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A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration

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  • Abhinandan Kumar Singh

    (Thermal Process Engineering, Faculty of Process and Systems Engineering, Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany)

  • Evangelos Tsotsas

    (Thermal Process Engineering, Faculty of Process and Systems Engineering, Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany)

Abstract

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.

Suggested Citation

  • Abhinandan Kumar Singh & Evangelos Tsotsas, 2021. "A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration," Energies, MDPI, vol. 14(21), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7221-:d:670686
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    References listed on IDEAS

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    1. Skorupski, Krzysztof & Mroczka, Janusz & Wriedt, Thomas & Riefler, Norbert, 2014. "A fast and accurate implementation of tunable algorithms used for generation of fractal-like aggregate models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 106-117.
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    Cited by:

    1. Wang, Rui & Singh, Abhinandan Kumar & Kolan, Subash Reddy & Tsotsas, Evangelos, 2022. "Fractal analysis of aggregates: Correlation between the 2D and 3D box-counting fractal dimension and power law fractal dimension," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

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