Experimental implementation of a Quality-by-Control (QbC) framework using a mechanistic PBM-based nonlinear model predictive control involving chord length distribution measurement for the batch cooling crystallization of l-ascorbic acid

Szilágyi, Botond; Borsos, Ákos; Pal, Kanjakha

doi:10.1016/j.ces.2018.09.032

Cited by 34 publications

(30 citation statements)

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“…This requires only simple feedback control strategies to automatically identify the operating curve for a given process with minimal experimental effort and calibration. 31,[36][37][38] The operating curve can then be used as a setpoint in open-loop implementations, which is part of the scale-up strategy implemented in this work and shown in a schematic representation in Figure 1. First, open-loop experiments are implemented to identify the key operating parameters for the antisolvent crystallization.…”

Section: Scale-up Strategymentioning

confidence: 99%

Polymorphic Control and Scale-Up Strategy for Antisolvent Crystallization Using Direct Nucleation Control

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Szilágyi

Diego

et al. 2020

Crystal Growth & Design

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In this work, a scale-up strategy based on the principles of direct design and Quality-by-Control (QbC) was applied and investigated using direct nucleation control (DNC). Process analytical technologies (PATs) were implemented for process monitoring and control. Antisolvent crystallization of indomethacin (IMC) was performed in a ternary solvent and antisolvent system.Mixture of acetone-methanol (66.5-33.5 wt%) was used as solvent and water as antisolvent since the mixed solvent system provides increased process yield and facilitates the suppression of the undesired acetone solvates. The effect of different process parameters, such as seed load, seed size and initial concentration were investigated on process time, solid form and particle size distribution (PSD). The simplified solvent and antisolvent addition profiles obtained from the DNC were implemented directly in open-loop control to investigate reproducibility of the designed process.The open-loop operation was successfully scaled up by one order of magnitude, obtaining crystalline products with similar properties (solid form and PSD) as in the small-scale experiments.The results provide a proof-of-concept showing how the direct design approach can be applied in the rapid development of a robust crystallization process and efficient scale-up to yield the desired solid form with desired particulate properties (unimodal PSD and no agglomeration).

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Section: Scale-up Strategymentioning

confidence: 99%

Polymorphic Control and Scale-Up Strategy for Antisolvent Crystallization Using Direct Nucleation Control

Østergaard

Szilágyi

Diego

et al. 2020

Crystal Growth & Design

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“…From the previous presentation on dynamic design space, it became clear that both control and systems theory play an important role. The concept of dynamic design space can only be practically implemented with advanced control concepts; see [30][31][32] and references therein. Assuming functioning control loop structures for the individual process units, it is precisely the higher-level control architecture that enables flexible pharmaceutical manufacturing processes that adapt to disturbances while complying with the required final CQA acceptance limits [33][34][35][36][37].…”

Section: Control and Systems Theorymentioning

confidence: 99%

Working within the Design Space: Do Our Static Process Characterization Methods Suffice?

et al. 2020

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The Process Analytical Technology initiative and Quality by Design paradigm have led to changes in the guidelines and views of how to develop drug manufacturing processes. On this occasion the concept of the design space, which describes the impact of process parameters and material attributes on the attributes of the product, was introduced in the ICH Q8 guideline. The way the design space is defined and can be presented for regulatory approval seems to be left to the applicants, among who at least a consensus on how to characterize the design space seems to have evolved. The large majority of design spaces described in publications seem to follow a “static” statistical experimentation and modeling approach. Given that temporal deviations in the process parameters (i.e., moving within the design space) are of a dynamic nature, static approaches might not suffice for the consideration of the implications of variations in the values of the process parameters. In this paper, different forms of design space representations are discussed and the current consensus is challenged, which in turn, establishes the need for a dynamic representation and characterization of the design space. Subsequently, selected approaches for a dynamic representation, characterization and validation which are proposed in the literature are discussed, also showcasing the opportunity to integrate the activities of process characterization, process monitoring and process control strategy development.

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“…Crystallization 1 is a widely used separation and purification process, 2,3 which is used to separate the compound of interest from a solvent or a mixture of solvents 4 . As a unit operation, 5 it is used in a multitude of industries including chemical, 6 food processing, 7 petrochemical, and microelectronics 8 .…”

Section: Introductionmentioning

confidence: 99%

“…Spherical agglomeration (SA) 2,3,26,27 offers a potential solution to the aforementioned problems by agglomerating crystals directly within the crystallizer 28,29,30 . The idea behind SA is to produce fine crystals (primary crystals [PC]) by crystallization 31 and then agglomerate them in situ by adding a bridging liquid 14 .…”

Section: Introductionmentioning

confidence: 99%

Iterative model‐based experimental design for spherical agglomeration processes

et al. 2021

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Spherical agglomeration (SA) is a process intensification strategy, which can reduce the number of unit operations in pharmaceutical manufacturing. SA merges drug substance crystallization with drug product wet granulation, reducing capital, and operating costs. However, SA is a highly nonlinear process, thus for its efficient operation model‐based design and control strategies are beneficial. These require the development of a high‐fidelity process model with appropriately estimated parameters. There are two major problems associated with the development of a high‐fidelity process models—(i) selection of the appropriate model corresponding to the underlying process mechanisms, and (ii) accurate estimation of the parameters. This work focuses on the identification of the best fitting model that correlates with experimental observations using cross‐validation experiments. Further, an iterative model‐based experimental design strategy is developed, which uses D‐optimal experimental design criterion to minimize the number of experiments necessary to obtain accurate parameter estimates.

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Experimental implementation of a Quality-by-Control (QbC) framework using a mechanistic PBM-based nonlinear model predictive control involving chord length distribution measurement for the batch cooling crystallization of l-ascorbic acid

Cited by 34 publications

References 50 publications

Polymorphic Control and Scale-Up Strategy for Antisolvent Crystallization Using Direct Nucleation Control

Polymorphic Control and Scale-Up Strategy for Antisolvent Crystallization Using Direct Nucleation Control

Working within the Design Space: Do Our Static Process Characterization Methods Suffice?

Iterative model‐based experimental design for spherical agglomeration processes

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