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

Abstract

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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