IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i20p3732-d938959.html
   My bibliography  Save this article

Artificial Neural Network as a Tool for Estimation of the Higher Heating Value of Miscanthus Based on Ultimate Analysis

Author

Listed:
  • Ivan Brandić

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Lato Pezo

    (Institute of General and Physical Chemistry, University of Belgrade, Studentski trg 12/V, 11000 Belgrade, Serbia)

  • Nikola Bilandžija

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Anamarija Peter

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Jona Šurić

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Neven Voća

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

Abstract

Miscanthus is a perennial energy crop that produces high yields and has the potential to be converted into energy. The ultimate analysis determines the composition of the biomass and the energy value in terms of the higher heating value (HHV), which is the most important parameter in determining the quality of the fuel. In this study, an artificial neural network (ANN) model based on the principle of supervised learning was developed to predict the HHV of miscanthus biomass. The developed ANN model was compared with the models of predictive regression models (suggested from the literature) and the accuracy of the developed model was determined by the coefficient of determination. The paper presents data from 192 miscanthus biomass samples based on ultimate analysis and HHV. The developed model showed good properties and the possibility of prediction with high accuracy (R 2 = 0.77). The paper proves the possibility of using ANN models in practical application in determining fuel properties of biomass energy crops and greater accuracy in predicting HHV than the regression models offered in the literature.

Suggested Citation

  • Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2022. "Artificial Neural Network as a Tool for Estimation of the Higher Heating Value of Miscanthus Based on Ultimate Analysis," Mathematics, MDPI, vol. 10(20), pages 1-12, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3732-:d:938959
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/20/3732/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/20/3732/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Noushabadi, Abolfazl Sajadi & Dashti, Amir & Ahmadijokani, Farhad & Hu, Jinguang & Mohammadi, Amir H., 2021. "Estimation of higher heating values (HHVs) of biomass fuels based on ultimate analysis using machine learning techniques and improved equation," Renewable Energy, Elsevier, vol. 179(C), pages 550-562.
    2. Kartal, Furkan & Özveren, Uğur, 2020. "A deep learning approach for prediction of syngas lower heating value from CFB gasifier in Aspen plus®," Energy, Elsevier, vol. 209(C).
    3. Ioannis O. Vardiambasis & Theodoros N. Kapetanakis & Christos D. Nikolopoulos & Trinh Kieu Trang & Toshiki Tsubota & Ramazan Keyikoglu & Alireza Khataee & Dimitrios Kalderis, 2020. "Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values," Energies, MDPI, vol. 13(17), pages 1-20, September.
    4. Koçar, Günnur & Civaş, Nilgün, 2013. "An overview of biofuels from energy crops: Current status and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 900-916.
    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. Justyna Kujawska & Monika Kulisz & Piotr Oleszczuk & Wojciech Cel, 2023. "Improved Prediction of the Higher Heating Value of Biomass Using an Artificial Neural Network Model Based on the Selection of Input Parameters," Energies, MDPI, vol. 16(10), pages 1-16, May.
    2. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    3. Mislav Kontek & Luka Brezinščak & Vanja Jurišić & Ivan Brandić & Alan Antonović & Božidar Matin & Karlo Špelić & Tajana Krička & Ana Matin, 2023. "Mitigating the Energy Crisis: Utilization of Seed Production Wastes for Energy Production in Continental Croatia," Energies, MDPI, vol. 16(2), pages 1-11, January.

    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. Nabavi-Pelesaraei, Ashkan & Azadi, Hossein & Van Passel, Steven & Saber, Zahra & Hosseini-Fashami, Fatemeh & Mostashari-Rad, Fatemeh & Ghasemi-Mobtaker, Hassan, 2021. "Prospects of solar systems in production chain of sunflower oil using cold press method with concentrating energy and life cycle assessment," Energy, Elsevier, vol. 223(C).
    2. Navas-Anguita, Zaira & García-Gusano, Diego & Iribarren, Diego, 2019. "A review of techno-economic data for road transportation fuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 11-26.
    3. Holmatov, B. & Hoekstra, A.Y. & Krol, M.S., 2019. "Land, water and carbon footprints of circular bioenergy production systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 224-235.
    4. Tsolas, Spyridon D. & Karim, M. Nazmul & Hasan, M.M. Faruque, 2018. "Optimization of water-energy nexus: A network representation-based graphical approach," Applied Energy, Elsevier, vol. 224(C), pages 230-250.
    5. Jiao Zhang & Qian Wang & Yiping Xia & Katsunori Furuya, 2022. "Knowledge Map of Spatial Planning and Sustainable Development: A Visual Analysis Using CiteSpace," Land, MDPI, vol. 11(3), pages 1-24, February.
    6. Peter, Christiane & Specka, Xenia & Aurbacher, Joachim & Kornatz, Peter & Herrmann, Christiane & Heiermann, Monika & Müller, Janine & Nendel, Claas, 2017. "The MiLA tool: Modeling greenhouse gas emissions and cumulative energy demand of energy crop cultivation in rotation," Agricultural Systems, Elsevier, vol. 152(C), pages 67-79.
    7. Owen Sedej & Eric Mbonimpa & Trevor Sleight & Jeremy Slagley, 2022. "Application of Machine Learning to Predict the Performance of an EMIPG Reactor Using Data from Numerical Simulations," Energies, MDPI, vol. 15(7), pages 1-22, March.
    8. Eksi, Guner & Karaosmanoglu, Filiz, 2017. "Combined bioheat and biopower: A technology review and an assessment for Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1313-1332.
    9. Šuhaj, Patrik & Husár, Jakub & Haydary, Juma & Annus, Július, 2022. "Experimental verification of a pilot pyrolysis/split product gasification (PSPG) unit," Energy, Elsevier, vol. 244(PA).
    10. Małgorzata Sieradzka & Agata Mlonka-Mędrala & Izabela Kalemba-Rec & Markus Reinmöller & Felix Küster & Wojciech Kalawa & Aneta Magdziarz, 2022. "Evaluation of Physical and Chemical Properties of Residue from Gasification of Biomass Wastes," Energies, MDPI, vol. 15(10), pages 1-19, May.
    11. Xie, Xinyu & Wang, Xiaofang & Zhao, Pu & Hao, Yichen & Xie, Rong & Liu, Haitao, 2023. "Learning time-aware multi-phase flow fields in coal-supercritical water fluidized bed reactor with deep learning," Energy, Elsevier, vol. 263(PD).
    12. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    13. Meenakshi Sharma & Rajesh Kaushal & Prashant Kaushik & Seeram Ramakrishna, 2021. "Carbon Farming: Prospects and Challenges," Sustainability, MDPI, vol. 13(19), pages 1-15, October.
    14. Marcin Jewiarz & Marek Wróbel & Krzysztof Mudryk & Szymon Szufa, 2020. "Impact of the Drying Temperature and Grinding Technique on Biomass Grindability," Energies, MDPI, vol. 13(13), pages 1-22, July.
    15. Condor, Billriz E. & de Luna, Mark Daniel G. & Lacson, Carl Francis Z. & Acebu, Paula Isabel G. & Abarca, Ralf Ruffel M. & Nagarajan, Dillirani & Lee, Duu-Jong & Chang, Jo-Shu, 2024. "Effects of carbon dioxide concentration and swine wastewater on the cultivation of Chlorella vulgaris FSP-E and bioethanol production from microalgae biomass," Applied Energy, Elsevier, vol. 369(C).
    16. Bonassa, Gabriela & Schneider, Lara Talita & Canever, Victor Bruno & Cremonez, Paulo André & Frigo, Elisandro Pires & Dieter, Jonathan & Teleken, Joel Gustavo, 2018. "Scenarios and prospects of solid biofuel use in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2365-2378.
    17. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    18. Singh, N.B. & Kumar, Ashwani & Rai, Sarita, 2014. "Potential production of bioenergy from biomass in an Indian perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 65-78.
    19. Ji, Xi & Long, Xianling, 2016. "A review of the ecological and socioeconomic effects of biofuel and energy policy recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 41-52.
    20. Jambo, Siti Azmah & Abdulla, Rahmath & Mohd Azhar, Siti Hajar & Marbawi, Hartinie & Gansau, Jualang Azlan & Ravindra, Pogaku, 2016. "A review on third generation bioethanol feedstock," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 756-769.

    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:jmathe:v:10:y:2022:i:20:p:3732-:d:938959. 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.