IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v111y2013icp827-841.html
   My bibliography  Save this article

Computational study of biodiesel–diesel fuel blends on emission characteristics for a light-duty diesel engine using OpenFOAM

Author

Listed:
  • Mohamed Ismail, Harun
  • Ng, Hoon Kiat
  • Gan, Suyin
  • Lucchini, Tommaso

Abstract

In this paper, emissions formation process and its interaction with the combustion event are established for fossil diesel and the methyl esters of coconut (CME), palm (PME) and soy (SME) across three different engine conditions. Here, the OpenFOAM® open source CFD codes are utilised to simulate the in-cylinder events. The ignition delay (ID) period and the timing of peak pressure are accurately predicted to within ±0.2° crank angle for all the test cases. The maximum error between the experimental and computed peak pressure values across the test range is limited to below 4.5%. The change in the fuel type from fossil diesel to biodiesel alters the physical and the chemical delays, both of which affect the overall ID period. As a result, variations in the combustion behaviour and hence the emission characteristics are observed. Neat CME is found to produce both NOx and soot reduction across all the engine loads tested. The most significant reduction in soot level is achieved at high load operation, while greatest NOx reduction is recorded under low load condition when neat or B50 blends of the test biodiesel fuels are used. The best operating condition to result in simultaneous soot and NOx reduction through the use of biodiesel is at mid load condition with an engine speed of 2000rev/min.

Suggested Citation

  • Mohamed Ismail, Harun & Ng, Hoon Kiat & Gan, Suyin & Lucchini, Tommaso, 2013. "Computational study of biodiesel–diesel fuel blends on emission characteristics for a light-duty diesel engine using OpenFOAM," Applied Energy, Elsevier, vol. 111(C), pages 827-841.
    • Handle: RePEc:eee:appene:v:111:y:2013:i:c:p:827-841
    DOI: 10.1016/j.apenergy.2013.05.068
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261913004819
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2013.05.068?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. Mohamed Ismail, Harun & Ng, Hoon Kiat & Gan, Suyin, 2012. "Evaluation of non-premixed combustion and fuel spray models for in-cylinder diesel engine simulation," Applied Energy, Elsevier, vol. 90(1), pages 271-279.
    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. Plamondon, E. & Seers, P., 2014. "Development of a simplified dynamic model for a piezoelectric injector using multiple injection strategies with biodiesel/diesel-fuel blends," Applied Energy, Elsevier, vol. 131(C), pages 411-424.
    2. Ho, Sze-Hwee & Wong, Yiik-Diew & Chang, Victor Wei-Chung, 2014. "Evaluating the potential of biodiesel (via recycled cooking oil) use in Singapore, an urban city," Resources, Conservation & Recycling, Elsevier, vol. 91(C), pages 117-124.
    3. Zhang, Yanzhi & Li, Zilong & Tamilselvan, Pachiannan & Jiang, Chenxu & He, Zhixia & Zhong, Wenjun & Qian, Yong & Wang, Qian & Lu, Xingcai, 2019. "Experimental study of combustion and emission characteristics of gasoline compression ignition (GCI) engines fueled by gasoline-hydrogenated catalytic biodiesel blends," Energy, Elsevier, vol. 187(C).
    4. Decan, Gilles & Broekaert, Stijn & Lucchini, Tommaso & D’Errico, Gianluca & Vierendeels, Jan & Verhelst, Sebastian, 2018. "Evaluation of wall heat flux calculation methods for CFD simulations of an internal combustion engine under both motored and HCCI operation," Applied Energy, Elsevier, vol. 232(C), pages 451-461.
    5. Bari, S. & Saad, Idris, 2014. "Effect of guide vane height on the performance and emissions of a compression ignition (CI) engine run with biodiesel through simulation and experiment," Applied Energy, Elsevier, vol. 136(C), pages 431-444.
    6. Ali Raza & Hassan Mehboob & Sajjad Miran & Waseem Arif & Syed Farukh Javaid Rizvi, 2020. "Investigation on the Characteristics of Biodiesel Droplets in the Engine Cylinder," Energies, MDPI, vol. 13(14), pages 1-14, July.
    7. Khan, Shahanwaz & Panua, Rajsekhar & Bose, Probir Kumar, 2019. "The impact of combustion chamber configuration on combustion and emissions of a single cylinder diesel engine fuelled with soybean methyl ester blends with diesel," Renewable Energy, Elsevier, vol. 143(C), pages 335-351.
    8. Serrano, L. & Lopes, M. & Pires, N. & Ribeiro, I. & Cascão, P. & Tarelho, L. & Monteiro, A. & Nielsen, O. & da Silva, M. Gameiro & Borrego, C., 2015. "Evaluation on effects of using low biodiesel blends in a EURO 5 passenger vehicle equipped with a common-rail diesel engine," Applied Energy, Elsevier, vol. 146(C), pages 230-238.

    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. Channapattana, S.V. & Pawar, Abhay A. & Kamble, Prashant G., 2017. "Optimisation of operating parameters of DI-CI engine fueled with second generation Bio-fuel and development of ANN based prediction model," Applied Energy, Elsevier, vol. 187(C), pages 84-95.
    2. Fukang Ma & Changlu Zhao & Fujun Zhang & Zhenfeng Zhao & Shuanlu Zhang, 2015. "Effects of Scavenging System Configuration on In-Cylinder Air Flow Organization of an Opposed-Piston Two-Stroke Engine," Energies, MDPI, vol. 8(6), pages 1-19, June.
    3. 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.
    4. 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.
    5. 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.
    6. Chang, Yu-Cheng & Lee, Wen-Jhy & Wang, Lin-Chi & Yang, Hsi-Hsien & Cheng, Man-Ting & Lu, Jau-Huai & Tsai, Ying I. & Young, Li-Hao, 2014. "Effects of waste cooking oil-based biodiesel on the toxic organic pollutant emissions from a diesel engine," Applied Energy, Elsevier, vol. 113(C), pages 631-638.
    7. 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.
    8. 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.
    9. Edward Roper & Yaodong Wang & Zhichao Zhang, 2022. "Numerical Investigation of the Application of Miller Cycle and Low-Carbon Fuels to Increase Diesel Engine Efficiency and Reduce Emissions," Energies, MDPI, vol. 15(5), pages 1-20, February.
    10. Talebian-Kiakalaieh, Amin & Amin, Nor Aishah Saidina & Zarei, Alireza & Noshadi, Iman, 2013. "Transesterification of waste cooking oil by heteropoly acid (HPA) catalyst: Optimization and kinetic model," Applied Energy, Elsevier, vol. 102(C), pages 283-292.
    11. 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).
    12. 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.
    13. 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).
    14. Ng, Hoon Kiat & Gan, Suyin & Ng, Jo-Han & Pang, Kar Mun, 2013. "Simulation of biodiesel combustion in a light-duty diesel engine using integrated compact biodiesel–diesel reaction mechanism," Applied Energy, Elsevier, vol. 102(C), pages 1275-1287.
    15. Zhao, Junfeng & Wang, Junmin, 2013. "Control-oriented multi-phase combustion model for biodiesel fueled engines," Applied Energy, Elsevier, vol. 108(C), pages 92-99.
    16. 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.
    17. 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.
    18. Pang, Kar Mun & Ng, Hoon Kiat & Gan, Suyin, 2012. "In-cylinder diesel spray combustion simulations using parallel computation: A performance benchmarking study," Applied Energy, Elsevier, vol. 93(C), pages 466-478.
    19. Wong, Pak Kin & Wong, Ka In & Vong, Chi Man & Cheung, Chun Shun, 2015. "Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search," Renewable Energy, Elsevier, vol. 74(C), pages 640-647.
    20. Lin, Jiefeng & Gaustad, Gabrielle & Trabold, Thomas A., 2013. "Profit and policy implications of producing biodiesel–ethanol–diesel fuel blends to specification," Applied Energy, Elsevier, vol. 104(C), pages 936-944.

    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:appene:v:111:y:2013:i:c:p:827-841. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.