Applications of artificial intelligence‐based modeling for bioenergy systems: A review
Applications of artificial intelligence‐based modeling for bioenergy systems: A review
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DOI：
10.1111/gcbb.12816
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发表时间：
2021-05
期刊：
影响因子：
--
通讯作者：
Mochen Liao;Yuan Yao
中科院分区：
文献类型：
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作者：
Mochen Liao;Yuan Yao
生物能源被广泛认为是化石燃料的可持续替代品。然而，生物质能源产品的大规模应用受到原料可变性、转化经济性和供应链可靠性等挑战的限制。近几十年来，人工智能（AI）这一新兴概念已被应用于生物能源系统，以应对这些挑战。本文回顾了2005年至2019年期间发表的164篇将不同人工智能技术应用于生物能源系统的文章。本文重点介绍了各种人工智能技术在应对生物能源相关研究挑战和提高生物能源系统性能方面的独特能力。具体来说，我们通过输入变量、输出变量、AI技术、数据集大小和性能来表征AI研究。我们研究了人工智能在生物能源系统整个生命周期中的应用。我们确定了人工智能主要应用的四个领域，包括（1）生物质特性的预测，（2）生物质转化过程性能的预测，包括不同的转化途径和技术，（3）生物燃料特性的预测和生物能源最终使用系统的性能，以及（4）供应链建模和优化。根据审查，人工智能在生成难以直接测量的数据，改进生物质转化和生物燃料最终用途的传统模型，以及克服传统计算技术对生物能源供应链设计和优化的挑战方面特别有用。对于未来的研究，需要努力开发标准化和实用的程序来选择人工智能技术和确定训练数据样本，以加强生物能源相关领域的数据收集，记录和共享，并从整体角度探索人工智能在支持生物能源系统可持续发展方面的潜力。
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.