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

Author

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  • 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
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