Systematic model identification and optimization-based active polymorphic control of crystallization processes

Simone, Elena; Szilágyi, Botond

doi:10.1016/j.ces.2017.09.034

Cited by 33 publications

(36 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recently, the kinetic model of solvent‐mediated transformation processes was extended to a hypothetical dimorphic system by considering additional crystallization mechanisms 15 . Accounting for the solvent‐mediated polymorphic transformation of ortho‐aminobenzoic acid, a PBE‐based polymorphic control strategy was developed for production of the stable polymorph 16 . Nonetheless, no PBE‐based studies can be found in the literature to model the crystallization process of concomitant polymorphism.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, we utilized process modeling and simulation to bridge kinetic events, including nucleation and crystal growth, with a population balance equation (PBE) model. PBE models are widely used to simulate various crystallization processes, including solventmediated polymorphic transformation [13][14][15][16] and chiral resolution by preferential crystallization. 17,18 Both seeded and unseeded phase transformation processes of L-glutamic acid α-form to the stable β-form were described using a population balance model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Form selection of concomitant polymorphs: A case study informed by crystallization kinetics modeling

et al. 2021

View full text Add to dashboard Cite

Molecular mechanisms and process kinetics of crystallizing concomitant polymorphs remain poorly understood. Solvent-mediated phase transformation and concomitant crystallization are difficult to be distinguished in practice, as multiple forms can be detected at the same time. Herein, we developed a population balance model to simulate a concomitant crystallization process of two polymorphs of tolfenamic acid. Our kinetic modeling aims to understand concomitant crystallization and help guide form selection of such a molecular system. Crystallization kinetics of ethanolic solutions were uncovered from induction time measurements, as well as seeded and unseeded crystallization experiments. Experimental and simulation results demonstrate that the stable form I crystallizes concomitantly with the metastable form II. The faster growing form II results in an intermediate decline in the composition of form I in crystallized samples, a characteristic feature of the concomitantly crystallized system. A four-quadrant scheme of attainable polymorph outcome was simulated under various crystallization conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Form selection of concomitant polymorphs: A case study informed by crystallization kinetics modeling

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Based on the combination of Raman spectroscopy and ATR-UV/Visible spectroscopy, Simone et al [40,83] developed active polymorphic feedback control (AFPC) and used it to perform hierarchical control to achieve crystal refining, as shown in the simple schematic diagram of the control strategy (Figure 9). Raman spectroscopy is used to detect the presence of the undesired form (P) by using a calibration-free approach; ATR-UV/Vis is used to measure the solute concentration (C) and performs SSC using a calibration-based approach to determine the set point for the temperature controller (Tsp).…”

Section: Polymorphic Feedback Controlmentioning

confidence: 99%

Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization

Gao

Zhang

et al. 2021

Crystals

View full text Add to dashboard Cite

Crystallization is one of the important unit operations for the separation and purification of solid products in the chemical, pharmaceutical, and pesticide industries, especially for realizing high-end, high-value solid products. The precise control of the solution crystallization process determines the polymorph, crystal shape, size, and size distribution of the crystal product, which is of great significance to improve product quality and production efficiency. In order to develop the crystallization process in a scientific method that is based on process parameters and data, process analysis technology (PAT) has become an important enabling platform. In this paper, we review the development of PAT in the field of crystallization in recent years. Based on the current research status of drug crystallization process control, the monitoring methods and control strategies of feedback control in the crystallization process were systematically summarized. The focus is on the application of model-free feedback control strategies based on the solution and solid information collected by various online monitoring equipment in product engineering, including improving particle size distribution, achieving polymorphic control, and improving purity. In this paper, the challenges of feedback control strategy in the crystallization process are also discussed, and the development trend of the feedback control strategy has been prospected.

show abstract

“…The bridging liquid is added, forming bridging liquid droplets under stirring due to the immiscibility between the bridging liquid and the mixture of solvent and anti‐solvent, which can effectively wet the individual crystals into spherical agglomerates 3 . Compared with the traditional granulation technology (Figure 1A), this technology (Figure 1B) significantly simplifies the equipment and process, achieving equipment space and cost savings, energy conservation, less carbon emission, consumption reduction, and reduced production cycle by over 40% 4–8 . Furthermore, the greatly enhanced powder properties of the spherical products achieve advanced functions, for example, direct tableting for drugs, 9–12 anti‐caking ability for food and fertilizers, 13 superior skin feeling for cosmetics, 14 high performance for explosives, 15–17 and so on.…”

Section: Introductionmentioning

confidence: 99%

Design of the spherical agglomerate size in crystallization by developing a two‐step bridging mechanism and the model

Yao

et al. 2021

AIChE Journal

View full text Add to dashboard Cite

Spherical agglomeration technology can produce high‐performance spherical particles in a single crystallization unit, although it is still challenging to control the particle size and shape. To solve this issue, a two‐step bridging (TSB) mechanism containing a preconditioning period, size period, and shape period is proposed. The dynamic balance among the forces of adhesion, dispersion, and capillary action in the multi‐liquid phases plays a key role. This is fully considered to establish the TSB‐based thermodynamic size model and particle design framework by weighting the force action regions in multi‐liquid phases with dynamic composition. The spherical agglomerates of benzoic acid, celecoxib, and salicylic acid with narrow particle size distributions and tunable particle size ranges of 2000–5000, 800–3500, and 1500–4500 μm, respectively, were designed and prepared successfully, showing good correlation with the calculation, which is superior to the reported methods and indicates that the mechanism has certain universality and guiding significance.

show abstract

Systematic model identification and optimization-based active polymorphic control of crystallization processes

Abstract: This is a repository copy of Systematic model identification and optimization-based active polymorphic control of crystallization processes.

Cited by 33 publications

References 37 publications

Form selection of concomitant polymorphs: A case study informed by crystallization kinetics modeling

Form selection of concomitant polymorphs: A case study informed by crystallization kinetics modeling

Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization

Design of the spherical agglomerate size in crystallization by developing a two‐step bridging mechanism and the model

Contact Info

Product

Resources

About