IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v218y2023ics0960148123012399.html
   My bibliography  Save this article

Dependence of composition-based approaches on hybrid biodiesel fuel properties prediction using artificial neural network and random tree algorithms

Author

Listed:
  • Giwa, Solomon O.
  • Taziwa, Raymond T.
  • Sharifpur, Mohsen

Abstract

Hybrid biodiesel (HB) synthesized via mixing two or more oils showed improved fuel properties and engine performance among other advantages and it is presently receiving attention in the research community. Different fatty acid methyl ester (FAME) composition-based approaches have been developed to improve the prediction of biodiesel fuel properties from the FAME constituents. However, for HB, the effect of these approaches on the fuel properties is lacking in the literature. This paper investigated the performance of the artificial neural network (ANN) and random tree to predict HB fuel properties from three FAME composition-based approaches (long chain saturation factor and degree of unsaturation (LCSF-DU), modified FAME compositions (MFC), and straight chain saturation factor and modified degree of unsaturation (SCSF-DUm)). FAMEs and fuel properties of HB sourced from the literature were used to develop ANN and random tree (RT) models. Data from the FAME composition-based approaches and fuel properties were used as input and output parameters respectively to develop these models. Results showed that the RT outperformed ANN in predicting the fuel properties for all the FAME composition-based approaches as marked by slightly higher R2 (ANN = 0.9921–0.9992 and RT = 0.9999–1.0000) and lower prediction errors (ANN (MAPE = 0.2684–8.0921 and RMSE = 0.0221–1.9732) and RT (MAPE = 0.00001–0.1691 and RMSE = 0.00001–0.0368). The R2 values demonstrated excellent performance of the developed ANN and RT models in predicting the fuel properties using different FAME composition-based approaches. Low prediction errors between the predicted and experimental-derived values of the fuel properties showed near experimental values prediction of these properties. With ANN, the use of the MFC approach was best in modeling the fuel properties whereas the RT modeling was independent of the FAME composition-based approaches. Calorific value and kinematic viscosity were best predicted using the SCSF-DUm approach while oxidative stability and flash point were predicted most using the LCSF-DU approach. Both the FAME composition-based approaches and learning algorithms have been revealed to influence the prediction of HB fuel properties.

Suggested Citation

  • Giwa, Solomon O. & Taziwa, Raymond T. & Sharifpur, Mohsen, 2023. "Dependence of composition-based approaches on hybrid biodiesel fuel properties prediction using artificial neural network and random tree algorithms," Renewable Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:renene:v:218:y:2023:i:c:s0960148123012399
    DOI: 10.1016/j.renene.2023.119324
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123012399
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119324?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. Kumar, Sandeep & Singhal, Mukesh Kumar & Sharma, Mahendra P., 2023. "Analysis of oil mixing for improvement of biodiesel quality with the application of mixture design method," Renewable Energy, Elsevier, vol. 202(C), pages 809-821.
    2. Jahirul, M.I. & Rasul, M.G. & Brown, R.J. & Senadeera, W. & Hosen, M.A. & Haque, R. & Saha, S.C. & Mahlia, T.M.I., 2021. "Investigation of correlation between chemical composition and properties of biodiesel using principal component analysis (PCA) and artificial neural network (ANN)," Renewable Energy, Elsevier, vol. 168(C), pages 632-646.
    3. Luqman Razzaq & Muhammad Mujtaba Abbas & Sajjad Miran & Salman Asghar & Saad Nawaz & Manzoore Elahi M. Soudagar & Nabeel Shaukat & Ibham Veza & Shahid Khalil & Anas Abdelrahman & Muhammad A. Kalam, 2022. "Response Surface Methodology and Artificial Neural Networks-Based Yield Optimization of Biodiesel Sourced from Mixture of Palm and Cotton Seed Oil," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    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. Guo, Jinrui & Li, Fashe & Zhang, Huicong & Duan, Yaozong & Wang, Shuang & Tan, Fangguan & Chen, Yong & Lu, Fengju & Luo, Linglin, 2023. "Effects of fuel components and combustion parameters on the formation mechanism and emission characteristics of aldehydes from biodiesel combustion," Renewable Energy, Elsevier, vol. 219(P1).
    2. Zeeshan, Muhammad & Ghazanfar, Sadia & Tariq, Muhammad & Asif, Hafiz Muhammad & Hussain, Ajaz & Usman, Muhamamd & Khan, Muhammad Ali & Mahmood, Khalid & Sirajuddin, Muhammad & Imran, Muhammad, 2023. "Synthesis of novel ternary NiO–CdO-Nd2O3 nanocomposite for biodiesel production," Renewable Energy, Elsevier, vol. 210(C), pages 800-809.
    3. Oliveira, Augusto Cesar Laviola de & Renato, Natalia dos Santos & Martins, Marcio Arêdes & Mendonça, Isabela Miranda de & Moraes, Camile Arêdes & Lago, Lucas Fernandes Rocha, 2023. "Renewable energy solutions based on artificial intelligence for farms in the state of Minas Gerais, Brazil: Analysis and proposition," Renewable Energy, Elsevier, vol. 204(C), pages 24-38.
    4. Suvarna, Manu & Jahirul, Mohammad Islam & Aaron-Yeap, Wai Hung & Augustine, Cheryl Valencia & Umesh, Anushri & Rasul, Mohammad Golam & Günay, Mehmet Erdem & Yildirim, Ramazan & Janaun, Jidon, 2022. "Predicting biodiesel properties and its optimal fatty acid profile via explainable machine learning," Renewable Energy, Elsevier, vol. 189(C), pages 245-258.
    5. Impha Yalagudige Dharmegowda & Lakshmidevamma Madarakallu Muniyappa & Parameshwara Siddalingaiah & Ajith Bintravalli Suresh & Manjunath Patel Gowdru Chandrashekarappa & Chander Prakash, 2022. "MgO Nano-Catalyzed Biodiesel Production from Waste Coconut Oil and Fish Oil Using Response Surface Methodology and Grasshopper Optimization," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    6. Mallesh B. Sanjeevannavar & Nagaraj R. Banapurmath & V. Dananjaya Kumar & Ashok M. Sajjan & Irfan Anjum Badruddin & Chandramouli Vadlamudi & Sanjay Krishnappa & Sarfaraz Kamangar & Rahmath Ulla Baig &, 2023. "Machine Learning Prediction and Optimization of Performance and Emissions Characteristics of IC Engine," Sustainability, MDPI, vol. 15(18), pages 1-30, September.
    7. Chen, Zhiwen & Zhao, Ming & Lv, Yi & Wang, Iwei & Tariq, Ghulam & Zhao, Sheng & Ahmed, Shakil & Dong, Weiguo & Ji, Guozhao, 2024. "Higher heating value prediction of high ash gasification-residues: Comparison of white, grey, and black box models," Energy, Elsevier, vol. 288(C).
    8. P. A. Harari & N. R. Banapurmath & V. S. Yaliwal & T. M. Yunus Khan & Irfan Anjum Badruddin & Sarfaraz Kamangar & Teuku Meurah Indra Mahlia, 2021. "Effect of Injection Timing and Injection Duration of Manifold Injected Fuels in Reactivity Controlled Compression Ignition Engine Operated with Renewable Fuels," Energies, MDPI, vol. 14(15), pages 1-19, July.
    9. Cuiling Li & Xiu Wang & Liping Chen & Xueguan Zhao & Yang Li & Mingzhou Chen & Haowei Liu & Changyuan Zhai, 2023. "Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns," Agriculture, MDPI, vol. 13(9), pages 1-26, August.
    10. Moradkhani, Mohammad Amin & Hosseini, Seyyed Hossein & Song, Mengjie & Teimoori, Khalil, 2024. "Comprehensive data-driven methods for estimating the thermal conductivity of biodiesels and their blends with alcohols and fossil diesels," Renewable Energy, Elsevier, vol. 221(C).
    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. Ratna Dewi Kusumaningtyas & Normaliza Normaliza & Elva Dianis Novia Anisa & Haniif Prasetiawan & Dhoni Hartanto & Harumi Veny & Fazlena Hamzah & Miradatul Najwa Muhd Rodhi, 2022. "Synthesis of Biodiesel via Interesterification Reaction of Calophyllum inophyllum Seed Oil and Ethyl Acetate over Lipase Catalyst: Experimental and Surface Response Methodology Analysis," Energies, MDPI, vol. 15(20), pages 1-14, October.
    13. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    14. Murugapoopathi, S. & Surendarnath, S. & Ramachandran, T. & Amesho, Kassian T.T. & Senthil, S., 2023. "Energy and exergy analysis of VCR engine fueled with rubber-seed oil methyl ester using response surface methodology," Energy, Elsevier, vol. 280(C).

    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:renene:v:218:y:2023:i:c:s0960148123012399. 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/renewable-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.