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人工智能辅助的深度学习细分和从生物质灰中获得的X射线微观扫描数据的定量分析

Anna Strandberg¹, Hubert Chevreau², Nils Skoglund¹

¹Umeå University, Department of Applied Physics and Electronics, Thermochemical Energy Conversion Laboratory, SE-901 87 Umeå, Sweden.

|July 23, 2024

概括

深度学习细分精确地从X射线微图数据中分析灰尘颗粒的微结构. 这种方法量化了多孔性和形态,改善了可持续的灰回收和营养回报.

科学领域:

材料科学材料科学材料科学
计算科学计算科学

背景情况:

X射线微观图像可以实现材料微观结构的非破坏性3D可视化.
分析灰颗粒的特性对于从燃烧残留流中可持续的营养回收至关重要.
以前的方法依赖于耗时的手动细分来进行图像分析.

研究的目的:

开发和实施深度学习细分方法,用于灰尘颗粒的微观扫描数据.
为了能够准确地对灰颗粒形态,多孔性和内部微观结构进行定量分析.
在分析异质材料时克服手动细分的局限性.

主要方法:

应用了U-Net架构的深度学习模型来对X射线微断层图像数据进行图像细分.
使用手动输入和算法预测的组合训练了深度学习模型.
对细分数据进行了定量分析,包括毛孔度,毛孔大小分布和特定表面积.

主要成果:

深度学习模型准确地从异质粒子中的孔和背景中细分了灰材料.
定量分析提供了对粒子形态,孔隙性和内部结构的详细见解.
深度学习方法成功地解决了手动细分所面临的挑战,例如分离类似的强度和处理文物.

结论:

深度学习细分为分析灰尘颗粒中复杂的微观结构提供了强大而准确的方法.
这种方法增强了对可持续灰回收至关重要的物理性质的定量分析.
与传统的手动图像分析技术相比,开发的方法提高了效率和准确性.

关键词:

图像分析图像分析用深度学习细分和定量分析对X射线断层扫描数据的图像分析.内部微观结构内部微观结构微型CT技术的使用开放的孔隙体积的数量.毛孔大小分布的分布多孔性多孔性具体的表面积面积是一个特定的表面积.墙壁厚度墙壁厚度灰灰回收的废弃物回收在 μCTCT 中.

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