Enhancing biomass conversion to bioenergy with machine learning : Gains and problems
Copyright © 2024. Published by Elsevier B.V..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:927 |
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| Enthalten in: |
The Science of the total environment - 927(2024) vom: 01. Apr., Seite 172310 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Rupeng [VerfasserIn] |
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| Links: |
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| Themen: |
Bioenergy production |
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| Anmerkungen: |
Date Completed 24.04.2024 Date Revised 25.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.scitotenv.2024.172310 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM370887328 |
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| 520 | |a Copyright © 2024. Published by Elsevier B.V. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a Bioenergy production | |
| 650 | 4 | |a Biomass conversion | |
| 650 | 4 | |a Feedstock | |
| 650 | 4 | |a Full-scale application | |
| 650 | 4 | |a Machine learning | |
| 650 | 7 | |a Biofuels |2 NLM | |
| 700 | 1 | |a He, Zixiang |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Honglin |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Silin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shiyu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Ke |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Meng |e verfasserin |4 aut | |
| 700 | 1 | |a Ho, Shih-Hsin |e verfasserin |4 aut | |
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