Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review
Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review
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DOI：
10.1016/j.resconrec.2022.106847
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
H. Wang;Yuan Yao
中科院分区：
文献类型：
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作者：
H. Wang;Yuan Yao
生物质衍生材料（BDM）在水和农业系统中具有广泛的应用。作为一种新兴的工具，机器学习（ML）已被应用于BDM系统，以解决材料，流程和供应链设计的挑战。本文回顾了自2008年以来发表的53篇论文，以了解ML在支持BDM可持续发展和应用方面的能力，当前限制和未来潜力。以前的ML应用根据其目标分为三类-材料和工艺设计，最终用途性能预测和可持续性评估。这些机器学习应用程序专注于识别优化BDM系统的关键因素，预测材料特性和性能，逆向工程，以及解决可持续性评估的数据挑战。BDM数据集显示出很大的变化，其中约75%的数据点小于600个。包围模型和最先进的神经网络（NN）在这些数据集上表现良好。BDM系统的ML扩展的局限性在于集合和NN模型的可解释性较低，以及缺乏考虑地理时间动态的可持续性评估研究。建议为BDM系统的未来ML研究提供工作流程。需要更多的研究来探索ML应用于BDM系统的可持续发展，评估和优化。
Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems.