基于传感器融合和校准的自适应图像分析方法在原位晶体尺寸测量中的应用

IF 3.2 2区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Wei-Lee Wu, Madeline M. Mills, Ulrich Schacht, Charlie Rabinowitz, Vaso Vlachos and Zoltan K. Nagy*, 
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引用次数: 0

摘要

传统上,通过图像分析或激光衍射方法的离线测量用于分析晶体产品。在结晶过程中间歇性提取晶体样品通常是有益的,以更好地了解分批结晶和连续结晶中的复杂动力学。然而,频繁的侵入性产品采样可能会在过程动力学中导致不希望的干扰。已经开发了用于结晶系统的现场监测的各种过程分析技术;然而,从中获得定量的晶体尺寸分布(CSD)信息是具有挑战性的。虽然在传统浓度监测工具的情况下,系统特定校准是公认的标准程序,但大多数基于图像分析的技术试图提供CSD信息的直接测量。为了获得高纵横比结晶系统颗粒尺寸的快速准确的原位图像分析测量,进行了系统的离线图像分析校准方法,以对离线Malvern Morphologi图像分析结果进行建模。校准图像分析算法以从不同尺寸和固体浓度的原位图像中提取尺寸分布数据。使用遗传算法,通过最小化模型和离线图像分析测量之间的尺寸分布误差,自动优化图像分析算法中的各种方法和参数。然后将在校准参数中观察到的趋势拟合为取决于固体密度的连续函数,以便能够适应固体负载的变化。然后用已知固体负载的不同颗粒数据集对算法进行验证。最后,为了证明传感器融合和在线应用中的概念验证,将自适应图像分析算法与UV/vis传感器相结合,并在动态数据集上进行测试,以预测由于溶解、成核和生长导致的整个结晶过程中固体负载变化的尺寸分布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Sensor Fusion and Calibration-Based Adaptive Image Analysis Procedure for In Situ Crystal Size Measurement

Sensor Fusion and Calibration-Based Adaptive Image Analysis Procedure for In Situ Crystal Size Measurement

Traditionally, off-line measurements via image analysis or laser diffraction methods are used to analyze the crystal product. It is often beneficial to extract crystal samples intermittently during the crystallization process to better understand the complex dynamics in batch and continuous crystallization. However, frequent invasive product sampling can lead to undesired disturbances in the process dynamics. Various process analytical technologies have been developed for in situ monitoring of crystallization systems; however, obtaining quantitative crystal size distribution (CSD) information from these is challenging. While in the case of traditional concentration monitoring tools system-specific calibration is an accepted standard procedure, most image analysis-based techniques attempt to provide direct measurement of CSD information. To obtain a fast and accurate in situ image analysis measurement of particle size for a high aspect ratio crystallization system, a systematic off-line image analysis calibration methodology was performed to model off-line Malvern Morphologi image analysis results. An image analysis algorithm was calibrated to extract size distribution data from in situ images of varying sizes and solid concentrations. Using a genetic algorithm, the various methods and parameters in the image analysis algorithm were automatically optimized by minimizing the size distribution error between the model and the off-line image analysis measurement. Trends observed in the calibrated parameters were then fitted to continuous functions depending on solid density to be able to adapt to changes in the solid loading. The algorithms were then validated with a different particle data set with a known solid loading. Last, to demonstrate the proof of concept in sensor fusion and online application, the adaptive image analysis algorithm was coupled with a UV/vis sensor and tested on a dynamic data set to predict the size distribution with varying solid loadings throughout the crystallization process due to dissolution, nucleation, and growth.

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来源期刊
Crystal Growth & Design
Crystal Growth & Design 化学-材料科学:综合
CiteScore
6.30
自引率
10.50%
发文量
650
审稿时长
1.9 months
期刊介绍: The aim of Crystal Growth & Design is to stimulate crossfertilization of knowledge among scientists and engineers working in the fields of crystal growth, crystal engineering, and the industrial application of crystalline materials. Crystal Growth & Design publishes theoretical and experimental studies of the physical, chemical, and biological phenomena and processes related to the design, growth, and application of crystalline materials. Synergistic approaches originating from different disciplines and technologies and integrating the fields of crystal growth, crystal engineering, intermolecular interactions, and industrial application are encouraged.
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