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Digital Twin Design of an Agrochemical Crystallization Process Using a 2D Population Balance Model Approach

Source: AIChE
  • Type:
    Conference Presentation
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    AIChE Member Credits 0.5
    AIChE Members $19.00
    AIChE Graduate Student Members Free
    AIChE Undergraduate Student Members Free
    Non-Members $29.00
  • Conference Type:
    AIChE Annual Meeting
  • Presentation Date:
    November 8, 2021
  • Duration:
    21 minutes
  • Skill Level:
    Intermediate
  • PDHs:
    0.50

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In the agrochemical industry, crystallization is a key separation process that is used in the to separate the agrochemical actives from reaction mixtures and impure solutions. A poorly designed crystallization step can lead to poor yield, low purity, and long filtration time. To minimize process cycle time, a series of design of experiments led by the Quality-by-Design (QbD) approach can be used by practice to optimize the operating profile of a crystallization process (i.e. temperature profile, solvent/antisolvent addition profile, pH profile).1 However, an exhaustive list of experiments covering all factors is needed to explore the whole design space experimentally. To avoid excessive open-loop experiments and personnel exposure to toxic chemicals, recently introduced Quality-by-Control (QbC) methodology controls critical quality attributes (CQAs) through implementation of feedback control algorithm to determine the operating profile of the process.2 For rapid process design, direct design or model free (mfQbC) approaches can be used to generate a robust operating profile leading the system to the desired CQAs. However, mfQbC results are often suboptimal with new process changes (scale-up, mixing conditions, and solvent addition), which requires model-based QbC (mbQbC) to further optimize the mfQbC results.3,4

In this work, a digital twin of an industrial agrochemical crystallization process was developed to generate a temperature cycling operating profile with the objective of maximizing crystal size to reduce filtration time. A 2D population balance model (PBM) solved via semi-discrete finite volume method was developed. Parameter estimation experiments to determine the 2D kinetics was performed using a well-mixed 2D PBM and laboratory scale experiments. Scale-up and antisolvent addition effects were incorporated in the digital twin by using a multicompartmental PBM approach with parameters refined from large-scale experiments. Various compartments were required to capture wall-effects from large jacket to vessel temperature differences and high shear environment near the impeller area.

References:

(1) Bondi, R. W.; Drennen, J. K. Quality by Design and the Importance of PAT in QbD; Academic Press, 2011; Vol. 10. https://doi.org/10.1016/B978-0-12-375680-0.00005-X.

(2) Su, Q.; Ganesh, S.; Moreno, M.; Bommireddy, Y.; Gonzalez, M.; Reklaitis, G. V.; Nagy, Z. K. A Perspective on Quality-by-Control (QbC) in Pharmaceutical Continuous Manufacturing. Comput. Chem. Eng. 2019, 125, 216–231. https://doi.org/10.1016/j.compchemeng.2019.03.001.

(3) Szilagyi, B.; Eren, A.; Quon, J. L.; Papageorgiou, C. D.; Nagy, Z. K. Application of Model-Free and Model-Based Quality-by-Control (QbC) for the Efficient Design of Pharmaceutical Crystallization Processes. Cryst. Growth Des. 2020, 20 (6), 3979–3996. https://doi.org/10.1021/acs.cgd.0c00295.

(4) Öner, M.; Bach, C.; Tajsoleiman, T.; Molla, G. S.; Freitag, M. F.; Stocks, S. M.; Abildskov, J.; Krühne, U.; Sin, G. Scale-up Modeling of a Pharmaceutical Crystallization Process via Compartmentalization Approach. In Computer Aided Chemical Engineering; Elsevier B.V., 2018; Vol. 44, pp 181–186. https://doi.org/10.1016/B978-0-444-64241-7.50025-2.

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Checkout

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Pricing


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AIChE Member Credits 0.5
AIChE Members $19.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
Non-Members $29.00
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