IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v114y2016icp603-612.html
   My bibliography  Save this article

Wear element analysis using neural networks of a DI diesel engine using biodiesel with exhaust gas recirculation

Author

Listed:
  • Manieniyan, V.
  • Vinodhini, G.
  • Senthilkumar, R.
  • Sivaprakasam, S.

Abstract

Wear is an important characteristic because of its great value in connection with the engine parts. The main focus of this work is to analyze the effect of neural network models for predicting engine performance such as wear of the DI diesel engine using B20 blend of Methyl Ester of Mahua (MEOM) and diesel. Experimental results revealed that 20% biodiesel blend is the optimum blend in terms of performance, emission and combustion characteristics. For B20 blend, it was also found experimentally that 15% hot EGR and 20% cold EGR were the optimum EGR ratios. Under the optimum EGR ratios identified, a series of experimental work was done with B20 blend and diesel at various loads to obtain the concentration of wear metals from the lubricating oil. Experimentally, it was found that wear metals were found to be lower for B20 biodiesel compared to diesel. Artificial neural networks (ANN) have become the premier candidate as the modeling tool. Using the experimental data, ANN models based on probabilistic neural networks (PNN) and radial basis function neural networks (RBFN) for predicting the engine wear were developed. The results show that ANN is sufficient enough in predicting the engine wear in terms of mean square error (MSE) and regression coefficient (R). Also among the ANN models tested, RBFN performs significantly better than PNN.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:114:y:2016:i:c:p:603-612
    DOI: 10.1016/j.energy.2016.08.040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216311537
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.08.040?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. Shivakumar & Srinivasa Pai, P. & Shrinivasa Rao, B.R., 2011. "Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings," Applied Energy, Elsevier, vol. 88(7), pages 2344-2354, July.
    3. Betiku, Eriola & Omilakin, Oluwasesan Ropo & Ajala, Sheriff Olalekan & Okeleye, Adebisi Aminat & Taiwo, Abiola Ezekiel & Solomon, Bamidele Ogbe, 2014. "Mathematical modeling and process parameters optimization studies by artificial neural network and response surface methodology: A case of non-edible neem (Azadirachta indica) seed oil biodiesel synth," Energy, Elsevier, vol. 72(C), pages 266-273.
    4. 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.
    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. Mohammad Fauzi, Ahmad Hafiidz & Amin, Nor Aishah Saidina & Mat, Ramli, 2014. "Esterification of oleic acid to biodiesel using magnetic ionic liquid: Multi-objective optimization and kinetic study," Applied Energy, Elsevier, vol. 114(C), pages 809-818.
    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. 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.
    9. Arcaklioglu, Erol & Çelikten, Ismet, 2005. "A diesel engine's performance and exhaust emissions," Applied Energy, Elsevier, vol. 80(1), pages 11-22, January.
    10. Moradi, G.R. & Dehghani, S. & Khosravian, F. & Arjmandzadeh, A., 2013. "The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield," Renewable Energy, Elsevier, vol. 50(C), pages 915-920.
    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. Chen, Hao & Wang, Yu & Zuo, Mingsheng & Zhang, Chao & Jia, Ninghong & Liu, Xiliang & Yang, Shenglai, 2022. "A new prediction model of CO2 diffusion coefficient in crude oil under reservoir conditions based on BP neural network," Energy, Elsevier, vol. 239(PC).
    2. Li, Tianyu & Liu, Huiying & Ding, Daolin, 2018. "Predictive energy management of fuel cell supercapacitor hybrid construction equipment," Energy, Elsevier, vol. 149(C), pages 718-729.

    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. 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. 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.
    3. Bahri, Bahram & Shahbakhti, Mahdi & Kannan, Kaushik & Aziz, Azhar Abdul, 2016. "Identification of ringing operation for low temperature combustion engines," Applied Energy, Elsevier, vol. 171(C), pages 142-152.
    4. 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.
    5. Bendu, Harisankar & Deepak, B.B.V.L. & Murugan, S., 2017. "Multi-objective optimization of ethanol fuelled HCCI engine performance using hybrid GRNN–PSO," Applied Energy, Elsevier, vol. 187(C), pages 601-611.
    6. Hosseini, Seyyed Hassan & Taghizadeh-Alisaraei, Ahmad & Ghobadian, Barat & Abbaszadeh-Mayvan, Ahmad, 2020. "Artificial neural network modeling of performance, emission, and vibration of a CI engine using alumina nano-catalyst added to diesel-biodiesel blends," Renewable Energy, Elsevier, vol. 149(C), pages 951-961.
    7. Yusri, I.M. & Abdul Majeed, A.P.P. & Mamat, R. & Ghazali, M.F. & Awad, Omar I. & Azmi, W.H., 2018. "A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 665-686.
    8. Song Hu & Stefano d’Ambrosio & Roberto Finesso & Andrea Manelli & Mario Rocco Marzano & Antonio Mittica & Loris Ventura & Hechun Wang & Yinyan Wang, 2019. "Comparison of Physics-Based, Semi-Empirical and Neural Network-Based Models for Model-Based Combustion Control in a 3.0 L Diesel Engine," Energies, MDPI, vol. 12(18), pages 1-41, September.
    9. 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.
    10. Bhowmik, Subrata & Paul, Abhishek & Panua, Rajsekhar & Ghosh, Subrata Kumar & Debroy, Durbadal, 2018. "Performance-exhaust emission prediction of diesosenol fueled diesel engine: An ANN coupled MORSM based optimization," Energy, Elsevier, vol. 153(C), pages 212-222.
    11. Subrata Bhowmik & Rajsekhar Panua & Subrata K Ghosh & Abhishek Paul & Durbadal Debroy, 2018. "Prediction of performance and exhaust emissions of diesel engine fuelled with adulterated diesel: An artificial neural network assisted fuzzy-based topology optimization," Energy & Environment, , vol. 29(8), pages 1413-1437, December.
    12. 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.
    13. 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.
    14. 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).
    15. 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.
    16. 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.
    17. 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.
    18. Yap, Wai Kean & Karri, Vishy, 2011. "ANN virtual sensors for emissions prediction and control," Applied Energy, Elsevier, vol. 88(12), pages 4505-4516.
    19. 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).
    20. Rajkumar, Sundararajan & Das, Arnab & Thangaraja, Jeyaseelan, 2022. "Integration of artificial neural network, multi-objective genetic algorithm and phenomenological combustion modelling for effective operation of biodiesel blends in an automotive engine," Energy, Elsevier, vol. 239(PA).

    More about this item

    Keywords

    Engine; Modeling; Neural; Wear;
    All these 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:114:y:2016:i:c:p:603-612. 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.