IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i2p227-d1329959.html
   My bibliography  Save this article

Comparison of Engine Performance and Emission Values of Biodiesel Obtained from Waste Pumpkin Seeds with Machine Learning

Author

Listed:
  • Seda Şahin

    (Department of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Selçuk University, 42140 Konya, Türkiye)

  • Ayşe Torun

    (Horticulture Department, Faculty of Agriculture, Selçuk University, 42140 Konya, Türkiye)

Abstract

This study was primarily conducted to investigate the potential use of pumpkin seed oil in biodiesel production. Initially, the fatty acid composition of oils extracted from discarded pumpkin seeds was determined. Then, biodiesel produced from discarded pumpkin seed oil was tested in an engine test setup. The performance and emission values of a four-cylinder diesel engine fueled with diesel (D100), biodiesel (PB100), and blended fuels (PB2D98, PB5D95, and PB20D80) were determined. Furthermore, three distinctive machine learning algorithms (artificial neural networks, XGBoost, and random forest) were employed to model engine performance and emission parameters. Models were generated based on the data from the PB100, PB2D98, and PB5D95 fuels, and model performance was assessed through the R 2 , RMSE, and MAPE metrics. The highest torque value (333.15 Nm) was obtained from 1200 rpm of D100 fuel. PB2D98 (2% biodiesel–98% diesel) had the lowest specific fuel consumption (194.33 g HPh −1 ) at 1600 rpm. The highest BTE (break thermal efficiency) value (30.92%) was obtained from diesel fuel at 1400 rpm. Regarding the blended fuels, PB2D98 exhibited the most fuel-efficient performance. Overall, in terms of engine performance and emission values, PB2M98 showed the closest results to diesel fuel. A comparison of machine learning algorithms revealed that artificial neural networks (ANNs) generally performed the best. However, the XGBoost algorithm proved to be more successful than other algorithms at predicting the performance and emissions of PB20D80 fuel. The present findings demonstrated that the XGBoost algorithm could be a more reliable option for predicting engine performance and emissions, especially for data-deficient fuels such as PB20D80.

Suggested Citation

  • Seda Şahin & Ayşe Torun, 2024. "Comparison of Engine Performance and Emission Values of Biodiesel Obtained from Waste Pumpkin Seeds with Machine Learning," Agriculture, MDPI, vol. 14(2), pages 1-21, January.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:2:p:227-:d:1329959
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/2/227/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/2/227/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Paul, Abhishek & Panua, Rajsekhar & Debroy, Durbadal, 2017. "An experimental study of combustion, performance, exergy and emission characteristics of a CI engine fueled by Diesel-ethanol-biodiesel blends," Energy, Elsevier, vol. 141(C), pages 839-852.
    2. Zhang, Zhi-Hui & Balasubramanian, Rajasekhar, 2016. "Investigation of particulate emission characteristics of a diesel engine fueled with higher alcohols/biodiesel blends," Applied Energy, Elsevier, vol. 163(C), pages 71-80.
    3. N. Magesh & T. Pushparaj & V. Vinoth Kannan & Deepak Thakur & Abhishek Sharma & Abdul Razak & Abdulrajak Buradi & Abiot Ketema & Hasan Koten, 2022. "Experimental Investigation and Prediction of Performance, Combustion, and Emission Features of a Diesel Engine Fuelled with Pumpkin-Maize Biodiesel using Different Machine Learning Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-17, October.
    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. Ghadikolaei, Meisam Ahmadi & Wong, Pak Kin & Cheung, Chun Shun & Ning, Zhi & Yung, Ka-Fu & Zhao, Jing & Gali, Nirmal Kumar & Berenjestanaki, Alireza Valipour, 2021. "Impact of lower and higher alcohols on the physicochemical properties of particulate matter from diesel engines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    2. Reyes García-Contreras & Andrés Agudelo & Arántzazu Gómez & Pablo Fernández-Yáñez & Octavio Armas & Ángel Ramos, 2019. "Thermoelectric Energy Recovery in a Light-Duty Diesel Vehicle under Real-World Driving Conditions at Different Altitudes with Diesel, Biodiesel and GTL Fuels," Energies, MDPI, vol. 12(6), pages 1-18, March.
    3. Zhang, Wei & Chang, Shaoyue & Wu, Wei & Dong, Lihui & Chen, Zhaohui & Chen, Guisheng, 2019. "A diesel/natural gas dual fuel mechanism constructed to reveal combustion and emission characteristics," Energy, Elsevier, vol. 179(C), pages 59-75.
    4. Kim, Keunsoo & Kim, Junghwan & Oh, Seungmook & Kim, Changup & Lee, Yonggyu, 2017. "Evaluation of injection and ignition schemes for the ultra-lean combustion direct-injection LPG engine to control particulate emissions," Applied Energy, Elsevier, vol. 194(C), pages 123-135.
    5. Bahman Najafi & Sina Faizollahzadeh Ardabili & Amir Mosavi & Shahaboddin Shamshirband & Timon Rabczuk, 2018. "An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and," Energies, MDPI, vol. 11(4), pages 1-18, April.
    6. EL-Seesy, Ahmed I. & Hassan, Hamdy, 2019. "Investigation of the effect of adding graphene oxide, graphene nanoplatelet, and multiwalled carbon nanotube additives with n-butanol-Jatropha methyl ester on a diesel engine performance," Renewable Energy, Elsevier, vol. 132(C), pages 558-574.
    7. Paparao, Jami & Soundarya, N. & Murugan, S., 2023. "Advancing green technology: Experimental study on low heat rejection engine utilizing bio-based antioxidant-doped biodiesel-diesel blends and oxy-hydrogen gas," Energy, Elsevier, vol. 283(C).
    8. Mendiburu, Andrés Z. & Lauermann, Carlos H. & Hayashi, Thamy C. & Mariños, Diego J. & Rodrigues da Costa, Roberto Berlini & Coronado, Christian J.R. & Roberts, Justo J. & de Carvalho, João A., 2022. "Ethanol as a renewable biofuel: Combustion characteristics and application in engines," Energy, Elsevier, vol. 257(C).
    9. Zhang, Qiankun & Xia, Jin & Wang, Jianping & He, Zhuoyao & Zhao, Wenbin & Qian, Yong & Zheng, Liang & Liu, Rui & Lu, Xingcai, 2022. "Experimental study on ignition and combustion characteristics of biodiesel-butanol blends at different injection pressures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    10. Roy, Murari Mohon & Calder, Jorge & Wang, Wilson & Mangad, Arvind & Diniz, Fernando Cezar Mariano, 2016. "Cold start idle emissions from a modern Tier-4 turbo-charged diesel engine fueled with diesel-biodiesel, diesel-biodiesel-ethanol, and diesel-biodiesel-diethyl ether blends," Applied Energy, Elsevier, vol. 180(C), pages 52-65.
    11. Zuo, Lei & Wang, Junfeng & Mei, Deqing & Dai, Shengchao & Adu-Mensah, Derick, 2022. "Experimental investigation on combustion and (regulated and unregulated) emissions performance of a common-rail diesel engine using partially hydrogenated biodiesel-ethanol-diesel ternary blend," Renewable Energy, Elsevier, vol. 185(C), pages 1272-1283.
    12. Chao Jin & Xiaodan Li & Teng Xu & Juntong Dong & Zhenlong Geng & Jia Liu & Chenyun Ding & Jingjing Hu & Ahmed El ALAOUI & Qing Zhao & Haifeng Liu, 2023. "Zero-Carbon and Carbon-Neutral Fuels: A Review of Combustion Products and Cytotoxicity," Energies, MDPI, vol. 16(18), pages 1-29, September.
    13. Guirong Wu & Jun Cong Ge & Nag Jung Choi, 2021. "Effect of Ethanol Additives on Combustion and Emissions of a Diesel Engine Fueled by Palm Oil Biodiesel at Idling Speed," Energies, MDPI, vol. 14(5), pages 1-12, March.
    14. Wei, L. & Cheung, C.S. & Ning, Z., 2018. "Effects of biodiesel-ethanol and biodiesel-butanol blends on the combustion, performance and emissions of a diesel engine," Energy, Elsevier, vol. 155(C), pages 957-970.
    15. Navaneetha Krishnan Balakrishnan & Yew Heng Teoh & Heoy Geok How & Thanh Danh Le & Huu Tho Nguyen, 2023. "An Experimental Investigation on the Characteristics of a Compression Ignition Engine Fuelled by Diesel-Palm Biodiesel–Ethanol/Propanol Based Ternary Blends," Energies, MDPI, vol. 16(2), pages 1-18, January.
    16. Srikanth, H.V. & G, Sharanappa & Manne, Bhaskar & Kumar, S. Bharath, 2021. "Niger seed oil biodiesel as an emulsifier in diesel-ethanol blends for compression ignition engine," Renewable Energy, Elsevier, vol. 163(C), pages 1467-1478.
    17. Ma, Yinjie & Huang, Sheng & Huang, Ronghua & Zhang, Yu & Xu, Shijie, 2017. "Ignition and combustion characteristics of n-pentanol–diesel blends in a constant volume chamber," Applied Energy, Elsevier, vol. 185(P1), pages 519-530.
    18. Salih Özer, 2025. "The Role of Cheap Chemicals Containing Oxygen Used as Diesel Fuel Additives in Reducing Carbon Footprints," Sustainability, MDPI, vol. 17(7), pages 1-26, April.
    19. Patel, Alok & Pruthi, Vikas & Pruthi, Parul A., 2019. "Innovative screening approach for the identification of triacylglycerol accumulating oleaginous strains," Renewable Energy, Elsevier, vol. 135(C), pages 936-944.
    20. Krishnan, M. Gowthama & Rajkumar, Sundararajan, 2022. "Effects of dual fuel combustion on performance, emission and energy-exergy characteristics of diesel engine fuelled with diesel-isobutanol and biodiesel-isobutanol," Energy, Elsevier, vol. 252(C).

    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:gam:jagris:v:14:y:2024:i:2:p:227-:d:1329959. 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.