IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2024i1p16-d1551544.html
   My bibliography  Save this article

Fuzzy Neural Network Applications in Biomass Gasification and Pyrolysis for Biofuel Production: A Review

Author

Listed:
  • Vladimir Bukhtoyarov

    (Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia
    Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

  • Vadim Tynchenko

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

  • Kirill Bashmur

    (Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)

  • Oleg Kolenchukov

    (Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)

  • Vladislav Kukartsev

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

  • Ivan Malashin

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

Abstract

The increasing demand for sustainable energy has spurred interest in biofuels as a renewable alternative to fossil fuels. Biomass gasification and pyrolysis are two prominent thermochemical conversion processes for biofuel production. While these processes are effective, they are often influenced by complex, nonlinear, and uncertain factors, making optimization and prediction challenging. This study highlights the application of fuzzy neural networks (FNNs)—a hybrid approach that integrates the strengths of fuzzy logic and neural networks—as a novel tool to address these challenges. Unlike traditional optimization methods, FNNs offer enhanced adaptability and accuracy in modeling nonlinear systems, making them uniquely suited for biomass conversion processes. This review not only highlights the ability of FNNs to optimize and predict the performance of gasification and pyrolysis processes but also identifies their role in advancing decision-making frameworks. Key challenges, benefits, and future research opportunities are also explored, showcasing the transformative potential of FNNs in biofuel production.

Suggested Citation

  • Vladimir Bukhtoyarov & Vadim Tynchenko & Kirill Bashmur & Oleg Kolenchukov & Vladislav Kukartsev & Ivan Malashin, 2024. "Fuzzy Neural Network Applications in Biomass Gasification and Pyrolysis for Biofuel Production: A Review," Energies, MDPI, vol. 18(1), pages 1-25, December.
    • Handle: RePEc:gam:jeners:v:18:y:2024:i:1:p:16-:d:1551544
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/1/16/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/1/16/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yanchen Liu & Minfang Peng, 2024. "Deep Learning Algorithm for Solving Interval of Weight Coefficient of Wind–Thermal–Storage System," Energies, MDPI, vol. 17(5), pages 1-18, February.
    2. Milano, Jassinnee & Ong, Hwai Chyuan & Masjuki, H.H. & Chong, W.T. & Lam, Man Kee & Loh, Ping Kwan & Vellayan, Viknes, 2016. "Microalgae biofuels as an alternative to fossil fuel for power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 180-197.
    3. Victor Arruda Ferraz de Campos & Luís Carmo-Calado & Roberta Mota-Panizio & Vitor Matos & Valter Bruno Silva & Paulo S. Brito & Daniela F. L. Eusébio & Celso Eduardo Tuna & José Luz Silveira, 2023. "A Waste-to-Energy Technical Approach: Syngas–Biodiesel Blend for Power Generation," Energies, MDPI, vol. 16(21), pages 1-18, October.
    4. Lerkkasemsan, Nuttapol & Achenie, Luke E.K., 2014. "Pyrolysis of biomass – fuzzy modeling," Renewable Energy, Elsevier, vol. 66(C), pages 747-758.
    5. Lerkkasemsan, Nuttapol, 2017. "Fuzzy logic-based predictive model for biomass pyrolysis," Applied Energy, Elsevier, vol. 185(P2), pages 1019-1030.
    6. Aigbe, Uyiosa Osagie & Ukhurebor, Kingsley Eghonghon & Osibote, Adelaja Otolorin & Hassaan, Mohamed A. & El Nemr, Ahmed, 2024. "Optimization and prediction of biogas yield from pretreated Ulva Intestinalis Linnaeus applying statistical-based regression approach and machine learning algorithms," Renewable Energy, Elsevier, vol. 235(C).
    7. Akter, Mst. Mahmoda & Surovy, Israt Zahan & Sultana, Nazmin & Faruk, Md. Omar & Gilroyed, Brandon H. & Tijing, Leonard & Arman, & Didar-ul-Alam, Md. & Shon, Ho Kyong & Nam, Sang Yong & Kabir, Mohammad, 2024. "Techno-economics and environmental sustainability of agricultural biomass-based energy potential," Applied Energy, Elsevier, vol. 359(C).
    8. Badreddine Babes & Noureddine Hamouda & Fahad Albalawi & Oualid Aissa & Sherif S. M. Ghoneim & Saad A. Mohamed Abdelwahab, 2022. "Experimental Investigation of an Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller for Buck DC/DC Converters," Sustainability, MDPI, vol. 14(13), pages 1-23, June.
    9. Gollakota, Anjani R.K. & Shu, Chi-Min & Sarangi, Prakash Kumar & Shadangi, Krushna Prasad & Rakshit, Sudip & Kennedy, John F. & Gupta, Vijai Kumar & Sharma, Minaxi, 2023. "Catalytic hydrodeoxygenation of bio-oil and model compounds - Choice of catalysts, and mechanisms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    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. de Jesus, Sérgio S. & Ferreira, Gabriela F. & Moreira, Larissa S. & Filho, Rubens Maciel, 2020. "Biodiesel production from microalgae by direct transesterification using green solvents," Renewable Energy, Elsevier, vol. 160(C), pages 1283-1294.
    2. Elhambakhsh, Abbas & Van Duc Long, Nguyen & Lamichhane, Pradeep & Hessel, Volker, 2023. "Recent progress and future directions in plasma-assisted biomass conversion to hydrogen," Renewable Energy, Elsevier, vol. 218(C).
    3. 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.
    4. Yoon, Kwangsuk & Lee, Sang Soo & Ok, Yong Sik & Kwon, Eilhann E. & Song, Hocheol, 2019. "Enhancement of syngas for H2 production via catalytic pyrolysis of orange peel using CO2 and bauxite residue," Applied Energy, Elsevier, vol. 254(C).
    5. Kim, Jung-Hun & Oh, Jeong-Ik & Tsang, Yiu Fai & Park, Young-Kwon & Lee, Jechan & Kwon, Eilhann E., 2020. "CO2-assisted catalytic pyrolysis of digestate with steel slag," Energy, Elsevier, vol. 191(C).
    6. Giuliana Vinci & Laura Gobbi & Daniela Porcaro & Sara Pinzi & Miguel Carmona-Cabello & Marco Ruggeri, 2024. "Environmental Evaluation of Chemical Plastic Waste Recycling: A Life Cycle Assessment Approach," Resources, MDPI, vol. 13(12), pages 1-16, December.
    7. Shashi Kumar & Apurv Yadav, 2018. "Comparative experimental investigation of preheated thumba oil for its performance testing on a CI engine," Energy & Environment, , vol. 29(4), pages 533-542, June.
    8. Kasmuri, N.H. & Kamarudin, S.K. & Abdullah, S.R.S. & Hasan, H.A. & Som, A.Md., 2017. "Process system engineering aspect of bio-alcohol fuel production from biomass via pyrolysis: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 914-923.
    9. Naveed, Muhammad Hamza & Khan, Muhammad Nouman Aslam & Mukarram, Muhammad & Naqvi, Salman Raza & Abdullah, Abdullah & Haq, Zeeshan Ul & Ullah, Hafeez & Mohamadi, Hamad Al, 2024. "Cellulosic biomass fermentation for biofuel production: Review of artificial intelligence approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    10. Teng, Sin Yong & Loy, Adrian Chun Minh & Leong, Wei Dong & How, Bing Shen & Chin, Bridgid Lai Fui & Máša, Vítězslav, 2019. "Catalytic thermal degradation of Chlorella Vulgaris: Evolving deep neural networks for optimization," MPRA Paper 95772, University Library of Munich, Germany.
    11. He, Zhixia & Wang, Bin & Zhang, Bo & Feng, Huan & Kandasamy, Sabariswaran & Chen, Haitao, 2020. "Synergistic effect of hydrothermal Co-liquefaction of Spirulina platensis and Lignin: Optimization of operating parameters by response surface methodology," Energy, Elsevier, vol. 201(C).
    12. Leong, Wai-Hong & Lim, Jun-Wei & Lam, Man-Kee & Uemura, Yoshimitsu & Ho, Yeek-Chia, 2018. "Third generation biofuels: A nutritional perspective in enhancing microbial lipid production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 950-961.
    13. Sedlar, D. Karasalihović & Vulin, D. & Krajačić, G. & Jukić, L., 2019. "Offshore gas production infrastructure reutilisation for blue energy production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 159-174.
    14. Zanganeh, Peyman & Goharrizi, Ataallah Soltani & Ayatollahi, Shahab & Feilizadeh, Mehrzad & Dashti, Hossein, 2020. "Efficiency improvement of solar stills through wettability alteration of the condensation surface: An experimental study," Applied Energy, Elsevier, vol. 268(C).
    15. Yellapu, Sravan Kumar & Klai, Nouha & Kaur, Rajwinder & Tyagi, Rajeshwar D. & Surampalli, Rao Y., 2019. "Oleaginous yeast biomass flocculation using bioflocculant produced in wastewater sludge and transesterification using petroleum diesel as a co-solvent," Renewable Energy, Elsevier, vol. 131(C), pages 217-228.
    16. Pomeroy, Brett & Grilc, Miha & Likozar, Blaž, 2022. "Artificial neural networks for bio-based chemical production or biorefining: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    17. Choi, Dongho & Kim, Minyoung & Kim, Seungwon & Lee, Doyeon & Tsang, Yiu Fai & Park, Won-Kun & Kwon, Eilhann E., 2024. "Fabrication of red mud-carbon composite from extremophilic microalgae and its utilisation in biodiesel production," Applied Energy, Elsevier, vol. 372(C).
    18. Peters, Jens F. & Banks, Scott W. & Bridgwater, Anthony V. & Dufour, Javier, 2017. "A kinetic reaction model for biomass pyrolysis processes in Aspen Plus," Applied Energy, Elsevier, vol. 188(C), pages 595-603.
    19. Vassilis Stavrakas & Niki-Artemis Spyridaki & Alexandros Flamos, 2018. "Striving towards the Deployment of Bio-Energy with Carbon Capture and Storage (BECCS): A Review of Research Priorities and Assessment Needs," Sustainability, MDPI, vol. 10(7), pages 1-27, June.
    20. Stamenković, Olivera S. & Siliveru, Kaliramesh & Veljković, Vlada B. & Banković-Ilić, Ivana B. & Tasić, Marija B. & Ciampitti, Ignacio A. & Đalović, Ivica G. & Mitrović, Petar M. & Sikora, Vladimir Š., 2020. "Production of biofuels from sorghum," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(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:gam:jeners:v:18:y:2024:i:1:p:16-:d:1551544. 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.