Applications of machine learning in thermochemical conversion of biomass-A review
Thermochemical conversion of biomass has been considered a promising technique to
produce alternative renewable fuel sources for future energy supply. However, these …
produce alternative renewable fuel sources for future energy supply. However, these …
Machine learning methods for modelling the gasification and pyrolysis of biomass and waste
S Ascher, I Watson, S You - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
Over the past two decades, the use of machine learning (ML) methods to model biomass
and waste gasification/pyrolysis has increased rapidly. Only 70 papers were published in …
and waste gasification/pyrolysis has increased rapidly. Only 70 papers were published in …
Evolution of porosity in kerogen type I during hydrous and anhydrous pyrolysis: Experimental study, mechanistic understanding, and model development
B Liu, MR Mohammadi, Z Ma, L Bai, L Wang, Y Xu… - Fuel, 2023 - Elsevier
In this study, to clarify the role of water during thermal maturation on organic porosity
evolution, pore structure characteristics of the Qingshankou shale (type I kerogen) were …
evolution, pore structure characteristics of the Qingshankou shale (type I kerogen) were …
Bioresource upgrade for sustainable energy, environment, and biomedicine
We conceptualize bioresource upgrade for sustainable energy, environment, and
biomedicine with a focus on circular economy, sustainability, and carbon neutrality using …
biomedicine with a focus on circular economy, sustainability, and carbon neutrality using …
Modeling the SOFC by BP neural network algorithm
S Song, X Xiong, X Wu, Z Xue - International Journal of Hydrogen Energy, 2021 - Elsevier
Solid oxide fuel cells (SOFCs) are complex systems in which electrochemistry,
thermophysics and ion conduction occur simultaneously. The coupling of the multi-physics …
thermophysics and ion conduction occur simultaneously. The coupling of the multi-physics …
[HTML][HTML] Interpretable machine learning to model biomass and waste gasification
Abstract Machine learning has been regarded as a promising method to better model
thermochemical processes such as gasification. However, their black box nature can limit …
thermochemical processes such as gasification. However, their black box nature can limit …
Machine learning methods for modeling conventional and hydrothermal gasification of waste biomass: A review
Conventional and hydrothermal gasification are promising thermochemical technologies for
the production of syngas from waste biomass. Both gasification processes are complex, with …
the production of syngas from waste biomass. Both gasification processes are complex, with …
Sustainable energies and machine learning: An organized review of recent applications and challenges
In alignment with the rapid development of artificial intelligence in the era of data
management, the application domains for machine learning have expanded to all …
management, the application domains for machine learning have expanded to all …
[HTML][HTML] A comprehensive artificial neural network model for gasification process prediction
The viability and the relative merits of competing biomass and waste gasification schemes
depends on a complex mix of interacting factors. Conventional analytical methods that are …
depends on a complex mix of interacting factors. Conventional analytical methods that are …
Machine learning prediction of bio-oil characteristics quantitatively relating to biomass compositions and pyrolysis conditions
It is crucial to predict the characteristics of pyrolytic bio-oil accurately for its application, but
the prediction results are greatly influenced by biomass compositions and pyrolysis …
the prediction results are greatly influenced by biomass compositions and pyrolysis …