Modelling and optimization of seeded batch crystallizers

Hu, Qingwen; Rohani, Sohrab; Jutan, Arthur

doi:10.1016/j.compchemeng.2004.09.011

Cited by 81 publications

(93 citation statements)

References 14 publications

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“…Obtaining an analytical solution to the equation is difficult, and researchers [16][17][18][19][20][21][22] suggested the use of numerical methods to solve the equation efficiently and accurately. Among these methods, the method of moments has been widely used because it could convert the partial differential equation to a group of ordinary differential equations easily.…”

Section: Population Balance Mathematical Representationmentioning

confidence: 99%

Modeling and estimation of the kinetics of seeded solvent-mediated phase transformation in a batch crystallizer

Tang

Shen

Shi

2011

J. Wuhan Univ. Technol.-Mat. Sci. Edit.

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Modeling the kinetics of the preparing process is necessary to produce a product with the appropriate particle properties and minimum production cost. Owing to the lackness of crystal size distributor (CSD) information, however, solvent-mediated phase transformation encounters difficulty in modeling the kinetics as compared to solution crystallization. Consequently, a model was established by making the product CSD to move along by horizontal translation to obtain the CSDs of the stable phase in the process of transformation. Then the moment method was used to solve the popular balance equation, and the least square nonlinear regression method was applied to estimate the kinetics parameters. The model has been successfully used to simulate the transformation of CaSO 4 •2H 2 O to α-CaSO 4 •1/2H 2 O in an isothermal seeded batch crystallizer with different stirring speeds, and it is benefi cial to producing high performance α-CaSO 4 •1/2H 2 O crystals which have the right particle characteristics.

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Section: Population Balance Mathematical Representationmentioning

confidence: 99%

Modeling and estimation of the kinetics of seeded solvent-mediated phase transformation in a batch crystallizer

Tang

Shen

Shi

2011

J. Wuhan Univ. Technol.-Mat. Sci. Edit.

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“…Because of the inherent relation between nucleation and growth of a crystal to the extent of supersaturation, the optimum supersaturation has to be such that the desired objectives are achieved while respecting design and operating constraints. Although there are many studies that manipulate only the temperature profile to get the optimum product properties, [11][12][13][14][15] there are many other experimental as well as simulation approaches that use seed amount and size as an additional variable to manipulate product properties. [16][17][18][19][20][21] This gives an additional degree of freedom in attaining the optimal configuration.…”

Section: Introductionmentioning

confidence: 99%

“…However, the most widely used method for solving the population balance equation for use in optimization and control is a scheme based on the first few moments of the crystal size distribution 32,35 although there are a few studies involving use of a high resolution models for optimization. 15,30 The scheme based on moments is, however, faster, gives fairly good representation of the process, and is used in this study. In fact, Angelov et al 32 have used the moment based model for optimization of the same preferential crystallization system in their latest single objective optimization framework and this study is the extension of their work to a more comprehensive four objective optimization framework.…”

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confidence: 99%

Preferential crystallization: Multi‐objective optimization framework

Bhat¹,

Huang²

2009

AIChE Journal

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in Wiley InterScience (www.interscience.wiley.com).A four objective optimization framework for preferential crystallization of D-L threonine solution is presented. The objectives are maximization of average crystal size and productivity, and minimization of batch time and the coefficient of variation at the desired purity while respecting design and operating constraints. The cooling rate, enantiomeric excess of the preferred enantiomer, and the mass of seeds are used as the decision variables. The optimization problem is solved by using adaptation of the nondominated sorting genetic algorithm. The results obtained clearly distinguish different regimes of interest during preferential crystallization. The multi-objective analysis presented in this study is generic and gives a simplified picture in terms of three zones of operations obtained because of relative importance of nucleation and growth. Such analysis is of great importance in providing better insight for design and decision making, and improving the performance of the preferential crystallization that is considered as a promising future alternative to chromatographic separation of enantiomers.

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“…The crystallization model was based on solving the population balance and the mass balance by using Matlab™ programming [7]. The population balance equation for a batch crystallizer can be described by the following equation: ( , ) ( ( , ) ( , )) 0…”

Section: Crystallization Modelmentioning

confidence: 99%

“…Crystal size distribution (CSD) plays an important role in the quality of this anti-platelet drug. Solubility of the product and the temperature profile are important factors that affect the CSD [7]. The solubility of the CPG in solvent used for the crystallization process was determined experimentally by using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR) instrument.…”

Section: Introductionmentioning

confidence: 99%