Application of Model Predictive Control to a Continuous Multiple‐Effect Crystallizer

Partial seeding, in which the nuclei originating from seed crystals grow to yield the product crystals, was simulated and optimized for controlling the batch cooling crystallization process of L-arginine. The product quality was evaluated by the coefficient of variation (CV) of the crystal size distribution. First, the optimum seed amount for partial seeding was estimated by simulation. Then, the simulated values of the optimum seed amount and the resulting local minimum CV were correlated with the seed crystal size and the cooling rate. The correlation can be utilized for estimating the seed amount in the case that the seed crystals are added in a slurry. Finally, these simulated values were compared to the measured ones. Consequently, the optimum seed amount was suggested to be reasonably predicted.

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“…The population balance and the mass balance were assumed to be represented by Eqs. and , respectively , .

true \frac{\partial (W_{s} n)}{\partial t} + G \frac{\partial (W_{s} n)}{\partial L} = W_{normals} B δ (L - L_{0})

true - R_{normalh} \frac{\partial W_{normala}}{\partial t} = ρ_{normalc} k_{normalv} \frac{\partial (W_{s} μ_{3})}{\partial t} = 3 ρ_{normalc} k_{normalv} G W_{normals} μ_{2}

…”

Section: Methodsmentioning

confidence: 99%

“…The population balance and the mass balance were assumed to be represented by Eqs. (1) and (2), respectively [23,24].…”

Section: Modeling and Simulationmentioning

confidence: 99%

Partial Seeding Policy for Controlling the Crystal Quality in Batch Cooling Crystallization

Unno

Hirasawa

2020

Chem Eng & Technol

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“…The method of moments was utilized for solving Eqs. and as simultaneous ordinary differential equations, and the variable L was erased from the differential equations , :

true \partial (W_{s} n) false/ \partial t + G \partial (W_{s} n) false/ \partial L = W_{normals} B δ (L - L_{0})

true - R_{normalh} \partial W_{normala} false/ \partial t = ρ_{normalc} k_{normalv} \partial (W_{s} μ_{3}) false/ \partial t

…”

Section: Methodsmentioning

confidence: 99%

“…In this study, the decrease in solvent caused by solvation was also incorporated into balance equations. In addition, the method of moments was utilized for solving balance equations as simultaneous ordinary differential equations , . Observed values of concentration, mean mass size, and CSD were compared with simulated ones in order to validate balance equations and rate equations.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Kinetics for Batch Cooling Crystallization by Focused‐Beam Reflectance Measurements

et al. 2019

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Numerical simulation of the seeded batch cooling crystallization process of active pharmaceutical ingredients was performed. Crystal size distribution was observed by focused-beam reflectance measurements and concentration by simultaneous Fourier transform infrared spectroscopy. First, kinetic parameters were estimated. Nucleation rate coefficient and order were estimated from the linear regression equation between cooling rate and metastable zone width. The remaining parameters were estimated from the evaluation function. Then, the numerical simulation was performed by utilizing the method of moments. Finally, observed data were compared with simulated data. The simulated values of mean mass size corresponded reasonably with the observed ones.An ethanolic solution of acetaminophen (CH 3 CONHC 6 H 4 OH, AAP) and an aqueous solution of L-arginine (C 6 H 14 N 4 O 2 , Arg) were selected as target substances. Physical properties of these substances are listed in Tab. 1.

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Process Control

Lakerveld¹,

Benyahia²

2020

The Handbook of Continuous Crystallization

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This chapter discusses the control aspects of continuous crystallization processes. Common control objectives for continuous crystallization are related to crystal product quality, process stabilization, economic performance, and environmental impact. Supersaturation is often used as controlled variable to obtain desirable crystal quality attributes, although direct approaches with a crystal quality attribute as controlled variable have also been developed. Sensors to measure crystal quality attributes or supersaturation in situ are readily available, which makes the application of automated feedback control loops attractive. A mixed-suspension mixed-product-removal crystallizer has limited options for process actuation unless fines dissolution is employed. Novel plug-flow crystallizers allow for the adoption of different control strategies (e.g., controlled cooling profiles with seeding). Model-based controllers in combination with state observers can handle time-varying model uncertainty, input constraints, sensor and actuator faults and asynchronous measurements, whereas model-predictive control has the unique capability to enforce multiple process constraints and is most effective when dealing with complicated interactions between multiple inputs and outputs. State observers can also be used to design effective filters for actuator fault detection. The rapid development of dynamic process models, advanced analytical techniques and improved numerical methods are main drivers of the current trend towards model-based control strategies for continuous crystallization.

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Application of Model Predictive Control to a Continuous Multiple‐Effect Crystallizer

Cited by 3 publications

References 37 publications

Partial Seeding Policy for Controlling the Crystal Quality in Batch Cooling Crystallization

Partial Seeding Policy for Controlling the Crystal Quality in Batch Cooling Crystallization

Estimation of Kinetics for Batch Cooling Crystallization by Focused‐Beam Reflectance Measurements

Process Control

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