IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v333y2025ics0360544225031111.html

Sustainable diesel engine performance enhancement using pyrolytic plastic oil blends: experimental investigation and artificial neural network-based prediction

Author

Listed:
  • Dutta, Sarajit
  • Khan, Muhammad Neamat Ullah
  • Hoque, Md Emdadul
  • Jin, Yingai
  • Alam, Firoz
  • Trinuruk, Piyatida

Abstract

This paper presents the use of pyrolytic plastic oil (PPO) blends in single-cylinder, water-cooled diesel engines, analyzing their performance across various loading conditions. PPO was produced via pyrolysis of waste plastics, with HDPE being the dominant material. Five different fuel blends were created by varying volumetric ratios, and their physical properties were analyzed, showing similarities with pure diesel. Chemical characterizations through FTIR and UV–Vis spectroscopy confirmed the presence of aromatics, conjugated bonds and oxygenated compounds in PPO blends, influencing engine performance. Results demonstrated improved brake thermal efficiency (BTE) with higher blend ratios, though knocking tendency increased at peak loads. The PPO40 blend demonstrated optimal performance across all conditions. An artificial neural network (ANN) model was developed using experimental data, with engine load and blend ratios as input features and output parameters including brake specific fuel consumption (BSFC), BTE, exhaust gas temperature (EGT), and brake mean effective pressure (BMEP). The model demonstrated excellent accuracy in predicting engine performance, with statistical consistency validated against experimental results. The model's ability to predict outcomes for an untested intermediate blend (PPO70) across load conditions highlights its strength as a predictive tool. Additionally, factors affecting performance, such as concentration of chemical functional groups, were discussed in comparison with pure diesel. This research validates the potential of PPO as an alternative fuel for CI engines, offering a sustainable solution to reduce reliance on fossil fuels.

Suggested Citation

  • Dutta, Sarajit & Khan, Muhammad Neamat Ullah & Hoque, Md Emdadul & Jin, Yingai & Alam, Firoz & Trinuruk, Piyatida, 2025. "Sustainable diesel engine performance enhancement using pyrolytic plastic oil blends: experimental investigation and artificial neural network-based prediction," Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:energy:v:333:y:2025:i:c:s0360544225031111
    DOI: 10.1016/j.energy.2025.137469
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225031111
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.137469?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

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Belay, Nega Chanie & Fentaw, Birhanu Adisie & Nallamothu, Ramesh Babu & Kebede, Melese Shiferaw, 2025. "Performance evaluation of liquid fuel derived from waste plastics for diesel engine applications and emissions characteristics," Energy, Elsevier, vol. 318(C).
    5. 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).
    6. 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.
    7. Devaraj, J. & Robinson, Y. & Ganapathi, P., 2015. "Experimental investigation of performance, emission and combustion characteristics of waste plastic pyrolysis oil blended with diethyl ether used as fuel for diesel engine," Energy, Elsevier, vol. 85(C), pages 304-309.
    8. 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.
    9. 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.
    10. Mani, M. & Nagarajan, G., 2009. "Influence of injection timing on performance, emission and combustion characteristics of a DI diesel engine running on waste plastic oil," Energy, Elsevier, vol. 34(10), pages 1617-1623.
    11. Simsek, Suleyman & Uslu, Samet & Simsek, Hatice, 2022. "Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine," Energy, Elsevier, vol. 239(PD).
    12. Mani, M. & Nagarajan, G. & Sampath, S., 2011. "Characterisation and effect of using waste plastic oil and diesel fuel blends in compression ignition engine," Energy, Elsevier, vol. 36(1), pages 212-219.
    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. 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.
    2. 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).
    3. 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).
    4. 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).
    5. Simsek, Suleyman & Uslu, Samet & Simsek, Hatice, 2022. "Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine," Energy, Elsevier, vol. 239(PD).
    6. Das, Amar Kumar & Hansdah, Dulari & Panda, Achyut Kumar, 2021. "Thermal balancing and exergetic performance evaluation of a compression ignition engine fuelled with waste plastic pyrolytic oil and different fuel additives," Energy, Elsevier, vol. 229(C).
    7. Das, Amar Kumar & Sahu, Santosh Kumar & Panda, Achyut Kumar, 2022. "Current status and prospects of alternate liquid transportation fuels in compression ignition engines: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    8. 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.
    9. 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.
    10. 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).
    11. Haruki Tajima & Takuya Tomidokoro & Takeshi Yokomori, 2022. "Deep Learning for Knock Occurrence Prediction in SI Engines," Energies, MDPI, vol. 15(24), pages 1-14, December.
    12. Gad, M.S. & Panchal, Hitesh & Ağbulut, Ümit, 2022. "Waste to Energy: An experimental comparison of burning the waste-derived bio-oils produced by transesterification and pyrolysis methods," Energy, Elsevier, vol. 242(C).
    13. 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.
    14. Aliakbari, Karim & Ebrahimi-Moghadam, Amir & Pahlavanzadeh, Mohammadsadegh & Moradi, Reza, 2023. "Performance characteristics and exhaust emissions of a single-cylinder diesel engine for different fuels: Experimental investigation and artificial intelligence network," Energy, Elsevier, vol. 284(C).
    15. Othman, Mohd Fahmi & Adam, Abdullah & Najafi, G. & Mamat, Rizalman, 2017. "Green fuel as alternative fuel for diesel engine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 694-709.
    16. 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.
    17. 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.
    18. Bhowmik, Mrinal & Muthukumar, P. & Anandalakshmi, R., 2019. "Experimental based multilayer perceptron approach for prediction of evacuated solar collector performance in humid subtropical regions," Renewable Energy, Elsevier, vol. 143(C), pages 1566-1580.
    19. 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.
    20. 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.

    More about this item

    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:333:y:2025:i:c:s0360544225031111. 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.