Optimization of a Continuous Fluidized Bed Process for the Separation of Enantiomers by Preferential Crystallization

Enantiomers are molecules found in two variants being non-superimposable mirror images of each other. Chemical synthesis typically produces a 50:50 % mixture of both enantiomers. The separation of such a mixture is crucial for many applications, because enantiomer and counter-enantiomer often have completely different effects on biological organisms or the human body. However, the separation is a challenging task, as the physical properties of both enantiomers are identical. The presented work consists of two parts, which are based on model optimization. The first part of this paper describes the process of fitting the model to experimental data described in ( Gansch et al., 2020 ). The focus of the second part is on optimization considering two conflicting objectives: total harvested product mass and purity of the product. A sensitivity analysis presented in ( Gansch et al., 2020 ) is used as a basis for the selection of optimization variables. Some parameters have a significant impact, and their optimization allows to control not only the key characteristics as productivity and purity, but also the mean crystal size. The genetic algorithm CMAES ( Hansen, 2006 ) and parallel computing techniques are applied for optimization.