A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration
Agglomeration in spray fluidized bed (SFB) is a particle growth process that improves powder properties in the chemical, pharmaceutical, and food industries. In order to analyze the underlying mechanisms behind the generation of SFB agglomerates, modeling of the growth process is essential. Morpholo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/21/7221 |
| _version_ | 1797512599109107712 |
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| author | Abhinandan Kumar Singh Evangelos Tsotsas |
| author_facet | Abhinandan Kumar Singh Evangelos Tsotsas |
| author_sort | Abhinandan Kumar Singh |
| collection | DOAJ |
| description | Agglomeration in spray fluidized bed (SFB) is a particle growth process that improves powder properties in the chemical, pharmaceutical, and food industries. In order to analyze the underlying mechanisms behind the generation of SFB agglomerates, modeling of the growth process is essential. Morphology plays an imperative role in understanding product behavior. In the present work, the sequential tunable algorithm developed in previous studies to generate monodisperse SFB agglomerates is improved and extended to polydisperse primary particles. The improved algorithm can completely retain the given input fractal properties (fractal dimension and prefactor) for polydisperse agglomerates (with normally distributed radii of primary particles having a standard deviation of 10% from the mean value). Other morphological properties strongly agreed with the experimental SFB agglomerates. Furthermore, this tunable aggregation model is integrated into the Monte Carlo (MC) simulation. The kinetics of the overall agglomeration at various operating conditions, like binder concentration and inlet fluidized gas temperature, are investigated. The present model accurately predicts the morphological descriptors of SFB agglomerates and the overall kinetics under various operating parameters. |
| first_indexed | 2024-03-10T06:03:03Z |
| format | Article |
| id | doaj.art-d1feeeae4545491c9636b4433f082fbc |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T06:03:03Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d1feeeae4545491c9636b4433f082fbc2023-11-22T20:45:55ZengMDPI AGEnergies1996-10732021-11-011421722110.3390/en14217221A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed AgglomerationAbhinandan Kumar Singh0Evangelos Tsotsas1Thermal Process Engineering, Faculty of Process and Systems Engineering, Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, GermanyThermal Process Engineering, Faculty of Process and Systems Engineering, Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, GermanyAgglomeration in spray fluidized bed (SFB) is a particle growth process that improves powder properties in the chemical, pharmaceutical, and food industries. In order to analyze the underlying mechanisms behind the generation of SFB agglomerates, modeling of the growth process is essential. Morphology plays an imperative role in understanding product behavior. In the present work, the sequential tunable algorithm developed in previous studies to generate monodisperse SFB agglomerates is improved and extended to polydisperse primary particles. The improved algorithm can completely retain the given input fractal properties (fractal dimension and prefactor) for polydisperse agglomerates (with normally distributed radii of primary particles having a standard deviation of 10% from the mean value). Other morphological properties strongly agreed with the experimental SFB agglomerates. Furthermore, this tunable aggregation model is integrated into the Monte Carlo (MC) simulation. The kinetics of the overall agglomeration at various operating conditions, like binder concentration and inlet fluidized gas temperature, are investigated. The present model accurately predicts the morphological descriptors of SFB agglomerates and the overall kinetics under various operating parameters.https://www.mdpi.com/1996-1073/14/21/7221agglomerationmorphologyMonte Carlotunable aggregation modelpolydisperse primary particles |
| spellingShingle | Abhinandan Kumar Singh Evangelos Tsotsas A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration Energies agglomeration morphology Monte Carlo tunable aggregation model polydisperse primary particles |
| title | A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration |
| title_full | A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration |
| title_fullStr | A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration |
| title_full_unstemmed | A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration |
| title_short | A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration |
| title_sort | fast and improved tunable aggregation model for stochastic simulation of spray fluidized bed agglomeration |
| topic | agglomeration morphology Monte Carlo tunable aggregation model polydisperse primary particles |
| url | https://www.mdpi.com/1996-1073/14/21/7221 |
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