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A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches.
Sakheta, Aban; Nayak, Richi; O'Hara, Ian; Ramirez, Jerome.
Affiliation
  • Sakheta A; Centre for Agriculture and the Bioeconomy, Faculty of Science, Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia; School of Mechanical, Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology, 2 George St, Brisbane City,
  • Nayak R; School of Computer Science, Faculty of Science, Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia; Centre for Data Science, Queensland University of Technology, 2 George Street, Brisbane, 4000, QLD, Australia.
  • O'Hara I; Centre for Agriculture and the Bioeconomy, Faculty of Science, Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia; School of Mechanical, Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology, 2 George St, Brisbane City,
  • Ramirez J; Centre for Agriculture and the Bioeconomy, Faculty of Science, Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia; School of Mechanical, Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology, 2 George St, Brisbane City,
Bioresour Technol ; 386: 129490, 2023 Oct.
Article in En | MEDLINE | ID: mdl-37460019
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 industrial-scale 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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Biofuels Language: En Journal: Bioresour Technol Journal subject: ENGENHARIA BIOMEDICA Year: 2023 Document type: Article Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Biofuels Language: En Journal: Bioresour Technol Journal subject: ENGENHARIA BIOMEDICA Year: 2023 Document type: Article Country of publication: United kingdom