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Enhancing biomass conversion to bioenergy with machine learning: Gains and problems.
Wang, Rupeng; He, Zixiang; Chen, Honglin; Guo, Silin; Zhang, Shiyu; Wang, Ke; Wang, Meng; Ho, Shih-Hsin.
Afiliación
  • Wang R; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
  • He Z; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
  • Chen H; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
  • Guo S; School of Medicine and Health, Harbin Institute of Technology, Harbin 150040, PR China.
  • Zhang S; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
  • Wang K; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
  • Wang M; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
  • Ho SH; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China. Electronic address: stephen6949@hit.edu.cn.
Sci Total Environ ; 927: 172310, 2024 Jun 01.
Article en En | MEDLINE | ID: mdl-38599406
ABSTRACT
The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biomasa / Biocombustibles / Aprendizaje Automático Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biomasa / Biocombustibles / Aprendizaje Automático Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos