Applications of artificial intelligence‐based modeling for bioenergy systems: A review
| Autor: | Yuan Yao, Mochen Liao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
020209 energy
Supply chain lcsh:TJ807-830 lcsh:Renewable energy sources Biomass 02 engineering and technology 010501 environmental sciences bioenergy lcsh:HD9502-9502.5 7. Clean energy 01 natural sciences 12. Responsible consumption Bioenergy 0202 electrical engineering electronic engineering information engineering biochemical conversion Waste Management and Disposal supply chain 0105 earth and related environmental sciences biomass Renewable Energy Sustainability and the Environment Forestry artificial intelligence lcsh:Energy industries. Energy policy. Fuel trade 13. Climate action Biofuel Environmental science biofuel Applications of artificial intelligence Biochemical engineering Agronomy and Crop Science |
| Zdroj: | GCB Bioenergy, Vol 13, Iss 5, Pp 774-802 (2021) |
| ISSN: | 1757-1693 1757-1707 |
| Popis: | Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives. |
| Databáze: | OpenAIRE |
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