基于Micro-CT和深度学习的盐溶液冰离心分离微观结构研究

doi:10.11949/0438-1157.20251264

摘要/Abstract

摘要：

冷冻离心法是一种基于冷冻过程的复合脱盐方法。分布在冰内部的盐胞会影响盐溶液冰的盐度，降低脱盐效果。本文在实验室可控条件下单向冷冻质量浓度为3 wt%的NaCl溶液，冷冻温度设置为–24 °C；采用高分辨率X射线计算机断层扫描（micro-computed tomography， Micro-CT）技术扫描了不同离心参数处理前后的NaCl溶液冰，获得了冰样的CT图像；使用深度学习（deep learning， DL）技术分析了CT图像中盐胞和空气相面积比例等微观参数的变化，并将其与离心前后溶液冰的盐度进行了对比分析。还研究了离心前后冰样内部孔隙和连通性的变化。本研究证明了将Micro-CT技术与DL相结合，能够对离心处理前后盐溶液冰样品内部结构进行可视化观测和定量表征，有助于从微观角度深入理解冷冻离心脱盐过程。

关键词: NaCl溶液冰, 深度学习, 盐胞, 冷冻离心脱盐, 微型计算机断层成像

Abstract:

Freezing and centrifugal desalination is a freezing-based method used to separate salt from water or obtain a concentrated solution. The distribution of salt inclusions inside the ice is a key factor influencing desalination efficiency. Under controlled laboratory conditions, the 3 wt% NaCl solution samples were frozen unidirectional at -24 °C, followed by micro-computed tomography (Micro-CT) scanning both before and after centrifugal treatment. Each sample was processed with a distinct centrifugation time or rotational speed. Deep learning (DL) technique was employed to segment the obtained Micro-CT images and to quantitively analyze the microstructure of the NaCl solution ice samples. The area ratios of both salt inclusions and air phase were compared with the measured salinities of the samples. Changes in the internal porosity and connectivity of ice samples before and after centrifugation were also investigated. It was demonstrated that the combination of Micro-CT and DL technics is an effective way to visualize and quantitatively characterize the microstructure of salt solution ice samples. This method is helpful to understand freezing and centrifugal desalination process from the microscopic view.

Key words: NaCl solution ice, Deep learning, Salt inclusions, Freezing-centrifugal desalination, Micro-computed tomography

中图分类号:

X703.1

张灿, 杨晖, 崔哲, 王溪. 基于Micro-CT和深度学习的盐溶液冰离心分离微观结构研究[J]. 化工学报, DOI: 10.11949/0438-1157.20251264.

Can ZHANG, Hui YANG, Zhe CUI, Xi WANG. Study on microstructure of NaCl solution ice centrifugal separation based on Micro-CT and deep learning[J]. CIESC Journal, DOI: 10.11949/0438-1157.20251264.

图/表 16

参考文献 31

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冰样品编号	离心时间（min）	离心转速（r/min）	扫描段数/段
Sample A	0	0	7
Sample B	1	800	6
Sample C	1	1000	6
Sample D	3	800	6

冰样品编号	离心时间（min）	离心转速（r/min）	扫描段数/段
Sample A	0	0	7
Sample B	1	800	6
Sample C	1	1000	6
Sample D	3	800	6

算法	类别	准确度	Dice系数	敏感度	特异度
5层U-Net	整体	0.9995	0.9993	0.9993	0.9997
	盐胞	0.9993	0.9959	0.9918	1.0000
	空气相	1.0000	1.0000	1.0000	1.0000
	冰	0.9993	0.9996	1.0000	0.9924

算法	类别	准确度	Dice系数	敏感度	特异度
5层U-Net	整体	0.9995	0.9993	0.9993	0.9997
	盐胞	0.9993	0.9959	0.9918	1.0000
	空气相	1.0000	1.0000	1.0000	1.0000
	冰	0.9993	0.9996	1.0000	0.9924

冰样品编号	盐溶液质量（g）	盐溶液盐度（%）	排出液体质量（g）	排出液体盐度（%）	剩余融化液质量（g）	剩余融化液盐度（%）
Sample B	10.01	3	0.471	22.82	9.415	1.95
Sample C	10.02	3	0.650	21.28	9.338	1.73
Sample D	10.04	3	0.714	22.5	9.256	1.48