An experimentally validated simulation model for a four-stage spray dryer

Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik; Utzen, Christer; Jørgensen, John Bagterp

doi:10.1016/j.jprocont.2017.05.001

Cited by 15 publications

(9 citation statements)

References 23 publications

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“…Maltodextrin DE‐18 is a polysaccharide that is among others used as an additive for foods. Maltodextrin is used because it has similar properties to milk, well‐defined composition and can be re‐wetted for reuse in other experiments 19 …”

Section: The Multistage Spray Dryer Setupmentioning

confidence: 99%

“…However, the experiments must be performed during production, which leads to production losses 5,15‐18 . For example, the experiment campaign runs at least 8 hours for a spray dryer for a single product due to long time constants 19 . Additionally, experts are required to supervise the experiments for process safety.…”

Section: Introductionmentioning

confidence: 99%

“…The whitebox models are typically of two kinds, an all‐describing model (such as Computation Fluid Dynamics and high‐fidelity simulation models) and a control‐model. The former whitebox models have a high number of parameters and are as such unsuitable for control design 19,27,28 . The latter whitebox models have a smaller number of parameters, but still require that the process nonlinearities are excited in the datasets.…”

Section: Introductionmentioning

confidence: 99%

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Greybox model for multistage spray drying plants constrained to small datasets

et al. 2020

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Dynamic models of spray drying plants are required for many multivariable control strategies for spray dryers, for example, for model predictive control. Often, the model and its parameters are determined by fitting the model to experimental data. When the experimental data is generated, the experiments disturb normal production and introduce production losses. These losses increase the expenses for determining the model. Therefore, the experimentation time must be limited, which then limits the amount of data available for the model fitting. The limited amount of data restricts the complexity of the spray dryer model. In this article, we propose a greybox model with less complexity than the available models in the literature. The greybox model is derived by combining the process structure, physical relations and data from experimental data. The model is validated with experimental data from an MSD20 pilot plant. Further, the greybox model is compared to a model estimated by subspace identification. The comparison shows that the greybox model has a higher fit to the validation data than the subspace method.

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Section: The Multistage Spray Dryer Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Greybox model for multistage spray drying plants constrained to small datasets

et al. 2020

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“…This approach bridges the research drive towards model-based methods for spray drying and the goals of QbD in the pharmaceutical industry. Previous research in these lines has focused on developing models for spray drying [1,[12][13][14][15][16] or on QbD for spray drying using the design of experiments and defining the design space based on statistical analysis of the experimental data [17][18][19][20]. In this work, the focus is put first on the evaluation of the methods to apply GSA to the spray drying process, followed by the critical analysis of the results to demonstrate its potential to exploit the knowledge of the system contained in the model.…”

Section: Introductionmentioning

confidence: 99%

“…Several models have been developed in the past for the spray drying process. The level of detail and associated complexity of these varies from considering one dimensional differential equations [12,13] to complex partial differential equations, which aim to capture gradients in more multiple dimensions [15]. CFD simulations show the complex flow patterns that are developed inside the drying chamber due to the geometry of the chamber, velocity and pressure gradients in the gas, as well as the size distribution of the particles [16].…”

Section: Introductionmentioning

confidence: 99%

Global Sensitivity Analysis of a Spray Drying Process

2019

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Spray drying is a key unit operation used to achieve particulate products of required properties. Despite its widespread use, the product and process design, as well as the process control remain highly empirical and depend on trial and error experiments. Studying the effect of operational parameters experimentally is tedious, time consuming, and expensive. In this paper, we carry out a model-based global sensitivity analysis (GSA) of the process. Such an exercise allows us to quantify the impact of different process parameters, many of which interact with each other, on the product properties and conditions that have an impact on the functionality of the final drug product. Moreover, classical sensitivity analysis using the Sobol-based sensitivity indices was supplemented by a polynomial chaos-based sensitivity analysis, which proved to be an efficient method to reduce the computational cost of the GSA. The results obtained demonstrate the different response dependencies of the studied variables, which helps to identify possible control strategies that can result in major robustness for the spray drying process.

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