Robust Bayesian estimation of kinetics for the polymorphic transformation of <scp>L</scp>‐glutamic acid crystals

Hermanto, Martin Wijaya; Kee, Nicholas C. S.; Tan, Reginald B. H.; Chiu, Min‐Sen; Braatz, Richard D.

doi:10.1002/aic.11623

Cited by 66 publications

(71 citation statements)

References 53 publications

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“…Crystals retained in the sieve of 180 µm were selected. The CSD approximates to a normal distribution using Equation (34) (Hermanto et al, 2008). Table 3 includes the required parameters needed to solve Equation (34).…”

Section: Seed's Csdmentioning

confidence: 99%

A Study on Empirical and Mechanistic Approaches for Modelling Cane Sugar Crystallization

Bolaños¹

2018

Rev. Mex. Ing. Quim.

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This study presents the kinetic modelling for cane sugar batch crystallization. The study is based on two approaches to describe the nucleation (homogeneous primary and secondary) and growth kinetics, and the birth and death rates of crystals: a) empirical models with equations of type power law (TPL) and b) mechanistic approach with thermodynamic equations. Both approaches, describing the phenomena of crystallization at the microscopic level (by solving population balance equation by method of lines), were validated using experimental data from a pilot-scale plant. From the results of both approaches and experimental validation, we conclude that the mechanistic approach best represents the dynamic behavior of cane sugar batch crystallization under different operational conditions than the empirical approach (TPL). Keywords: crystallization, modelling, kinetics, mechanistic and empirical approaches. ResumenEste estudio presenta el modelado cinético para la cristalización por lotes de azúcar de caña. El estudio se basa en dos enfoques para describir las cinéticas de nucleación (primaria homogénea y secundaria), de crecimiento y las tasas de nacimiento y muerte de cristales: a) modelos empíricos con ecuaciones de tipo ley de potencias (TLP) y b) enfoque mecanístico con ecuaciones termodinámicas. Ambos enfoques describen los fenómenos de cristalización a nivel microscópico (con la solución del balance de población por método de líneas) y fueron validados usando datos experimentales provenientes de una planta escala piloto. De los resultados de ambos enfoques y la validación experimental, se concluye que el enfoque mecanístico representa mejor los comportamientos dinámicos de la cristalización por lotes de azúcar de caña bajo diferentes condiciones operativas con respecto al enfoque empírico (TLP). Palabras clave: cristalización, modelado, enfoque mecanístico y empírico.

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Section: Seed's Csdmentioning

confidence: 99%

A Study on Empirical and Mechanistic Approaches for Modelling Cane Sugar Crystallization

Bolaños¹

2018

Rev. Mex. Ing. Quim.

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“…with the intrinsic solubility of two polymorphs given by (13) where I c and z denote the ion strength and ion charge, respectively.…”

Section: Kspmentioning

confidence: 99%

“…Furthermore, the effect of a-form crystals on the secondary nucleation of bform crystals was also considered, to take into account that b-form crystals can nucleate from the surface of a-form crystals. 13 The growth and nucleation expressions are assumed to have similar dependencies on supersaturation as reported in past studies, 20,21 with a length-dependent term included in the crystal growth kinetic expression. The expressions for the crystallization kinetics are: a-form crystallization kinetics when S a > 1…”

Section: Crystallization Mechanisms and Kineticsmentioning

confidence: 99%

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Modeling and Bayesian parameter estimation for semibatch pH‐shift reactive crystallization of l‐glutamic acid

Chiu

Braatz

2014

AIChE Journal

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A mathematical model for semibatch pH-shift reactive crystallization of L-glutamic acid is developed that takes into account the effects of protonation and deprotonation in the species balance of glutamic acid, crystal size distribution, polymorphic crystallization, and nonideal solution properties. The crystallization mechanisms of a-and b-forms of glutamic acid are addressed by considering primary and secondary nucleation, size-dependent growth rate, and mixing effects on nucleation. The kinetic parameters are estimated by Bayesian inference from batch experimental data collected from literature. Probability distributions of the estimated parameters in addition to their point estimates are obtained by Markov Chain Monte Carlo simulation. The first-principles model is observed in good agreement with the experimental data and can be further used for model predictions in robust control strategies.

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“…Although the kinetic estimation can be a nontrivial task, accurate kinetic information is very important for obtaining a reliable process model for polymorphic crystallization. Such model can accelerate process design, finding optimal operating conditions as well as development of suitable control strategy [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Kinetics Estimation and Polymorphic Transformation Modeling of Buspirone Hydrochloride

Trifkovic¹,

Rohani²,

Sheikhzadeh³

2012

JCPT

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In this work, solvent-mediated polymorphic transformation of metastable Form II to stable Form I of Buspirone Hydrochloride (BUS-HCl) was studied. The polymorphic transformation was monitored using in-situ Raman spectroscopy. The solvent-mediated transformation process is governed by the dissolution of Form II and the nucleation and subsequent growth of Form I. The model parameters for each of these sub-processes were determined with the aid of experimental data including polymorphic fraction in solid phase, solute concentration, and the crystal size distribution. In order to estimate the kinetic parameters, independent seeded batch sets of experiments were conducted, first to estimate the growth rate of Form I, and consequently to estimate the secondary nucleation of Form I and dissolution rate of Form II. The experimental data showed that the secondary nucleation of Form I occurred slightly after the dissolution of Form II was initiated. The estimated parameters for growth, nucleation and dissolution rates were successfully implemented in the population model and validated with the experiments.

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Robust Bayesian estimation of kinetics for the polymorphic transformation of L‐glutamic acid crystals

Cited by 66 publications

References 53 publications

A Study on Empirical and Mechanistic Approaches for Modelling Cane Sugar Crystallization

A Study on Empirical and Mechanistic Approaches for Modelling Cane Sugar Crystallization

Modeling and Bayesian parameter estimation for semibatch pH‐shift reactive crystallization of l‐glutamic acid

Kinetics Estimation and Polymorphic Transformation Modeling of Buspirone Hydrochloride

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