Real-Time Image Processing Based Online Feedback Control System for Cooling Batch Crystallization

Borsos, Ákos; Szilágyi, Botond; Agachi, Paul Şerban; Nagy, Zoltán K.

doi:10.1021/acs.oprd.6b00242

Cited by 59 publications

(44 citation statements)

References 43 publications

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“…Polymorph concentration control (PCC) is based on the online Raman real-time measurement of polymorph in solution [36]. The new image analysis based direct nucleation control (IA-DNC) is established by PVM to monitor particles in solution [37]. The combination of SSC-DNC [38] and the masscount (MC) framework approach was also established by ATR-FTIR and FBRM in the reported studies [39].…”

Section: Solution Crystallization Process Control Approachesmentioning

confidence: 99%

“…Sayin et al [74] found that DIA is a promising method for monitoring continuous twin-screw wet granulation. In addition, DIA has been successfully applied to feedback control of batch crystallization [37] and high-shear batch granulation [75]. Madarász et al [73] established an online particle size analysis tool for continuous wet granulation based on DIA, which realized real-time feedback control of the process by controlling the liquid feed rate.…”

Section: Image Analysis Based Feedback Controlmentioning

confidence: 99%

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Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization

Gao

Zhang

et al. 2021

Crystals

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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.

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Section: Solution Crystallization Process Control Approachesmentioning

confidence: 99%

Section: Image Analysis Based Feedback Controlmentioning

confidence: 99%

Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization

Gao

Zhang

et al. 2021

Crystals

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show abstract

“…For a predefined difference between the initial and final temperatures, the produced crystal number is inversely proportional with the mean crystal size. The actual crystal number density in solution, however cannot be directly measured but a relative particle number can be tracked, traditionally with Focused Beam Reflectance Measurement (FBRM) (Saleemi et al, 2012), and more recently also with in-line imaging techniques (Borsos et al, 2017). Increase of FBRM counts generally result from nucleation while decrease in counts results from dissolution.…”

Section: Direct Nucleation Control Of L-ascorbic Acid Crystallizationmentioning

confidence: 99%

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

Borsos

Pal

2019

Chemical Engineering Science

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L-ascorbic acid is synthetized in large industrial scale from glucose and marketed as an immune system strengthening agent and anti-oxidant ingredient. The overall yield of conversion of the precursor glucose to L-ascorbic acid is limited, therefore the crystallization is a critically important step of the L-ascorbic acid production from economic point of view. It is widely accepted that the crystal size distribution (CSD) influences numerous relevant macroscopic properties of the final crystalline product and it also significantly affects the downstream operations. The present paper discusses the chord length distribution (CLD, which is directly related to the CSD) control, during the crystallization of L-ascorbic acid from aqueous solution. Batch crystallization process is 2 employed, which is the classical, and still dominant, operation in fine chemical and pharmaceutical industries. A comparative experimental study of two state-of-the-art Quality-by-Control (QbC) based crystallization design approaches are presented: (1) a model-free QbC based on direct nucleation control (DNC) and (2) a model-based QbC using a novel nonlinear model predicative control (NMPC) framework. In the first investigation, the DNC, a process analytical technology based state-of-the-art model free control strategy, is applied. Although, DNC requires minimal preliminary system information and often provides robust process control, due to the unusual crystallization behavior of L-ascorbic acid, it leads to long batch times and oscillatory operation. In a second study the benefits of model-based QbC approach are demonstrated, based on using a NMPC approach. A population balance based crystallization process model is built and calibrated by estimating the nucleation and growth kinetics from concentration and CLD measurements. A projection based CSD to CLD forward transformation is used in the estimation of nucleation and growth kinetics. For robustness and adaptive behavior, the NMPC is coupled with a growing horizon state estimator, which is aimed to continuously improve the model by readjusting the kinetic constants. The study demonstrates that the model-based QbC framework can lead to rapid and robust crystallization process development with the NMPC system presenting good control behavior under significant plant model mismatch (PMM) conditions.

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“…Online imaging has proved to be a useful tool for monitoring crystallization processes. [16][17][18][25][26][27][28] Although accurate identification of the polymorphic form of crystals should be based on XRD, if the shape of two different polymorphs is very different, on-line imaging can also be used for polymorph identification. 27 In this work, the 2D Vision Probe provided by Pharma Vision (Qingdao) Intelligent…”

Section: Monitoring Of Crystal Growth Using On-line Imaging Techniquementioning

confidence: 99%

Improved Understanding of Cefixime Trihydrate Reactive Crystallization and Process Scale-up with the Aid of PAT

Zhang

Wang

2019

Org. Process Res. Dev.

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Real-Time Image Processing Based Online Feedback Control System for Cooling Batch Crystallization

Abstract: • Real time image processing based DNC system is developed and presented • Converged, stable control of batch cooling crystallization was achieved in most cases • The IA-DNC presents several advantages compared to the FBRM based DNC

Cited by 59 publications

References 43 publications

Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization

Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization

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

Improved Understanding of Cefixime Trihydrate Reactive Crystallization and Process Scale-up with the Aid of PAT

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