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

Vladimir Bukhtoyarov, Vadim Tynchenko (), Kirill Bashmur, Oleg Kolenchukov, Vladislav Kukartsev and Ivan Malashin ()
Additional contact information
Vladimir Bukhtoyarov: Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, 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

Energies, 2024, vol. 18, issue 1, 1-25

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.

Keywords: biomass gasification; biomass pyrolysis; biofuels; fuzzy neural networks; renewable energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/1/16/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/1/16/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2024:i:1:p:16-:d:1551544

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().