Fluidized bed spray granulation: Analysis of the system behaviour by means of dynamic flowsheet simulation

Dosta, Maksym; Heinrich, Stefan; Werther, Joachim

doi:10.1016/j.powtec.2010.07.018

Cited by 48 publications

(40 citation statements)

References 21 publications

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“…This was followed by plotting the time taken to simulate each case versus grid size and then the speedup ratio versus grid size. The ratios were calculated as Ratio = single CPU time gfor time (10) or Ratio = GPU fortime gfor time .…”

Section: Comparing Cpu-for Gpu-for and Gpu-gfor Executionmentioning

confidence: 99%

“…Although the underlying mechanisms of such processes are yet to be thoroughly grasped, granulation, a particle design process, is one such area where substantial progress has been made over the years [5]. The approaches for modeling such systems are as numerous as they are varied: Discrete Element Modeling (DEM) [6], Population Balance Modeling (PBM) [3,[7][8][9][10][11][12][13], hybrid models by combining PBM with DEM [14], PBM with Volume of Fluid (VoF) methods [15], and PBM with Computational Fluid Dynamics (CFD) [16], to name a few. Of the aforementioned, the most widely used are the DEM and PBM methods.…”

Section: Introductionmentioning

confidence: 99%

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Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Prakash

Chaudhury

Ramachandran

2013

Modelling and Simulation in Engineering

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Computer-aided modeling and simulation are a crucial step in developing, integrating, and optimizing unit operations and subsequently the entire processes in the chemical/pharmaceutical industry. This study details two methods of reducing the computational time to solve complex process models, namely, the population balance model which given the source terms can be very computationally intensive. Population balance models are also widely used to describe the time evolutions and distributions of many particulate processes, and its efficient and quick simulation would be very beneficial. The first method illustrates utilization of MATLAB's Parallel Computing Toolbox (PCT) and the second method makes use of another toolbox, JACKET, to speed up computations on the CPU and GPU, respectively. Results indicate significant reduction in computational time for the same accuracy using multicore CPUs. Many-core platforms such as GPUs are also promising towards computational time reduction for larger problems despite the limitations of lower clock speed and device memory. This lends credence to the use of highfidelity models (in place of reduced order models) for control and optimization of particulate processes.

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Section: Comparing Cpu-for Gpu-for and Gpu-gfor Executionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Prakash

Chaudhury

Ramachandran

2013

Modelling and Simulation in Engineering

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“…For the macroscale calculations of solids processes the dynamic flowsheet simulation system "SolidSim- Dynamics" has been applied [9]. The temporal change of the PSD, which occurs due to the continuous transport of the particles in the state space, has been described by the one-dimensional populationbalance equation (PBE) with particle diameter as internal coordinate.…”

Section: Macroscale Levelmentioning

confidence: 99%

“…To calculate the evaporation process, the system of differential equations for the description of air temperature, mass of liquid film, temperature of liquid film, particle temperature and air humidity has been formulated [9].The Reynolds number, which is needed for the approximation of heat and mass transfer coefficients, depends on the relative velocity between particle and fluid and it is obtained based on the results received from the microscale.…”

Section: Mesoscale Levelmentioning

confidence: 99%

Novel multiscale simulation environment for modeling of fluidized bed granulation

Dosta

Heinrich

2013

AIP Conference Proceedings

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Abstract. In this contribution the architecture of a novel simulation environment,which has been developedfor the multiscale modeling of fluidized bed spray granulation,is presented. The novel environment describes the granulation process on four different time and length scales. On the one hand, it allows to predict dynamics of the global production process, whereby, on the other hand, material properties can be considered.

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“…Schütte et al (1998) presented their data in a patent without further interpretation and without explanations regarding reproducibility. Besides Heinrich et al (2002), Radichkov et al (2006) and Dreyschultze et al (in press), a wide range of authors deal with these oscillations of PSD in simulations (for example Bück et al, 2015;Cotabarren et al, 2010;Dosta et al, 2010;Drechsler et al, 2005;Heinrich et al, 2003;Palis and Kienle, 2012;Palis and Kienle, 2014) or use them to motivate the need of process control or better models (for example Bertin et al, 2013;Cotabarren et al, 2011). But none of these references includes an experimental proof of the occurrence of PSD oscillations.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of process stability of continuous spray fluidized bed layering with external product separation

Schmidt

Rieck

Bück

et al. 2015

Chemical Engineering Science

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Experimental investigation of dynamics in continuous layering with external classification. Proof of oscillations in particle size distribution (stable limit cycle). Similar periodic behavior for significant variation in mean diameter of milled particles. Milling behavior determined as key process influence. a b s t r a c tThe present work investigates experimentally continuous spray fluidized bed layering with external product separation. In this process configuration, growing particles are continuously withdrawn and classified externally by two sieves into product fraction, undersize and oversize fraction. The oversize fraction is milled and fed back to the fluidized bed alongside with the undersized fraction. New seed particles for the layering process are provided by recycling of milled particles and agglomeration of dust particles, formed by dried droplets (overspray) and attrition. Until now, this process configuration has been investigated by simulations only. For certain parameter regimes, these models predict oscillations of the particle size distribution. Based on these predictions, even theoretical controllers have already been developed to diminish these oscillations. An experimental investigation of these oscillations of the particle size has not yet been published. Our work presents a systematic study of the process and proves the existence of oscillations of the particle size distribution in the fluidized bed.

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Fluidized bed spray granulation: Analysis of the system behaviour by means of dynamic flowsheet simulation

Cited by 48 publications

References 21 publications

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Novel multiscale simulation environment for modeling of fluidized bed granulation

Experimental investigation of process stability of continuous spray fluidized bed layering with external product separation

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