Fuzzy Neural Network Applications in Biomass Gasification and Pyrolysis for Biofuel Production: A Review DOI Creative Commons
V V Bukhtoyarov, В С Тынченко, K A Bashmur

et al.

Energies, Journal Year: 2024, Volume and Issue: 18(1), P. 16 - 16

Published: Dec. 24, 2024

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 biofuel production. While these effective, they often influenced by complex, nonlinear, uncertain factors, making optimization prediction challenging. This study highlights the application of fuzzy neural networks (FNNs)—a hybrid approach that integrates strengths logic networks—as novel tool address challenges. Unlike traditional methods, FNNs offer enhanced adaptability accuracy modeling nonlinear systems, them uniquely suited biomass processes. review not only ability optimize predict performance but also identifies their role advancing decision-making frameworks. Key challenges, benefits, future research opportunities explored, showcasing transformative potential

Language: Английский

Transfer learning-based deep learning models for flood and erosion detection in coastal area of Algeria DOI
Yacine Hasnaoui, Salah Eddine Tachi, Hamza Bouguerra

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 21, 2025

Language: Английский

Citations

0
Fuzzy Neural Network Applications in Biomass Gasification and Pyrolysis for Biofuel Production: A Review DOI Creative Commons
V V Bukhtoyarov, В С Тынченко, K A Bashmur

et al.

Energies, Journal Year: 2024, Volume and Issue: 18(1), P. 16 - 16

Published: Dec. 24, 2024

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 biofuel production. While these effective, they often influenced by complex, nonlinear, uncertain factors, making optimization prediction challenging. This study highlights the application of fuzzy neural networks (FNNs)—a hybrid approach that integrates strengths logic networks—as novel tool address challenges. Unlike traditional methods, FNNs offer enhanced adaptability accuracy modeling nonlinear systems, them uniquely suited biomass processes. review not only ability optimize predict performance but also identifies their role advancing decision-making frameworks. Key challenges, benefits, future research opportunities explored, showcasing transformative potential

Language: Английский

Citations

1