Lightweight deep learning-based shape characterization and apparent growth kinetics analysis in continuous crystallization

LI Qiang; ZHANG Xiangyang

doi:10.11949/0438-1157.20260806

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Lightweight deep learning-based shape characterization and apparent growth kinetics analysis in continuous crystallization

更新时间：2026-07-11

- Lightweight deep learning-based shape characterization and apparent growth kinetics analysis in continuous crystallization
- CIESC Journal (2026)
- 作者机构：
  
  华东理工大学化学工程与低碳技术全国重点实验室，上海 200237
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20260806
  CLC： TQ 026.5
- Received：11 June 2026，
  
  Revised：2026-07-10，
  
  Accepted：11 July 2026，
- 稿件说明：
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LI Qiang, ZHANG Xiangyang. Lightweight deep learning-based shape characterization and apparent growth kinetics analysis in continuous crystallization[J/OL]. CIESC Journal, 2026.
DOI：

LI Qiang, ZHANG Xiangyang. Lightweight deep learning-based shape characterization and apparent growth kinetics analysis in continuous crystallization[J/OL]. CIESC Journal, 2026. DOI： 10.11949/0438-1157.20260806.

摘要

面向连续结晶中产品形貌表征难的问题，提出基于轻量化深度学习的表观动力学分析方法。引入视觉基础大模型SAM3辅助预标注，利用轻量化网络（YOLO26m-seg）对晶体图像进行实例分割（单张耗时约17 ms），结合图像二阶矩提取形貌特征。以库埃特—泰勒连续结晶器内双氰胺结晶过程为研究对象，结果表明在稳态工况下产品

为117.0 μm，并基于双判据方法识别出约2.9%的可疑团聚体。建立二维表观动力学模型，量化双氰胺长短轴表观生长速率比（1.75），讨论了强剪切流场对传质边界层的非对称影响。为连续结晶产品质量控制提供算法支撑。

Abstract

To address the challenge of characterizing product habits in continuous crystallization

an apparent kinetic analysis method based on lightweight deep learning is proposed. A vision foundation model (SAM3) was introduced for auxiliary pre-labeling

and a lightweight network (YOLO26m-seg) was utilized for the instance segmentation of crystal images

achieving a processing speed of approximately 17 ms per frame. Morphological features were then extracted using image second-order moments. Using the continuous crystallization of dicyandiamide in a Couette-Taylor crystallizer as a case study

the median particle size (

) under steady-state conditions was determined to be 117.0 μm

and approximately 2.9% of suspicious agglomerates were identified based on a dual criterion. Furthermore

a two-dimensional apparent kinetic model was established to quantify the apparent growth rate ratio of the major to minor axes of dicyandiamide (1.75)

and the asymmetrical effect of the strong shear flow field on the mass transfer boundary layer was discussed. This study provides algorithmic support for the quality control of continuous crystallization products.

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references

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