IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v198y2020ics0360544220303935.html
   My bibliography  Save this article

NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis

Author

Listed:
  • Wang, Guoyang
  • Awad, Omar I.
  • Liu, Shiyu
  • Shuai, Shijin
  • Wang, Zhiming

Abstract

Information on Nitrogen oxide (NOx) concentrations play a significant role in aftertreatment systems. In this work, a method to estimate NOx emissions by means of mutual information (MI) and back propagation neural network (BPNN) was introduced. All measured signals were ranked by MI value and the most significant parameters were classified according to their physical meanings. The model inputs were selected by analysis of the classified groups. The forecasting model was developed by the BPNN algorithm to predict raw NOx emissions and NO mass flow rate (MFR) before Selective catalytic reduction (SCR) with selected input variables. Ranking, classification, selection, and training were carried out under steady-state conditions and the world harmonized stationary cycle. The verified BPNN network could well predict raw NOx emissions and NO MFR before SCR. Compared to static map prediction, the mean absolute deviation and root mean square error of BPNN are reduced by about 15%, which also indicated that the MI-based feature selection method was effective. The proposed approach is a generic approach for NOx emission prediction, which could also reduce the requirement for expert knowledge on feature selection, has a lower computational cost, and could be used in engine and aftertreatment control system of real driving vehicle.

Suggested Citation

  • Wang, Guoyang & Awad, Omar I. & Liu, Shiyu & Shuai, Shijin & Wang, Zhiming, 2020. "NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis," Energy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220303935
    DOI: 10.1016/j.energy.2020.117286
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220303935
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117286?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. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    2. Raptotasios, Spiridon I. & Sakellaridis, Nikolaos F. & Papagiannakis, Roussos G. & Hountalas, Dimitrios T., 2015. "Application of a multi-zone combustion model to investigate the NOx reduction potential of two-stroke marine diesel engines using EGR," Applied Energy, Elsevier, vol. 157(C), pages 814-823.
    3. Hu, Jie & Zeng, Jiawei & Wei, Li, 2018. "Failure diagnosis and tolerant control method for hydrothermally aged SCR system by utilizing EKF observer and MRAC controller," Energy, Elsevier, vol. 156(C), pages 103-121.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Fengyu & Su, Xinchao & Tan, Aoli & Yao, Jingjing & Li, Haipu, 2022. "Prediction of research octane number loss and sulfur content in gasoline refining using machine learning," Energy, Elsevier, vol. 261(PA).
    2. Wang, Jianxing & Guo, Lili & Zhang, Chengying & Song, Lei & Duan, Jiangyong & Duan, Liqiang, 2020. "Thermal power forecasting of solar power tower system by combining mechanism modeling and deep learning method," Energy, Elsevier, vol. 208(C).
    3. Xue-Bo Jin & Zhong-Yao Wang & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su & Hui-Jun Ma & Prasun Chakrabarti, 2023. "Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting," Mathematics, MDPI, vol. 11(4), pages 1-18, February.
    4. Wang, Zhihong & Luo, Kangwei & Yu, Hongsen & Feng, Kai & Ding, Hang, 2024. "NOx Emission prediction of heavy-duty diesel vehicles based on Bayesian optimization -Gated Recurrent Unit algorithm," Energy, Elsevier, vol. 292(C).
    5. Jia, Xiongjie & Sang, Yichen & Li, Yanjun & Du, Wei & Zhang, Guolei, 2022. "Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework," Energy, Elsevier, vol. 239(PE).
    6. Ye, Jiahao & Peng, Qingguo, 2023. "Improved emissions conversion of diesel oxidation catalyst using multifactor impact analysis and neural network," Energy, Elsevier, vol. 271(C).

    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. Maria Faruoli & Alessandro Coclite & Annarita Viggiano & Paolo Caso & Vinicio Magi, 2021. "A Comprehensive Numerical Analysis of the Scavenging Process in a Uniflow Two-Stroke Diesel Engine for General Aviation," Energies, MDPI, vol. 14(21), pages 1-19, November.
    2. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    3. Gu, Jie & Wang, Yingyuan & Hu, Jiancun & Zhang, Kun & Shi, Lei & Deng, Kangyao, 2024. "Real-time prediction of fuel consumption and emissions based on deep autoencoding support vector regression for cylinder pressure-based feedback control of marine diesel engines," Energy, Elsevier, vol. 300(C).
    4. Zhu, Sipeng & Gu, Yuncheng & Yuan, Hao & Ma, Zetai & Deng, Kangyao, 2020. "Thermodynamic analysis of the turbocharged marine two-stroke engine cycle with different scavenging air control technologies," Energy, Elsevier, vol. 191(C).
    5. Hoseini, S.S. & Najafi, G. & Ghobadian, B. & Mamat, Rizalman & Sidik, Nor Azwadi Che & Azmi, W.H., 2017. "The effect of combustion management on diesel engine emissions fueled with biodiesel-diesel blends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 307-331.
    6. Wang, Dawei & Shi, Lei & Zhu, Sipeng & Liu, Bo & Qian, Yuehua & Deng, Kangyao, 2020. "Numerical and thermodynamic study on effects of high and low pressure exhaust gas recirculation on turbocharged marine low-speed engine," Applied Energy, Elsevier, vol. 261(C).
    7. Zhu, Dengting & Zheng, Xinqian, 2019. "Fuel consumption and emission characteristics in asymmetric twin-scroll turbocharged diesel engine with two exhaust gas recirculation circuits," Applied Energy, Elsevier, vol. 238(C), pages 985-995.
    8. Xingyu Liang & Zhijie Zhu & Xinyi Cao & Kun Wang & Yuesen Wang, 2022. "Research on the Soot Generation of Diesel Surrogate Mechanisms of Different Carbon Chain Length," Energies, MDPI, vol. 15(20), pages 1-17, October.
    9. Theotokatos, Gerasimos & Guan, Cong & Chen, Hui & Lazakis, Iraklis, 2018. "Development of an extended mean value engine model for predicting the marine two-stroke engine operation at varying settings," Energy, Elsevier, vol. 143(C), pages 533-545.
    10. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    11. Zamboni, Giorgio & Moggia, Simone & Capobianco, Massimo, 2016. "Hybrid EGR and turbocharging systems control for low NOX and fuel consumption in an automotive diesel engine," Applied Energy, Elsevier, vol. 165(C), pages 839-848.
    12. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
    13. Iqbal, Rashid & Liu, Yancheng & Zeng, Yuji & Zhang, Qinjin & Zeeshan, Muhammad, 2024. "Comparative study based on techno-economics analysis of different shipboard microgrid systems comprising PV/wind/fuel cell/battery/diesel generator with two battery technologies: A step toward green m," Renewable Energy, Elsevier, vol. 221(C).
    14. Shichao Huang & Jing Zhang & Yu He & Xiaofan Fu & Luqin Fan & Gang Yao & Yongjun Wen, 2022. "Short-Term Load Forecasting Based on the CEEMDAN-Sample Entropy-BPNN-Transformer," Energies, MDPI, vol. 15(10), pages 1-14, May.
    15. Giuntini, Lorenzo & Lamioni, Rachele & Linari, Luca & Saccomano, Pietro & Mainardi, Davide & Tognotti, Leonardo & Galletti, Chiara, 2022. "Decarbonization of a tissue paper plant: Advanced numerical simulations to assess the replacement of fossil fuels with a biomass-derived syngas," Renewable Energy, Elsevier, vol. 198(C), pages 884-893.
    16. Hu, Yusha & Man, Yi, 2022. "Two-stage energy scheduling optimization model for complex industrial process and its industrial verification," Renewable Energy, Elsevier, vol. 193(C), pages 879-894.
    17. Yuan, Yupeng & Wang, Jixiang & Yan, Xinping & Shen, Boyang & Long, Teng, 2020. "A review of multi-energy hybrid power system for ships," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    18. Gong, Zhen & Feng, Liyan & Qu, Wenjing & Li, Lincheng & Wei, Lai, 2020. "Auto-ignition characteristics of methane/n-heptane mixtures under carbon dioxide and water dilution conditions," Applied Energy, Elsevier, vol. 278(C).
    19. Ling-Chin, Janie & Roskilly, Anthony P., 2016. "Investigating the implications of a new-build hybrid power system for Roll-on/Roll-off cargo ships from a sustainability perspective – A life cycle assessment case study," Applied Energy, Elsevier, vol. 181(C), pages 416-434.
    20. Pang, Kar Mun & Karvounis, Nikolas & Walther, Jens Honore & Schramm, Jesper, 2016. "Numerical investigation of soot formation and oxidation processes under large two-stroke marine diesel engine-like conditions using integrated CFD-chemical kinetics," Applied Energy, Elsevier, vol. 169(C), pages 874-887.

    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:198:y:2020:i:c:s0360544220303935. 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.