IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i11p2493-d1399591.html
   My bibliography  Save this article

Modeling and Prediction of Carbon Monoxide during the Start-Up in ICE through VARX Regression

Author

Listed:
  • Alejandro Garcia-Basurto

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

  • Angel Perez-Cruz

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

  • Aurelio Dominguez-Gonzalez

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

  • Juan J. Saucedo-Dorantes

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

Abstract

In a global society that is increasingly interrelated and focused on mobility, carbon monoxide emissions derived from internal combustion vehicles remain the most important factor that must be addressed to improve environmental quality. Certainly, air pollution generated by internal combustion engines threatens human health and the well-being of the planet. In this regard, this paper aims to address the urgent need to understand and face the CO emissions produced by internal combustion vehicles; therefore, this work proposes a mathematical model based on Auto-Regressive Exogenous that predicts the CO percentages produced by an internal combustion engine during its start-up. The main goal is to establish a strategy for diagnosing excessive CO emissions caused by changes in the engine temperature. The proposed CO emissions modeling is evaluated under a real dataset obtained from experiments, and the obtained results make the proposed method suitable for being implemented as a novel diagnosis tool in automotive maintenance programs.

Suggested Citation

  • Alejandro Garcia-Basurto & Angel Perez-Cruz & Aurelio Dominguez-Gonzalez & Juan J. Saucedo-Dorantes, 2024. "Modeling and Prediction of Carbon Monoxide during the Start-Up in ICE through VARX Regression," Energies, MDPI, vol. 17(11), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2493-:d:1399591
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/11/2493/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/11/2493/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    2. Zhu, Nanyang & Wang, Ying & Yuan, Kun & Yan, Jiahao & Li, Yaping & Zhang, Kaifeng, 2024. "GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations," Applied Energy, Elsevier, vol. 364(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yanan Xue & Jinliang Yin & Xinhao Hou, 2024. "Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning," Energies, MDPI, vol. 17(13), pages 1-25, July.
    2. Nitin Kumar Singh & Masaaki Nagahara, 2024. "LightGBM-, SHAP-, and Correlation-Matrix-Heatmap-Based Approaches for Analyzing Household Energy Data: Towards Electricity Self-Sufficient Houses," Energies, MDPI, vol. 17(17), pages 1-32, September.
    3. Wang, Zhi & Peng, Xianyong & Zhou, Huaichun & Cao, Shengxian & Huang, Wenbo & Yan, Weijie & Li, Kuangyu & Fan, Siyuan, 2024. "A dynamic modeling method using channel-selection convolutional neural network: A case study of NOx emission," Energy, Elsevier, vol. 290(C).
    4. Li, Shicheng & Ma, Suxia & Wang, Fang, 2023. "A combined NOx emission prediction model based on semi-empirical model and black box models," Energy, Elsevier, vol. 264(C).
    5. Wu, Zheng & Zhang, Yue & Dong, Ze, 2024. "NOx concentration prediction based on multi-channel fused spectral temporal graph neural network in coal-fired power plants," Energy, Elsevier, vol. 305(C).
    6. Ruomiao Yang & Tianfang Xie & Zhentao Liu, 2022. "The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines," Energies, MDPI, vol. 15(9), pages 1-16, April.
    7. Cesar de Lima Nogueira, Silvio & Och, Stephan Hennings & Moura, Luis Mauro & Domingues, Eric & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2023. "Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering," Energy, Elsevier, vol. 280(C).
    8. Han, Zhezhe & Tang, Xiaoyu & Xie, Yue & Liang, Ruiyu & Bao, Yongqiang, 2024. "Prediction of heavy-oil combustion emissions with a semi-supervised learning model considering variable operation conditions," Energy, Elsevier, vol. 288(C).
    9. Liu, Chunming & Wang, Chunling & Yin, Yujun & Yang, Peihong & Jiang, Hui, 2022. "Bi-level dispatch and control strategy based on model predictive control for community integrated energy system considering dynamic response performance," Applied Energy, Elsevier, vol. 310(C).
    10. Yeeun Moon & Younjeong Lee & Yejin Hwang & Jongpil Jeong, 2024. "Long Short-Term Memory Autoencoder and Extreme Gradient Boosting-Based Factory Energy Management Framework for Power Consumption Forecasting," Energies, MDPI, vol. 17(15), pages 1-21, July.
    11. Lv, Zhihan & Wang, Nana & Lou, Ranran & Tian, Yajun & Guizani, Mohsen, 2023. "Towards carbon Neutrality: Prediction of wave energy based on improved GRU in Maritime transportation," Applied Energy, Elsevier, vol. 331(C).
    12. Wu, Zheng & Zhang, Yue & Dong, Ze, 2023. "Prediction of NOx emission concentration from coal-fired power plant based on joint knowledge and data driven," Energy, Elsevier, vol. 271(C).
    13. Afshin Tatar & Amin Shokrollahi & Abbas Zeinijahromi & Manouchehr Haghighi, 2024. "Deep Learning for Predicting Hydrogen Solubility in n-Alkanes: Enhancing Sustainable Energy Systems," Sustainability, MDPI, vol. 16(17), pages 1-24, August.
    14. Haoda Ye & Qiuyu Zhu & Xuefan Zhang, 2024. "Short-Term Load Forecasting for Residential Buildings Based on Multivariate Variational Mode Decomposition and Temporal Fusion Transformer," Energies, MDPI, vol. 17(13), pages 1-22, June.
    15. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    16. Wang, Zhimin & Huang, Qian & Liu, Guanqing & Wang, Kexuan & Lyu, Junfu & Li, Shuiqing, 2024. "Knowledge-inspired data-driven prediction of overheating risks in flexible thermal-power plants," Applied Energy, Elsevier, vol. 364(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2493-:d:1399591. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.