IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v305y2024ics0360544224019595.html
   My bibliography  Save this article

Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: A combined application of artificial neural network and response surface methodology

Author

Listed:
  • Seetharaman, Sathyanarayanan
  • Suresh, S.
  • Shivaranjani, R.S.
  • Dhamodaran, Gopinath
  • JS, Femilda Josephin
  • Ali Alharbi, Sulaiman
  • Pugazhendhi, Arivalagan
  • Varuvel, Edwin Geo

Abstract

This research study mainly focuses on identifying the significant factors to be considered to discover the accuracy and reliability of the predictive models. The experimental results were employed to develop three different models: an artificial neural network (ANN), a response surface methodology (RSM), and a hybrid model. Brake thermal efficiency, specific fuel consumption, and regulated emissions were predicted using ANN, and inputs such as fuel blend concentration, CR, and engine speed were optimized using the RSM and hybrid models. The accuracy and reliability of the model results were validated with the least mean square error, mean absolute percentage error, and a higher signal-to-noise ratio. The higher R2 between 0.99426 and 0.9998 was observed by ANN whereas R2 by RSM and the hybrid model were relatively less. Similarly, the mean square error of ANN was relatively less compared to RSM and hybrid. However, the mean absolute percentage error observed in the validation test results for the optimized input parameters discovered by RSM, was less than 5 % for all the responses and higher in the hybrid model. Thus, the authors concluded that the ANN's predictive ability was much higher and RSM is the best suited for optimizing the engine parameters compared to the hybrid model.

Suggested Citation

  • Seetharaman, Sathyanarayanan & Suresh, S. & Shivaranjani, R.S. & Dhamodaran, Gopinath & JS, Femilda Josephin & Ali Alharbi, Sulaiman & Pugazhendhi, Arivalagan & Varuvel, Edwin Geo, 2024. "Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: A combined application of artificial neural network and response surface methodology," Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:energy:v:305:y:2024:i:c:s0360544224019595
    DOI: 10.1016/j.energy.2024.132185
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224019595
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.132185?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. Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
    2. Awad, Omar I. & Mamat, R. & Ibrahim, Thamir K. & Hammid, Ali Thaeer & Yusri, I.M. & Hamidi, Mohd Adnin & Humada, Ali M. & Yusop, A.F., 2018. "Overview of the oxygenated fuels in spark ignition engine: Environmental and performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 394-408.
    3. Varuvel, Edwin Geo & Seetharaman, Sathyanarayanan & Joseph Shobana Bai, Femilda Josephin & Devarajan, Yuvarajan & Balasubramanian, Dhinesh, 2023. "Development of artificial neural network and response surface methodology model to optimize the engine parameters of rubber seed oil – Hydrogen on PCCI operation," Energy, Elsevier, vol. 283(C).
    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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Han, Dandan & E, Jiaqiang & Deng, Yuanwang & Chen, Jingwei & Leng, Erwei & Liao, Gaoliang & Zhao, Xiaohuan & Feng, Changling & Zhang, Feng, 2021. "A review of studies using hydrocarbon adsorption material for reducing hydrocarbon emissions from cold start of gasoline engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    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. Awad, Omar I. & Mamat, R. & Ibrahim, Thamir K. & Hammid, Ali Thaeer & Yusri, I.M. & Hamidi, Mohd Adnin & Humada, Ali M. & Yusop, A.F., 2018. "Overview of the oxygenated fuels in spark ignition engine: Environmental and performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 394-408.
    8. Thakur, Amit Kumar & Kaviti, Ajay Kumar & Mehra, Roopesh & Mer, K.K.S., 2017. "Progress in performance analysis of ethanol-gasoline blends on SI engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 324-340.
    9. 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.
    10. Thangavelu, Saravana Kannan & Ahmed, Abu Saleh & Ani, Farid Nasir, 2016. "Review on bioethanol as alternative fuel for spark ignition engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 820-835.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Awad, Omar I. & Mamat, R. & Ali, Obed M. & Sidik, N.A.C. & Yusaf, T. & Kadirgama, K. & Kettner, Maurice, 2018. "Alcohol and ether as alternative fuels in spark ignition engine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2586-2605.
    16. Masum, B.M. & Masjuki, H.H. & Kalam, M.A. & Rizwanul Fattah, I.M. & Palash, S.M. & Abedin, M.J., 2013. "Effect of ethanol–gasoline blend on NOx emission in SI engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 209-222.
    17. Liu, Jinlong & Huang, Qiao & Ulishney, Christopher & Dumitrescu, Cosmin E., 2021. "Machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine," Applied Energy, Elsevier, vol. 300(C).
    18. 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.
    19. Ozsezen, Ahmet Necati & Canakci, Mustafa, 2011. "Performance and combustion characteristics of alcohol–gasoline blends at wide-open throttle," Energy, Elsevier, vol. 36(5), pages 2747-2752.
    20. Marietta Markiewicz & Łukasz Muślewski, 2019. "The Impact of Powering an Engine with Fuels from Renewable Energy Sources including its Software Modification on a Drive Unit Performance Parameters," Sustainability, MDPI, vol. 11(23), pages 1-16, November.

    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:305:y:2024:i:c:s0360544224019595. 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.