Title |
A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches |
ID_Doc |
74757 |
Authors |
Sakheta, A; Nayak, R; O'Hara, I; Ramirez, J |
Title |
A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches |
Year |
2023 |
Published |
|
DOI |
10.1016/j.biortech.2023.129490 |
Abstract |
Biofuels from lignocellulosic biomass converted via thermochemical technologies can be renewable and sustainable, which makes them promising as alternatives to conventional fossil fuels. Prior to building industrialscale thermochemical conversion plants, computational models are used to simulate process flows and conditions, conduct feasibility studies, and analyse process and business risk. This paper aims to provide an overview of the current state of the art in modelling thermochemical conversion of lignocellulosic biomass. Emphasis is given to the recent advances in artificial intelligence (AI)-based modelling that plays an increasingly important role in enhancing the performance of the models. This review shows that AI-based models offer prominent accuracy compared to thermodynamic equilibrium modelling implemented in some models. It is also evident that gasification and pyrolysis models are more matured than thermal liquefaction for lignocelluloses. Additionally, the knowledge gained and future directions in the applications of simulation and AI in process modelling are explored. |
Author Keywords |
Lignocellulosic biomass; Thermochemical conversion; Artificial intelligence; Process simulation; Modelling |
Index Keywords |
Index Keywords |
Document Type |
Other |
Open Access |
Open Access |
Source |
Science Citation Index Expanded (SCI-EXPANDED) |
EID |
WOS:001045267700001 |
WoS Category |
Agricultural Engineering; Biotechnology & Applied Microbiology; Energy & Fuels |
Research Area |
Agriculture; Biotechnology & Applied Microbiology; Energy & Fuels |
PDF |
https://doi.org/10.1016/j.biortech.2023.129490
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