Wei-Lee Wu, Madeline M. Mills, Ulrich Schacht, Charlie Rabinowitz, Vaso Vlachos and Zoltan K. Nagy*,
{"title":"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","authors":"Wei-Lee Wu, Madeline M. Mills, Ulrich Schacht, Charlie Rabinowitz, Vaso Vlachos and Zoltan K. Nagy*, ","doi":"10.1021/acs.cgd.3c00273","DOIUrl":null,"url":null,"abstract":"<p >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.</p>","PeriodicalId":34,"journal":{"name":"Crystal Growth & Design","volume":"23 10","pages":"7076–7089"},"PeriodicalIF":3.2000,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sensor Fusion and Calibration-Based Adaptive Image Analysis Procedure for In Situ Crystal Size Measurement\",\"authors\":\"Wei-Lee Wu, Madeline M. Mills, Ulrich Schacht, Charlie Rabinowitz, Vaso Vlachos and Zoltan K. Nagy*, \",\"doi\":\"10.1021/acs.cgd.3c00273\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >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.</p>\",\"PeriodicalId\":34,\"journal\":{\"name\":\"Crystal Growth & Design\",\"volume\":\"23 10\",\"pages\":\"7076–7089\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2023-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Crystal Growth & Design\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acs.cgd.3c00273\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Crystal Growth & Design","FirstCategoryId":"92","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.cgd.3c00273","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
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.
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.