IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v280y2023ics0360544223014603.html
   My bibliography  Save this article

Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering

Author

Listed:
  • Cesar de Lima Nogueira, Silvio
  • Och, Stephan Hennings
  • Moura, Luis Mauro
  • Domingues, Eric
  • Coelho, Leandro dos Santos
  • Mariani, Viviana Cocco

Abstract

This study was conducted to investigate the performance of a novel Random Forest (RF) model for predicting variables from an original experimental dataset of a diesel engine adapted to work with both compressed natural gas and diesel fuels. The aim was to develop a reliable framework for diesel engine emissions prediction that could assist designers, engineers, and decision-makers in optimizing engine performance and reducing emissions. The engine was modified to run on compressed natural gas as well as diesel fuel, and five variables were studied. Trials were done on a six-cylinder diesel engine to assess the RF model, employing various factors for improving engine performance and emissions, such as fuel injection angles, air-fuel ratio mixtures, diesel-to-gas exchange rates, and fuel rail pressure. A tree structured Parzen estimator and six feature engineering approaches were used to tune the RF model's parameters. In addition, the Shapley Additive explanation (SHAP) approach adapting a concept coming from game theory is employed to interpret the RF model outputs. The results analysis showed that the RF model correctly predicted the output signals of the diesel engine, with determination coefficient R2 of 0.9811, 0.9276, 0.9516, 0.8842, and 0.8944, respectively, for the studied five output variables. The RF regression model's predictive power can be used to generate an efficient modeling framework, and successfully predicts the output signals of the diesel engine, confirming the viability, effectiveness, and competitive performance.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223014603
    DOI: 10.1016/j.energy.2023.128066
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223014603
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128066?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Yaopeng & Jia, Ming & Han, Xu & Bai, Xue-Song, 2021. "Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA)," Energy, Elsevier, vol. 225(C).
    2. Zandie, Mohammad & Ng, Hoon Kiat & Gan, Suyin & Muhamad Said, Mohd Farid & Cheng, Xinwei, 2023. "Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends," Energy, Elsevier, vol. 262(PA).
    3. Yousefi, Amin & Guo, Hongsheng & Birouk, Madjid & Liko, Brian, 2019. "On greenhouse gas emissions and thermal efficiency of natural gas/diesel dual-fuel engine at low load conditions: Coupled effect of injector rail pressure and split injection," Applied Energy, Elsevier, vol. 242(C), pages 216-231.
    4. Fayad, Mohammed A. & Tsolakis, Athanasios & Martos, Francisco J., 2020. "Influence of alternative fuels on combustion and characteristics of particulate matter morphology in a compression ignition diesel engine," Renewable Energy, Elsevier, vol. 149(C), pages 962-969.
    5. 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).
    6. Luo, Jianbin & Liu, Zhonghang & Wang, Jie & Xu, Hongxiang & Tie, Yuanhao & Yang, Dayong & Zhang, Zhiqing & Zhang, Chengtao & Wang, Haijiao, 2022. "Investigation of hydrogen addition on the combustion, performance, and emission characteristics of a heavy-duty engine fueled with diesel/natural gas," Energy, Elsevier, vol. 260(C).
    7. Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
    8. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    9. Wang, Huaiyu & Ji, Changwei & Shi, Cheng & Ge, Yunshan & Meng, Hao & Yang, Jinxin & Chang, Ke & Wang, Shuofeng, 2022. "Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine," Energy, Elsevier, vol. 248(C).
    10. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
    11. Baak, M. & Koopman, R. & Snoek, H. & Klous, S., 2020. "A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    12. Wen, Yifan & Wu, Ruoxi & Zhou, Zihang & Zhang, Shaojun & Yang, Shengge & Wallington, Timothy J. & Shen, Wei & Tan, Qinwen & Deng, Ye & Wu, Ye, 2022. "A data-driven method of traffic emissions mapping with land use random forest models," Applied Energy, Elsevier, vol. 305(C).
    13. Sheng, Yu & Shi, Xunpeng, 2013. "Energy market integration and equitable growth across countries," Applied Energy, Elsevier, vol. 104(C), pages 319-325.
    14. Liu, Jinlong & Huang, Qiao & Ulishney, Christopher & Dumitrescu, Cosmin E., 2021. "Machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine," Applied Energy, Elsevier, vol. 300(C).
    15. Deng, Xiaorong & Li, Jing & Liang, Yifei & Yang, Wenming, 2023. "Dual-fuel engines fueled with n-butanol/n-octanol and n-butanol/DNBE: A comparative study of combustion and emissions characteristics," Energy, Elsevier, vol. 263(PC).
    16. Assouline, Dan & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2018. "Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests," Applied Energy, Elsevier, vol. 217(C), pages 189-211.
    17. Yusaf, Talal F. & Buttsworth, D.R. & Saleh, Khalid H. & Yousif, B.F., 2010. "CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network," Applied Energy, Elsevier, vol. 87(5), pages 1661-1669, May.
    18. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Moreno, Sinvaldo Rodrigues & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2021. "A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting," Energy, Elsevier, vol. 216(C).
    19. Shi, Cheng & Ji, Changwei & Ge, Yunshan & Wang, Shuofeng & Yang, Jinxin & Wang, Huaiyu, 2021. "Effects of split direct-injected hydrogen strategies on combustion and emissions performance of a small-scale rotary engine," Energy, Elsevier, vol. 215(PA).
    20. Park, Cheolwoong & Kim, Changgi & Lee, Sangho & Lee, Sunyoup & Lee, Janghee, 2019. "Comparative evaluation of performance and emissions of CNG engine for heavy-duty vehicles fueled with various caloric natural gases," Energy, Elsevier, vol. 174(C), pages 1-9.
    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. Liu, Jinlong & Huang, Qiao & Ulishney, Christopher & Dumitrescu, Cosmin E., 2021. "Machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine," Applied Energy, Elsevier, vol. 300(C).
    2. Li, Ji & Zhou, Quan & He, Xu & Chen, Wan & Xu, Hongming, 2023. "Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives," Energy, Elsevier, vol. 272(C).
    3. Wang, Huaiyu & Ji, Changwei & Shi, Cheng & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan & Chang, Ke & Meng, Hao & Wang, Xin, 2023. "Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm," Energy, Elsevier, vol. 263(PD).
    4. Bahri, Bahram & Shahbakhti, Mahdi & Kannan, Kaushik & Aziz, Azhar Abdul, 2016. "Identification of ringing operation for low temperature combustion engines," Applied Energy, Elsevier, vol. 171(C), pages 142-152.
    5. Aliakbari, Karim & Ebrahimi-Moghadam, Amir & Pahlavanzadeh, Mohammadsadegh & Moradi, Reza, 2023. "Performance characteristics and exhaust emissions of a single-cylinder diesel engine for different fuels: Experimental investigation and artificial intelligence network," Energy, Elsevier, vol. 284(C).
    6. Roy, Sumit & Banerjee, Rahul & Bose, Probir Kumar, 2014. "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, Elsevier, vol. 119(C), pages 330-340.
    7. Mohamed Ismail, Harun & Ng, Hoon Kiat & Queck, Cheen Wei & Gan, Suyin, 2012. "Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends," Applied Energy, Elsevier, vol. 92(C), pages 769-777.
    8. Dhahad, Hayder Abed & Hasan, Ahmed Mudheher & Chaichan, Miqdam Tariq & Kazem, Hussein A., 2022. "Prognostic of diesel engine emissions and performance based on an intelligent technique for nanoparticle additives," Energy, Elsevier, vol. 238(PB).
    9. Muhssen, Hassan Sadah & Masuri, Siti Ujila & Sahari, Barkawi Bin & Hairuddin, Abdul Aziz, 2021. "Design improvement of compressed natural gas (CNG)-Air mixer for diesel dual-fuel engines using computational fluid dynamics," Energy, Elsevier, vol. 216(C).
    10. Lei, Yan & Li, Yong & Qiu, Tao & Li, Yunqiang & Wang, Yupeng & Zhang, Chengguo & Liu, Jiaxing & Ding, Mengzhu & Liu, Xianwu & Peng, Guangyu, 2021. "Effects of high-pressure methane jet on premixed ignited flame in constant-volume bomb," Energy, Elsevier, vol. 220(C).
    11. Rezaei, Javad & Shahbakhti, Mahdi & Bahri, Bahram & Aziz, Azhar Abdul, 2015. "Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks," Applied Energy, Elsevier, vol. 138(C), pages 460-473.
    12. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    13. Yap, Wai Kean & Karri, Vishy, 2011. "ANN virtual sensors for emissions prediction and control," Applied Energy, Elsevier, vol. 88(12), pages 4505-4516.
    14. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    15. Kshirsagar, Charudatta M. & Anand, Ramanathan, 2017. "Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses," Applied Energy, Elsevier, vol. 189(C), pages 555-567.
    16. Jiao, Huichao & Ye, Xianlei & Zou, Run & Wang, Nana & Liu, Jinxiang, 2022. "Comparative study on ignition and combustion between conventional spark-ignition method and near-wall surface ignition method for small-scale Wankel rotary engine," Energy, Elsevier, vol. 255(C).
    17. 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.
    18. Manieniyan, V. & Vinodhini, G. & Senthilkumar, R. & Sivaprakasam, S., 2016. "Wear element analysis using neural networks of a DI diesel engine using biodiesel with exhaust gas recirculation," Energy, Elsevier, vol. 114(C), pages 603-612.
    19. Yusaf, T.F. & Yousif, B.F. & Elawad, M.M., 2011. "Crude palm oil fuel for diesel-engines: Experimental and ANN simulation approaches," Energy, Elsevier, vol. 36(8), pages 4871-4878.
    20. Wang, Jujie & Cui, Quan & He, Maolin, 2022. "Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine," Chaos, Solitons & Fractals, Elsevier, vol. 156(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:eee:energy:v:280:y:2023:i:c:s0360544223014603. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.