Preferential crystallization: Multi‐objective optimization framework

Abstract

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.

Inversionsbasierte optimale Steuerung von Batch-Kristallisationsprozessen zur Enantiomertrennung

“…Die für imp des ∈ {10 −4 , 10 −6 } und 3 , ,des ∈ {200 cm 3 , 300 cm 3 } resultierenden Wertepaare ( , 3 , , ) bzw. ( , 3 , ) und (imp , 3 …”

“…B. [1,3,13,15,16,29]. In [1] wird ein Temperaturprofil zur Maximierung der kristallisierten Menge des bevorzugten Enantiomers unter Beachtung der zuläs-sigen Verunreinigung bestimmt.…”

“…In [1] wird ein Temperaturprofil zur Maximierung der kristallisierten Menge des bevorzugten Enantiomers unter Beachtung der zuläs-sigen Verunreinigung bestimmt. In [3] wird der Prozess unter Berücksichtigung konkurrierender Ziele optimiert. Der vorliegende Aufsatz baut auf Vorarbeiten [15,16] auf, die Verfahren zur dynamischen Inversion von Batchprozessen der bevorzugten Kristallisation behandeln, um mittels geeigneter Stellgröẞenprofile vorgegebene Kristallgröẞen-verteilungen (KGVen) zu realisieren.…”

Integrating data‐based modeling and nonlinear control tools for batch process control

“…The inapplicability of step test based tuning techniques in batch processes negatively impacts the control performance of trajectory tracking approaches using proportional‐integral‐derivative controllers. In trajectory tracking predictive control approaches,1–8 with modeling errors and process noise, a process trajectory deemed optimal in off‐line calculations may be significantly sub‐optimal, or even infeasible in online implementation. In end‐point based MPC, a dynamic optimization (DO) problem that incorporates the final product quality specifications (in the objective function and/or constraints) is solved at each sampling instance until batch termination.…”