Abstract
References
- World Health Organization., “Air pollution.” https://www.who.int/health-topics/air-pollution, 2021.
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- De Lima Nogueira, S. C., Och, S. H., Moura, L. M., Domingues, E., dos Santos Coelho, L., & Mariani, V. C., “Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering”, Energy, 280, 128066, 2023.
- Shen, Q., Wang, G., Wang, Y., Zeng, B., Yu, X., & He, S., “Prediction Model for Transient NOx Emission of Diesel Engine Based on CNN-LSTM Network”, Energies, 16(14), 5347, 2023.
- Heywood, J. B., “ Internal combustion engine fundamentals”, McGraw-Hill Education., 2018
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Cournapeau, D., Van der Maaten, L., Weiss, R., & Duchesnay, É.., “Scikit-learn: Machine learning in Python. Journal of Machine Learning Research”, 12, 2825-2830, 2011.
- Montgomery, D. C., Peck, E. A., & Vining, G. G., “Introduction to linear regression analysis”, Published by John Wiley & Sons, Inc., Hoboken, New Jersey, 2012.
- Liu, Y., Wang, Y., & Zhang, J., A novel hybrid model based on data preprocessing and optimized decision tree for diesel engine NOx emission prediction under transient conditions. Energy, 239, 122207, 2022.
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- Smola, A. J., & Schölkopf, B., “A tutorial on support vector regression”, Statistics and computing, 14(3), 199-222, 2004.
- Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T., & Zeileis, A., “ Conditional variable importance for random forests”, BMC bioinformatics, 9(307), 2008.
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Abstract
This study presents a comparative analysis of machine learning models for predicting carbon monoxide (CO) emissions in automotive engines. Four models—Linear Regression, Decision Tree, Random Forest, and Support Vector Regression—were evaluated using a dataset of engine performance parameters and emission measurements. Among these, the Random Forest model demonstrated the highest predictive accuracy, achieving an R² score of 0.8965. Feature importance analysis identified nitrogen oxides (NOX), engine speed (RPM), and hydrocarbons (HC) as the most significant predictors of carbon monoxide emissions. Learning curve analysis provided insights into model generalization and highlighted potential limitations. The study underscores the value of data-driven approaches in optimizing engine design and controlling emissions. The findings contribute to the development of cleaner, more efficient vehicles, supporting sustainability efforts in the automotive industry. This research bridges data science and automotive engineering, offering a framework for advanced emission prediction and control that can be applied to other pollutants and engine types.
Keywords
Carbon monoxide emissions Machine learning Random Forest Engine performance optimization Emission control Sustainability Automotive engineering
Ethical Statement
It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography.
References
- World Health Organization., “Air pollution.” https://www.who.int/health-topics/air-pollution, 2021.
- Raub, J. A., Mathieu-Nolf, M., Hampson, N. B., & Thom, S. R., “Carbon monoxide poisoning—a public health perspective”. Toxicology, 145(1), 1-14, 2000.
- Environmental Protection Agency., “Ground-level Ozone Basics.” https://www.epa.gov/ground-level-ozone-pollution/ground-level-ozone-basics, 2021.
- Requia, W. J., Mohamed, M., Higgins, C. D., Arain, A., & Ferguson, M., “ How clean are electric vehicles? Evidence-based review of the effects of electric mobility on air pollutants, greenhouse gas emissions and human health. Atmospheric Environment”, 185, 64-77, 2018.
- Johnson, T., & Joshi, A., “ Review of vehicle engine efficiency and emissions”. SAE International Journal of Engines, 11(6), 2018.
- Gao, J., Chen, H., Tian, G., Ma, C., & Zhu, F., “An analysis of energy flow in a turbocharged diesel engine of a heavy truck and potential for recovery of exhaust heat”, Energy Conversion and Management, 185, 1040-1051, 2019.
- Reitz, R. D., et al., “IJER editorial: The future of the internal combustion engine”, International Journal of Engine Research, 21(1), 3-10, 2020.
- Janakiraman, V. M., Nguyen, X., & Assanis, D., “ Stochastic gradient based extreme learning machines for stable online learning of advanced combustion engines. Neurocomputing”, 177, 304-316, 2016.
- Wu, J. D., & Liu, J. C., “ Development of a predictive system for car fuel consumption using an artificial neural network. Expert Systems with Applications”, 38(5), 4967-4971, 2014.
- Roy, S., Banerjee, R., & Bose, P. K., “Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network”, Applied Energy, 119, 330-340, 2014.
- Wang, T., Jerrett, M., Sinsheimer, P., & Zhu.,” Estimating PM2.5 in Southern California using remote sensing data and light use efficiency modeling”, Implications for policy. Environmental Science & Technology, 50(9), 4724-4733, 2016.
- De Lima Nogueira, S. C., Och, S. H., Moura, L. M., Domingues, E., dos Santos Coelho, L., & Mariani, V. C., “Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering”, Energy, 280, 128066, 2023.
- Shen, Q., Wang, G., Wang, Y., Zeng, B., Yu, X., & He, S., “Prediction Model for Transient NOx Emission of Diesel Engine Based on CNN-LSTM Network”, Energies, 16(14), 5347, 2023.
- Heywood, J. B., “ Internal combustion engine fundamentals”, McGraw-Hill Education., 2018
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Cournapeau, D., Van der Maaten, L., Weiss, R., & Duchesnay, É.., “Scikit-learn: Machine learning in Python. Journal of Machine Learning Research”, 12, 2825-2830, 2011.
- Montgomery, D. C., Peck, E. A., & Vining, G. G., “Introduction to linear regression analysis”, Published by John Wiley & Sons, Inc., Hoboken, New Jersey, 2012.
- Liu, Y., Wang, Y., & Zhang, J., A novel hybrid model based on data preprocessing and optimized decision tree for diesel engine NOx emission prediction under transient conditions. Energy, 239, 122207, 2022.
- Breiman, L., “ Random forests”. Machine learning, 45(1), 5-32, 2001.
- Smola, A. J., & Schölkopf, B., “A tutorial on support vector regression”, Statistics and computing, 14(3), 199-222, 2004.
- Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T., & Zeileis, A., “ Conditional variable importance for random forests”, BMC bioinformatics, 9(307), 2008.
- Figueroa, R. L., Zeng-Treitler, Q., Kandula, S., & Ngo, L. H., “Predicting sample size required for classification performance”, BMC medical informatics and decision making, 12(8), 2012.
Details
| Primary Language | English |
|---|---|
| Subjects | Environmentally Sustainable Engineering, Environmental Engineering (Other) |
| Journal Section | Research Article |
| Authors | |
| Early Pub Date | March 27, 2025 |
| Publication Date | March 28, 2025 |
| Submission Date | October 10, 2024 |
| Acceptance Date | January 15, 2025 |
| Published in Issue | Year 2025Volume: 8 Issue: 1 |
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