OpenCrystalData: An open-access particle image database to facilitate learning, experimentation, and development of image analysis models for crystallization processes.

Barhate, Yash; Boyle, Christopher; Salami, Hossein; Wu, Wei-Lee; Taherimakhsousi, Nina; Rabinowitz, Charlie; Bommarius, Andreas; Cardona, Javier; Nagy, Zoltan K.; Rousseau, Ronald; Grover, Martha

doi:10.1016/j.dche.2024.100150

Digital Chemical Engineering

2024

DOI: 10.1016/j.dche.2024.100150

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OpenCrystalData: An open-access particle image database to facilitate learning, experimentation, and development of image analysis models for crystallization processes.

Yash Barhate,

Christopher Boyle,

Hossein Salami

et al.

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2024

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Cited by 2 publications

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In-line image analysis of particulate processes with deep learning: Optimizing training data generation via copy-paste augmentation

Daus,

Buchwald,

Peuker

2024

Powder Technology

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In-line image analysis of particulate processes with deep learning: Optimizing training data generation via copy-paste augmentation

Daus,

Buchwald,

Peuker

2024

Powder Technology

View full text Add to dashboard Cite

Batch Cooling Crystallization of a Model System Using Direct Nucleation Control and High-Performance In Situ Microscopy

Budimir Sacher,

Bolf,

Sejdić

2024

Crystals

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The aim of this study was to investigate the use of automated high performance in situ microscopy (HPM) for monitoring and direct nucleation control (DNC) during cooling crystallization. Compared to other techniques, HPM enables the detection of small crystals in the range of 1 to 10 μm. Therefore, a novel DNC-controlled variable was investigated to determine the potential improvement of the method. The laboratory system and process control software were developed in-house. A well-studied crystallization model system, the seeded batch cooling crystallization of α-glycine from water, was investigated under normal conditions and temperatures below 60 °C. It was postulated that length-weighted edge-to-edge counts in the range of 1 to 10 μm would be most sensitive to the onset of secondary nucleation and are therefore, used as a control variable. Linear cooling experiments were conducted to determine the initial setpoint for the DNC experiments. Three DNC experiments were then performed with different setpoints and an upper and lower counts limit. It was found that the DNC method can be destabilized with a low setpoint and narrow counts limits. In addition, the new controlled variable is highly sensitive to the formation of bubbles at the microscope window and requires careful evaluation. To address the advantages of the DNC method, an additional linear cooling experiment of the same duration was performed, and it was found that the DNC method resulted in a larger average crystal size. Overall, it can be concluded that the HPM method is suitable for DNC control and could be improved by modifying the image processing algorithm.

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OpenCrystalData: An open-access particle image database to facilitate learning, experimentation, and development of image analysis models for crystallization processes.

Cited by 2 publications

References 43 publications

In-line image analysis of particulate processes with deep learning: Optimizing training data generation via copy-paste augmentation

In-line image analysis of particulate processes with deep learning: Optimizing training data generation via copy-paste augmentation

Batch Cooling Crystallization of a Model System Using Direct Nucleation Control and High-Performance In Situ Microscopy

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