Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence
The knowledge of product particle size distribution (PSD) in crystallization processes is of high interest for the pharmaceutical and fine chemical industries, as well as in research and development. Not only can the efficiency of crystallization/production processes and product quality be increased...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/16/3/1002 |
| _version_ | 1797623970356264960 |
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| author | Stefan Höving Laura Neuendorf Timo Betting Norbert Kockmann |
| author_facet | Stefan Höving Laura Neuendorf Timo Betting Norbert Kockmann |
| author_sort | Stefan Höving |
| collection | DOAJ |
| description | The knowledge of product particle size distribution (PSD) in crystallization processes is of high interest for the pharmaceutical and fine chemical industries, as well as in research and development. Not only can the efficiency of crystallization/production processes and product quality be increased but also new equipment can be qualitatively characterized. A large variety of analytical methods for PSDs is available, most of which have underlying assumptions and corresponding errors affecting the measurement of the volume of individual particles. In this work we present a method for the determination of particle volumes in a bulk sample via micro-computed tomography and the application of artificial intelligence. The particle size of bulk samples of sucrose were measured with this method and compared to classical indirect measurement methods. Advantages of the workflow are presented. |
| first_indexed | 2024-03-11T09:36:16Z |
| format | Article |
| id | doaj.art-2f7f8f9a6677473c9a80b4bf8c4fb1ca |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-11T09:36:16Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-2f7f8f9a6677473c9a80b4bf8c4fb1ca2023-11-16T17:15:44ZengMDPI AGMaterials1996-19442023-01-01163100210.3390/ma16031002Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial IntelligenceStefan Höving0Laura Neuendorf1Timo Betting2Norbert Kockmann3Laboratory of Equipment Design, Department of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 68, 44227 Dortmund, GermanyLaboratory of Equipment Design, Department of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 68, 44227 Dortmund, GermanyLaboratory of Equipment Design, Department of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 68, 44227 Dortmund, GermanyLaboratory of Equipment Design, Department of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 68, 44227 Dortmund, GermanyThe knowledge of product particle size distribution (PSD) in crystallization processes is of high interest for the pharmaceutical and fine chemical industries, as well as in research and development. Not only can the efficiency of crystallization/production processes and product quality be increased but also new equipment can be qualitatively characterized. A large variety of analytical methods for PSDs is available, most of which have underlying assumptions and corresponding errors affecting the measurement of the volume of individual particles. In this work we present a method for the determination of particle volumes in a bulk sample via micro-computed tomography and the application of artificial intelligence. The particle size of bulk samples of sucrose were measured with this method and compared to classical indirect measurement methods. Advantages of the workflow are presented.https://www.mdpi.com/1996-1944/16/3/1002determination of particle size distributionsthree dimensional particle analysis in bulkmicro-computed tomographyMask–RCNNWadell sphericity |
| spellingShingle | Stefan Höving Laura Neuendorf Timo Betting Norbert Kockmann Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence Materials determination of particle size distributions three dimensional particle analysis in bulk micro-computed tomography Mask–RCNN Wadell sphericity |
| title | Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence |
| title_full | Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence |
| title_fullStr | Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence |
| title_full_unstemmed | Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence |
| title_short | Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence |
| title_sort | determination of particle size distributions of bulk samples using micro computed tomography and artificial intelligence |
| topic | determination of particle size distributions three dimensional particle analysis in bulk micro-computed tomography Mask–RCNN Wadell sphericity |
| url | https://www.mdpi.com/1996-1944/16/3/1002 |
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