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Modeling and Bayesian parameter estimation for semibatch pH‐shift reactive crystallization of l ‐glutamic acid
Author(s) -
Su QingLin,
Chiu MinSen,
Braatz Richard D.
Publication year - 2014
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.14481
Subject(s) - crystallization , nucleation , markov chain monte carlo , bayesian inference , chemistry , mathematics , thermodynamics , bayesian probability , physics , statistics
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 α‐ and β‐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. © 2014 American Institute of Chemical Engineers AIChE J , 60: 2828–2838, 2014