Techniques for Particulate Emissions Estimation from a Highly Boosted GDI Engine Fuelled by Different Gasoline Blends
Bibliographic record
Abstract
Light-duty vehicle emissions regulations worldwide impose stringent limits on particulate matter (PM) emissions, necessitating accurate modelling and prediction of particulate emissions across a range of sizes (as low as 10 nm). It has been shown that the decision tree-based ensemble machine learning technique known as Random Forest can accurately predict particle size, concentration, and accumulation mode geometric standard deviation (GSD) for particulate emission diameters as low as 23 nm from a highly boosted gasoline direct injection (GDI) engine operating on a single fuel, while also offering insights into the underlying factors of emissions production because of the interpretable nature of decision trees. This work builds on the prior Random Forest research as its basis and further investigates the relative performance of five decision tree-based machine learning techniques in predicting these particulate emission parameters and extends the work to 10 nm particles. In addition to Random Forest, the selected techniques consist of four gradient boosting models: GBM, XGBoost, LightGBM, and CatBoost. Moreover, the influences of fuel chemistry are assessed by using data from 13 gasoline fuel blends, including blends with ethanol and methanol – common bio- and e-fuels. The results show that the CatBoost model achieves the highest prediction accuracy (R2 between 0.77 and 0.932), even when the feature set is reduced to improve computational efficiency. Random Forest and LightGBM are also shown to be suitable for PM emissions estimation. Permutation feature importance was used to highlight the dependence of PM emissions on both fuel and engine operating parameters – offering new insights into the effect of fuel properties on particulate emissions and their formation in highly boosted engines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".