Surrogate modeling and control optimization of batch crystallization process of β form LGA

Abstract

To describe a quantitative relationship between the operating conditions of cooling crystallization process and product crystal size distribution (CSD), a surrogate modelling method based on the Gaussian process regression (GPR) is proposed by using only experimental data of batch crystallization process of β form L-glutamic acid (LGA). A modified design of experiments (DoE) is presented to reduce the number of batch crystallization experiments. Based on the surrogate model, an objective function reflecting the concentration of product CSD and desired yield is introduced to optimize these operating conditions. Experiments on the seeded cooling crystallization process of β-LGA are conducted to verify the effectiveness and advantage of the proposed method.