A three-dimensional population balance model of granulation with a mechanistic representation of the nucleation and aggregation phenomena

Poon, Jonathan M.-H.; Immanuel, Charles D.; Doyle, Francis J.; Litster, James D.

doi:10.1016/j.ces.2007.07.048

Cited by 98 publications

(66 citation statements)

References 39 publications

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“…The recent study of Poon et al [18] has demonstrated the importance to define and control nuclei size distribution since it has a huge impact on the size distribution of the final product. In summary, by utilizing the idea of AE spectra to be a function of present granule size distribution together with multi-way data analysis that enables the handling of the batch-to-batch variation [27], we were able to predict the granule size distribution of the end product by examining the AE spectra acquired from the nucleation phase.…”

Section: Discussionmentioning

confidence: 99%

“…A controlled transition between dominant rate processes is a prerequisite for a successful granulation. It is claimed that the wetting and nucleation phase, and thus the formed nuclei size distribution, has a huge impact on the size distribution of the final end product [18]. Therefore, if no unwanted changes occur in the ratio of the rate processes during the later stages of the process, the granulation outcome can be predicted from the system's behavior in the beginning of the process.…”

Section: Granulation Rate Processesmentioning

confidence: 99%

“…nucleation, has just ended. Moreover, according to the recent study of Poon et al [18], the nuclei size distribution has a huge impact on the size distribution of the final product. In order to examine the reason for this behavior, an examination of different granulation batches was carried out.…”

Section: N-pls Modelingmentioning

confidence: 99%

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Estimation of granule size distribution for batch fluidized bed granulation process using acoustic emission and N‐way PLS

Matero

Poutiainen

Leskinen

et al. 2010

Journal of Chemometrics

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Fluidized bed wet granulation of a pharmaceutical mixture is an unpredictable and complex process. Batch-to-batch variation, i.e. output material inconsistency, is still an unmanaged problem in fluidized bed granulation. The variation in outcome with different size distributions and yield arises from process conditions, which until now have proved difficult to control. The good quality batch is usually one with a high yield of the desired narrow granule size distribution. Thus, for successful granulation the size distribution needs to be controlled throughout the process in order to prevent the heterogeneity of different batch runs and inconsistent batch products. This paper introduces a method for determining end-product granule size distribution in the early phase of granulation, based on the observation that the quality of the early phase nucleation strongly affects the quality of the end product. In the quantitative model, the information gained from the process stream acquired with an acoustic emission (AE) transducer was combined and modeled using an N-way PLS method. Reference sieve analysis for the granule size distribution was performed off-line and was used as the response variable. AE monitoring is a non-invasive technique for evaluating granulation process performance by detecting the sound of particle impacts. AE caused by the particle-chamber wall interaction is influenced by the size of the granules; thus AE contains information about the granule size distribution. This study shows for the first time that the nucleation phase of granulation can be detected using AE techniques, and thus the possibility of predicting end product granule size distribution by means of AE measurement during the nucleation phase.

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Section: Discussionmentioning

confidence: 99%

Section: Granulation Rate Processesmentioning

confidence: 99%

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Estimation of granule size distribution for batch fluidized bed granulation process using acoustic emission and N‐way PLS

Matero

Poutiainen

Leskinen

et al. 2010

Journal of Chemometrics

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“…R nucleation and R breakage are not utilized in this study and therefore not described in more detail. The authors would like to direct the readers to Poon et al [8] and Ramachandran et al [3] for their descriptions. R aggregation (4)-(6) takes into account the formation/depletion of granules due to aggregation, for which the terms have been defined in the literature [32] as…”

Section: Introductionmentioning

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%

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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Enhanced Process Design and Control of a Multiple‐Input Multiple‐Output Granulation Process

Ramachandran

2017

Comprehensive Quality by Design for Pharmaceutical Product Development and Manufacture

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A three-dimensional population balance model of granulation with a mechanistic representation of the nucleation and aggregation phenomena

Cited by 98 publications

References 39 publications

Estimation of granule size distribution for batch fluidized bed granulation process using acoustic emission and N‐way PLS

Estimation of granule size distribution for batch fluidized bed granulation process using acoustic emission and N‐way PLS

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

Enhanced Process Design and Control of a Multiple‐Input Multiple‐Output Granulation Process

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