IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v322y2025ics0360544225012496.html

Experimental and predictive analysis of knock inducing factors for HCNG-fueled spark ignition engines

Author

Listed:
  • Farhan, Muhammad
  • Shahid, Muhammad Ihsan
  • Rao, Anas
  • Chen, Tianhao
  • Salam, Hamza Ahmad
  • Xin, Li
  • Xiao, Qiuhong
  • Ma, Fanhua

Abstract

Hydrogen and its derived fuels offer significant potential in the transportation sector due to their superior performance and lower emissions. However, knock remains a major challenge in hydrogen-enriched fuels, limiting engine efficiency and durability. This study aims to identify the key factors influencing knock in a hydrogen-enriched compressed natural gas (HCNG) fueled spark-ignition (SI) engine under varying operating conditions. Experiments were conducted by altering engine load (25 %–100 %), hydrogen enrichment (0 %–40 %), exhaust gas recirculation (EGR) (0 %–29 %), spark timing (14° CA bTDC to 35° CA bTDC), and engine speed (700 rpm–1700 rpm). The effects on combustion characteristics, including burn duration, knock ratio (KR), coefficient of variation of indicated mean effective pressure COV % (imep), in-cylinder heat transfer rate, indicated mean effective pressure (imep), in-cylinder pressure, and exhaust temperature, were analyzed. Results indicate that increasing engine load from 25 % to 100 % led to a 75.5 % rise in KR and a 77.7 % increase in heat transfer rate. Advancing spark timing from 47° CA bTDC to 55° CA bTDC resulted in a 49.4 % rise in KR and a 3.5 % increase in exhaust temperature. Conversely, EGR application reduced KR by 33.2 % at 1700 rpm. To predict KR, three machine learning algorithms—neural network fitting tool, support vector regression and linear interactions—were applied, with bayesian regularization achieving the lowest mean squared error. These findings provide valuable insights for optimizing electronic control unit (ECU) calibration and advancing HCNG engine development.

Suggested Citation

  • Farhan, Muhammad & Shahid, Muhammad Ihsan & Rao, Anas & Chen, Tianhao & Salam, Hamza Ahmad & Xin, Li & Xiao, Qiuhong & Ma, Fanhua, 2025. "Experimental and predictive analysis of knock inducing factors for HCNG-fueled spark ignition engines," Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225012496
    DOI: 10.1016/j.energy.2025.135607
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225012496
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135607?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Mehra, Roopesh Kumar & Duan, Hao & Luo, Sijie & Rao, Anas & Ma, Fanhua, 2018. "Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios," Applied Energy, Elsevier, vol. 228(C), pages 736-754.
    2. Santiago Molina & Ricardo Novella & Josep Gomez-Soriano & Miguel Olcina-Girona, 2021. "New Combustion Modelling Approach for Methane-Hydrogen Fueled Engines Using Machine Learning and Engine Virtualization," Energies, MDPI, vol. 14(20), pages 1-21, October.
    3. Suleiman Hassan Otuoze & Dexter V. L. Hunt & Ian Jefferson, 2021. "Neural Network Approach to Modelling Transport System Resilience for Major Cities: Case Studies of Lagos and Kano (Nigeria)," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    4. Chiong, Meng-Choung & Kang, Hooi-Siang & Shaharuddin, Nik Mohd Ridzuan & Mat, Shabudin & Quen, Lee Kee & Ten, Ki-Hong & Ong, Muk Chen, 2021. "Challenges and opportunities of marine propulsion with alternative fuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. 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).
    6. Farhan, Muhammad & Chen, Tianhao & Rao, Anas & Shahid, Muhammad Ihsan & Xiao, Qiuhong & Salam, Hamza Ahmad & Ma, Fanhua, 2024. "An experimental study of knock analysis of HCNG fueled SI engine by different methods and prediction of knock intensity by particle swarm optimization-support vector machine," Energy, Elsevier, vol. 309(C).
    7. Farhan, Muhammad & Chen, Tianhao & Rao, Anas & Shahid, Muhammad Ihsan & Liu, Yongzheng & Ma, Fanhua, 2024. "Comparative knock analysis of HCNG fueled spark ignition engine using different heat transfer models and prediction of knock intensity by artificial neural network fitting tool," Energy, Elsevier, vol. 304(C).
    8. Zhen, Xudong & Wang, Yang & Xu, Shuaiqing & Zhu, Yongsheng & Tao, Chengjun & Xu, Tao & Song, Mingzhi, 2012. "The engine knock analysis – An overview," Applied Energy, Elsevier, vol. 92(C), pages 628-636.
    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. Tang, Yiming & Wang, Bin & Li, Lei & Zhang, Zifan & Wang, Haifeng & Yu, Hao & Shen, Bo & Xie, Fangxi, 2025. "Knock detection and diagnosis in engines based on the WPKNN approach," Energy, Elsevier, vol. 339(C).
    2. Shahid, Muhammad Ihsan & Farhan, Muhammad & Rao, Anas & Li, Wei & Salam, Hamza Ahmad & Xiao, Qiuhong & Chen, Tianhao & Li, Xin & Ma, Fanhua, 2025. "Effect of methane/water flowrate on waste heat recovery and hydrogen production by steam methane reforming process and predicted by artificial neural network (ANN)," Energy, Elsevier, vol. 331(C).
    3. Rao, Anas & Chen, Tianhao & Shahid, Muhammad Ihsan & Farhan, Muhammad & Xiao, Qiuhong & Ma, Fanhua, 2025. "Descriptive statistical analysis of cyclic combustion variability and performance metrics in a hydrogen-enriched CNG spark-ignition engine at low speed," Energy, Elsevier, vol. 327(C).
    4. Salam, Hamza Ahmad & Farhan, Muhammad & Shahid, Muhammad Ihsan & Chen, Tianhao & Rao, Anas & Li, Xin & Ma, Fanhua, 2025. "Experimental and predictive analysis of performance, emission, and combustion of a heavy-duty HCNG fueled spark-ignition engine by optimized support vector machine," Energy, Elsevier, vol. 335(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. Farhan, Muhammad & Chen, Tianhao & Rao, Anas & Shahid, Muhammad Ihsan & Xiao, Qiuhong & Salam, Hamza Ahmad & Ma, Fanhua, 2024. "An experimental study of knock analysis of HCNG fueled SI engine by different methods and prediction of knock intensity by particle swarm optimization-support vector machine," Energy, Elsevier, vol. 309(C).
    2. Farhan, Muhammad & Chen, Tianhao & Rao, Anas & Shahid, Muhammad Ihsan & Liu, Yongzheng & Ma, Fanhua, 2024. "Comparative knock analysis of HCNG fueled spark ignition engine using different heat transfer models and prediction of knock intensity by artificial neural network fitting tool," Energy, Elsevier, vol. 304(C).
    3. Salam, Hamza Ahmad & Farhan, Muhammad & Shahid, Muhammad Ihsan & Chen, Tianhao & Rao, Anas & Li, Xin & Ma, Fanhua, 2025. "Experimental and predictive analysis of performance, emission, and combustion of a heavy-duty HCNG fueled spark-ignition engine by optimized support vector machine," Energy, Elsevier, vol. 335(C).
    4. Hai, Tao & Hussein Kadir, Dler & Ghanbari, Afshin, 2023. "Modeling the emission characteristics of the hydrogen-enriched natural gas engines by multi-output least-squares support vector regression: Comprehensive statistical and operating analyses," Energy, Elsevier, vol. 276(C).
    5. Shahid, Muhammad Ihsan & Farhan, Muhammad & Rao, Anas & Li, Wei & Salam, Hamza Ahmad & Xiao, Qiuhong & Chen, Tianhao & Li, Xin & Ma, Fanhua, 2025. "Effect of methane/water flowrate on waste heat recovery and hydrogen production by steam methane reforming process and predicted by artificial neural network (ANN)," Energy, Elsevier, vol. 331(C).
    6. Farhan, Muhammad & Chen, Tianhao & Rao, Anas & Shahid, Muhammad Ihsan & Xiao, Qiuhong & Liu, Yongzheng & Ma, Fanhua, 2024. "Performance, emissions and combustion analysis of hydrogen-enriched compressed natural gas spark ignition engine by optimized Gaussian process regression and neural network at low speed on different loads," Energy, Elsevier, vol. 302(C).
    7. Haruki Tajima & Takuya Tomidokoro & Takeshi Yokomori, 2022. "Deep Learning for Knock Occurrence Prediction in SI Engines," Energies, MDPI, vol. 15(24), pages 1-14, December.
    8. Tang, Yiming & Wang, Bin & Li, Lei & Zhang, Zifan & Wang, Haifeng & Yu, Hao & Shen, Bo & Xie, Fangxi, 2025. "Knock detection and diagnosis in engines based on the WPKNN approach," Energy, Elsevier, vol. 339(C).
    9. Shahid, Muhammad Ihsan & Farhan, Muhammad & Rao, Anas & Salam, Hamza Ahmad & Chen, Tianhao & Xiao, Qiuhong & Li, Xin & Ma, Fanhua, 2025. "Optimization of hydrogen production and system efficiency enhancement through exhaust heat utilization in hydrogen-enriched internal combustion engine," Energy, Elsevier, vol. 319(C).
    10. Tehseen Johar & Chiu-Fan Hsieh, 2023. "Design Challenges in Hydrogen-Fueled Rotary Engine—A Review," Energies, MDPI, vol. 16(2), pages 1-22, January.
    11. Zhen, Xudong & Wang, Yang, 2013. "Study of ignition in a high compression ratio SI (spark ignition) methanol engine using LES (large eddy simulation) with detailed chemical kinetics," Energy, Elsevier, vol. 59(C), pages 549-558.
    12. Milad Asadi & Amir Oshnooei-Nooshabadi & Samira-Sadat Saleh & Fattaneh Habibnezhad & Sonia Sarafraz-Asbagh & John Lodewijk Van Genderen, 2022. "Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    13. Amaral, Lucimar Venâncio & Santos, Nathália Duarte Souza Alvarenga & Roso, Vinícius Rückert & Sebastião, Rita de Cássia de Oliveira & Pujatti, Fabrício José Pacheco, 2021. "Effects of gasoline composition on engine performance, exhaust gases and operational costs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    14. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    15. Vadlamudi, Srikanth & Gugulothu, S.K. & Panda, Jibitesh Kumar & Ağbulut, Ümit, 2025. "Machine learning based hydrogen fuel approach: A detailed experimental study on CRDI engine performance, combustion, and environmental characteristics," Energy, Elsevier, vol. 336(C).
    16. La Xiang & Enzhe Song & Yu Ding, 2018. "A Two-Zone Combustion Model for Knocking Prediction of Marine Natural Gas SI Engines," Energies, MDPI, vol. 11(3), pages 1-23, March.
    17. Guardiola, C. & Pla, B. & Bares, P. & Barbier, A., 2018. "An analysis of the in-cylinder pressure resonance excitation in internal combustion engines," Applied Energy, Elsevier, vol. 228(C), pages 1272-1279.
    18. Manimaran, Rajayokkiam & Mohanraj, Thangavelu & Venkatesan, Moorthy & Ganesan, Rajamohan & Balasubramanian, Dhinesh, 2022. "A computational technique for prediction and optimization of VCR engine performance and emission parameters fuelled with Trichosanthes cucumerina biodiesel using RSM with desirability function approach," Energy, Elsevier, vol. 254(PB).
    19. Yajun Xiong & Hui Tang & Xiaobo Tian, 2022. "Research on Structural Toughness of Railway City Network in Yellow River Basin and Case Study of Zhengzhou 7–20 Rainstorm Disaster," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    20. Wei, Haiqiao & Feng, Dengquan & Pan, Mingzhang & Pan, JiaYing & Rao, XiaoKang & Gao, Dongzhi, 2016. "Experimental investigation on the knocking combustion characteristics of n-butanol gasoline blends in a DISI engine," Applied Energy, Elsevier, vol. 175(C), pages 346-355.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:322:y:2025:i:c:s0360544225012496. 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.