IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v160y2018icp774-782.html
   My bibliography  Save this article

Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network model

Author

Listed:
  • Javed, Syed
  • Baig, Rahmath Ulla
  • Murthy, Y.V.V. Satyanarayana

Abstract

Two challenges that have motivated researchers are the mitigation of emissions and a reduction in the reliance on diesel fuel. A potential replacement for diesel is biodiesel, which is derived from animal fat or vegetable oil. The large number of studies on the performance and emission characteristics of biodiesel is notable. In such studies, the noise emissions have seldom been disregarded or treated as a trivial matter. Extending the previously published research by the authors, an experimental investigation was carried out to study the effects of new fuel types on the noise emissions. Blends of Jatropha methyl ester (JME) biodiesel suspended with zinc oxide (ZnO) nanoparticles along with hydrogen (H2) in dual-fuel mode were used as fuel for an experimental diesel engine test rig. The noise levels in decibels (dB) under variations in the biodiesel percentage, nanoparticle size, and flow rates of H2 at different loads were recorded. It was observed that 20% and 30% JME biodiesel blends suspended with ZnO nanoparticles of 40 nm in size have superior noise attenuation. To avoid a strenuous experimentation, an artificial neural network model was developed for noise prediction with a regression coefficient of 0.9992.

Suggested Citation

  • Javed, Syed & Baig, Rahmath Ulla & Murthy, Y.V.V. Satyanarayana, 2018. "Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network model," Energy, Elsevier, vol. 160(C), pages 774-782.
    • Handle: RePEc:eee:energy:v:160:y:2018:i:c:p:774-782
    DOI: 10.1016/j.energy.2018.07.041
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218313379
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.07.041?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. 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.
    2. Ramadhas, A.S. & Muraleedharan, C. & Jayaraj, S., 2005. "Performance and emission evaluation of a diesel engine fueled with methyl esters of rubber seed oil," Renewable Energy, Elsevier, vol. 30(12), pages 1789-1800.
    3. Gölcü, Mustafa & Sekmen, Yakup & ErduranlI, Perihan & Sahir Salman, M., 2005. "Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine," Applied Energy, Elsevier, vol. 81(2), pages 187-197, June.
    4. 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.
    5. Saravanan, N. & Nagarajan, G., 2010. "Performance and emission studies on port injection of hydrogen with varied flow rates with Diesel as an ignition source," Applied Energy, Elsevier, vol. 87(7), pages 2218-2229, July.
    6. 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.
    7. Ramadhas, A.S. & Jayaraj, S. & Muraleedharan, C. & Padmakumari, K., 2006. "Artificial neural networks used for the prediction of the cetane number of biodiesel," Renewable Energy, Elsevier, vol. 31(15), pages 2524-2533.
    8. Zhou, J.H. & Cheung, C.S. & Leung, C.W., 2014. "Combustion, performance, regulated and unregulated emissions of a diesel engine with hydrogen addition," Applied Energy, Elsevier, vol. 126(C), pages 1-12.
    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. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    2. Dey, Suman & Reang, Narath Moni & Majumder, Arindam & Deb, Madhujit & Das, Pankaj Kumar, 2020. "A hybrid ANN-Fuzzy approach for optimization of engine operating parameters of a CI engine fueled with diesel-palm biodiesel-ethanol blend," Energy, Elsevier, vol. 202(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. Roy, Sumit & Ghosh, Ashmita & Das, Ajoy Kumar & Banerjee, Rahul, 2015. "Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR," Applied Energy, Elsevier, vol. 140(C), pages 52-64.
    2. Mohammed I. Jahirul & Richard J. Brown & Wijitha Senadeera & Ian M. O'Hara & Zoran D. Ristovski, 2013. "The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks," Energies, MDPI, vol. 6(8), pages 1-43, July.
    3. Kumar, Niraj & Varun, & Chauhan, Sant Ram, 2013. "Performance and emission characteristics of biodiesel from different origins: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 633-658.
    4. Ganesan, P. & Rajakarunakaran, S. & Thirugnanasambandam, M. & Devaraj, D., 2015. "Artificial neural network model to predict the diesel electric generator performance and exhaust emissions," Energy, Elsevier, vol. 83(C), pages 115-124.
    5. Wong, Ka In & Wong, Pak Kin & Cheung, Chun Shun & Vong, Chi Man, 2013. "Modeling and optimization of biodiesel engine performance using advanced machine learning methods," Energy, Elsevier, vol. 55(C), pages 519-528.
    6. Çay, Yusuf & Korkmaz, Ibrahim & Çiçek, Adem & Kara, Fuat, 2013. "Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network," Energy, Elsevier, vol. 50(C), pages 177-186.
    7. 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.
    8. Deb, Madhujit & Paul, Abhishek & Debroy, Durbadal & Sastry, G.R.K. & Panua, Raj Sekhar & Bose, P.K., 2015. "An experimental investigation of performance-emission trade off characteristics of a CI engine using hydrogen as dual fuel," Energy, Elsevier, vol. 85(C), pages 569-585.
    9. Altarazi, Yazan S.M. & Abu Talib, Abd Rahim & Yu, Jianglong & Gires, Ezanee & Abdul Ghafir, Mohd Fahmi & Lucas, John & Yusaf, Talal, 2022. "Effects of biofuel on engines performance and emission characteristics: A review," Energy, Elsevier, vol. 238(PC).
    10. Sun, Ping & Zhang, Jufang & Dong, Wei & Li, Decheng & Yu, Xiumin, 2023. "Prediction of oxygen-enriched combustion and emission performance on a spark ignition engine using artificial neural networks," Applied Energy, Elsevier, vol. 348(C).
    11. 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.
    12. Babu, D. & Thangarasu, Vinoth & Ramanathan, Anand, 2020. "Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel," Applied Energy, Elsevier, vol. 263(C).
    13. Bishop, Justin D.K. & Stettler, Marc E.J. & Molden, N. & Boies, Adam M., 2016. "Engine maps of fuel use and emissions from transient driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 202-217.
    14. Kaya, Canan & Hamamci, Candan & Baysal, Akin & Akba, Osman & Erdogan, Sait & Saydut, Abdurrahman, 2009. "Methyl ester of peanut (Arachis hypogea L.) seed oil as a potential feedstock for biodiesel production," Renewable Energy, Elsevier, vol. 34(5), pages 1257-1260.
    15. Sanjid, A. & Masjuki, H.H. & Kalam, M.A. & Rahman, S.M. Ashrafur & Abedin, M.J. & Palash, S.M., 2013. "Impact of palm, mustard, waste cooking oil and Calophyllum inophyllum biofuels on performance and emission of CI engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 664-682.
    16. 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.
    17. Yilmaz, I.T. & Gumus, M., 2018. "Effects of hydrogen addition to the intake air on performance and emissions of common rail diesel engine," Energy, Elsevier, vol. 142(C), pages 1104-1113.
    18. 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.
    19. Yang, Bo & Xi, Chengxun & Wei, Xing & Zeng, Ke & Lai, Ming-Chia, 2015. "Parametric investigation of natural gas port injection and diesel pilot injection on the combustion and emissions of a turbocharged common rail dual-fuel engine at low load," Applied Energy, Elsevier, vol. 143(C), pages 130-137.
    20. Dey, Suman & Reang, Narath Moni & Majumder, Arindam & Deb, Madhujit & Das, Pankaj Kumar, 2020. "A hybrid ANN-Fuzzy approach for optimization of engine operating parameters of a CI engine fueled with diesel-palm biodiesel-ethanol blend," Energy, Elsevier, vol. 202(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:160:y:2018:i:c:p:774-782. 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.