IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i9p2410-d169286.html
   My bibliography  Save this article

Artificial Neural Network Modeling and Sensitivity Analysis of Performance and Emissions in a Compression Ignition Engine Using Biodiesel Fuel

Author

Listed:
  • Farzad Jaliliantabar

    (Mechanics of Biosystems Engineering Department, Tarbiat Modares University, Tehran 14115-336, Iran)

  • Barat Ghobadian

    (Mechanics of Biosystems Engineering Department, Tarbiat Modares University, Tehran 14115-336, Iran)

  • Gholamhassan Najafi

    (Mechanics of Biosystems Engineering Department, Tarbiat Modares University, Tehran 14115-336, Iran)

  • Talal Yusaf

    (Office of the Pro Vice-Chancellor, Federation University, Ballarat, VIC 3350, Australia)

Abstract

In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.

Suggested Citation

  • Farzad Jaliliantabar & Barat Ghobadian & Gholamhassan Najafi & Talal Yusaf, 2018. "Artificial Neural Network Modeling and Sensitivity Analysis of Performance and Emissions in a Compression Ignition Engine Using Biodiesel Fuel," Energies, MDPI, vol. 11(9), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2410-:d:169286
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/9/2410/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/9/2410/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ashnani, Mohammad Hossein Mohammadi & Johari, Anwar & Hashim, Haslenda & Hasani, Elham, 2014. "A source of renewable energy in Malaysia, why biodiesel?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 244-257.
    2. Wan Ghazali, Wan Nor Maawa & Mamat, Rizalman & Masjuki, H.H. & Najafi, Gholamhassan, 2015. "Effects of biodiesel from different feedstocks on engine performance and emissions: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 585-602.
    3. Chauhan, Bhupendra Singh & Kumar, Naveen & Cho, Haeng Muk, 2012. "A study on the performance and emission of a diesel engine fueled with Jatropha biodiesel oil and its blends," Energy, Elsevier, vol. 37(1), pages 616-622.
    4. Li, Shiwu & Wang, Yunpeng & Dong, Shengwu & Chen, Yang & Cao, Fenghua & Chai, Fang & Wang, Xiaohong, 2009. "Biodiesel production from Eruca Sativa Gars vegetable oil and motor, emissions properties," Renewable Energy, Elsevier, vol. 34(7), pages 1871-1876.
    5. 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.
    6. Rakopoulos, Dimitrios C. & Rakopoulos, Constantine D. & Giakoumis, Evangelos G. & Papagiannakis, Roussos G. & Kyritsis, Dimitrios C., 2014. "Influence of properties of various common bio-fuels on the combustion and emission characteristics of high-speed DI (direct injection) diesel engine: Vegetable oil, bio-diesel, ethanol, n-butanol, die," Energy, Elsevier, vol. 73(C), pages 354-366.
    7. Aydin, Hüseyin & Bayindir, Hasan, 2010. "Performance and emission analysis of cottonseed oil methyl ester in a diesel engine," Renewable Energy, Elsevier, vol. 35(3), pages 588-592.
    8. Ahmad Fitri Yusop & Rizalman Mamat & Talal Yusaf & Gholamhassan Najafi & Mohd Hafizil Mat Yasin & Akasyah Mohd Khathri, 2018. "Analysis of Particulate Matter (PM) Emissions in Diesel Engines Using Palm Oil Biodiesel Blended with Diesel Fuel," Energies, MDPI, vol. 11(5), pages 1-25, April.
    9. Safieddin Ardebili, M. & Ghobadian, B. & Najafi, G. & Chegeni, A., 2011. "Biodiesel production potential from edible oil seeds in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 3041-3044, August.
    10. 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.
    11. Sam Ki Yoon & Min Soo Kim & Han Joo Kim & Nag Jung Choi, 2014. "Effects of Canola Oil Biodiesel Fuel Blends on Combustion, Performance, and Emissions Reduction in a Common Rail Diesel Engine," Energies, MDPI, vol. 7(12), pages 1-18, December.
    12. 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.
    13. Ghobadian, Barat, 2012. "Liquid biofuels potential and outlook in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4379-4384.
    14. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
    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. Ooi, Jong Boon & Kau, Chia Chuin & Manoharan, Dilrukshan Naveen & Wang, Xin & Tran, Manh-Vu & Hung, Yew Mun, 2023. "Effects of multi-walled carbon nanotubes on the combustion, performance, and emission characteristics of a single-cylinder diesel engine fueled with palm-oil biodiesel-diesel blend," Energy, Elsevier, vol. 281(C).
    2. Nawar Al-Esawi & Mansour Al Qubeissi & Ruslana Kolodnytska, 2019. "The Impact of Biodiesel Fuel on Ethanol/Diesel Blends," Energies, MDPI, vol. 12(9), pages 1-11, May.
    3. 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.
    4. 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.
    5. 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.
    6. Jaliliantabar, Farzad & Ghobadian, Barat & Carlucci, Antonio Paolo & Najafi, Gholamhassan & Mamat, Rizalman & Ficarella, Antonio & Strafella, Luciano & Santino, Angelo & De Domenico, Stefania, 2020. "A comprehensive study on the effect of pilot injection, EGR rate, IMEP and biodiesel characteristics on a CRDI diesel engine," Energy, Elsevier, vol. 194(C).
    7. Piotr Bera, 2019. "Development of Engine Efficiency Characteristic in Dynamic Working States," Energies, MDPI, vol. 12(15), pages 1-14, July.
    8. Chukwuemeka Uguba Owora & Samson Kolawole Fasogbon, 2020. "Rainfall Variability and Trends over Central Ethiopia," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 24(4), pages 145-156, May.

    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. E, Jiaqiang & Pham, Minhhieu & Zhao, D. & Deng, Yuanwang & Le, DucHieu & Zuo, Wei & Zhu, Hao & Liu, Teng & Peng, Qingguo & Zhang, Zhiqing, 2017. "Effect of different technologies on combustion and emissions of the diesel engine fueled with biodiesel: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 620-647.
    2. Hosseini, Seyed Ehsan & Andwari, Amin Mahmoudzadeh & Wahid, Mazlan Abdul & Bagheri, Ghobad, 2013. "A review on green energy potentials in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 533-545.
    3. Zaharin, M.S.M. & Abdullah, N.R. & Najafi, G. & Sharudin, H. & Yusaf, T., 2017. "Effects of physicochemical properties of biodiesel fuel blends with alcohol on diesel engine performance and exhaust emissions: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 475-493.
    4. 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.
    5. 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.
    6. Jaliliantabar, Farzad & Ghobadian, Barat & Najafi, Gholamhassan & Mamat, Rizalman & Carlucci, Antonio Paolo, 2019. "Multi-objective NSGA-II optimization of a compression ignition engine parameters using biodiesel fuel and exhaust gas recirculation," Energy, Elsevier, vol. 187(C).
    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. Taghizadeh-Alisaraei, Ahmad & Motevali, Ali & Ghobadian, Barat, 2019. "Ethanol production from date wastes: Adapted technologies, challenges, and global potential," Renewable Energy, Elsevier, vol. 143(C), pages 1094-1110.
    9. 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).
    10. 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.
    11. Mahmudul, H.M. & Hagos, F.Y. & Mamat, R. & Adam, A. Abdul & Ishak, W.F.W. & Alenezi, R., 2017. "Production, characterization and performance of biodiesel as an alternative fuel in diesel engines – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 497-509.
    12. Mofijur, M. & Atabani, A.E. & Masjuki, H.H. & Kalam, M.A. & Masum, B.M., 2013. "A study on the effects of promising edible and non-edible biodiesel feedstocks on engine performance and emissions production: A comparative evaluation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 391-404.
    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. 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.
    15. Hoseini, S.S. & Najafi, G. & Ghobadian, B. & Mamat, R. & Ebadi, M.T. & Yusaf, Talal, 2018. "Novel environmentally friendly fuel: The effects of nanographene oxide additives on the performance and emission characteristics of diesel engines fuelled with Ailanthus altissima biodiesel," Renewable Energy, Elsevier, vol. 125(C), pages 283-294.
    16. Mofijur, M. & Rasul, M.G. & Hyde, J. & Azad, A.K. & Mamat, R. & Bhuiya, M.M.K., 2016. "Role of biofuel and their binary (diesel–biodiesel) and ternary (ethanol–biodiesel–diesel) blends on internal combustion engines emission reduction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 265-278.
    17. Tayari, Sara & Abedi, Reza & Rahi, Abbas, 2020. "Comparative assessment of engine performance and emissions fueled with three different biodiesel generations," Renewable Energy, Elsevier, vol. 147(P1), pages 1058-1069.
    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. 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.

    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:gam:jeners:v:11:y:2018:i:9:p:2410-:d:169286. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.