Optimization of β-L-glutamic Acid Crystallization Processes via Moving Horizon Estimation

In this paper, a process optimization method is proposed for cooling crystallization process of β form L-glutamic acid, based on the moving horizon estimation (MHE) of the moment information of crystal growth kinetics. By transforming the population balance equations (PBEs) of the process kinetic model into differential algebraic equations using the quadrature method of moments (QMOM), a modified MHE estimation algorithm with variable horizon is developed to estimate low-order moments reflecting the fundamental characteristics of cooling crystallization process. Based on these estimated moments, an optimization program is established to solve the optimal operation profile of cooling temperature, by imposing the constraints of moment and cooling rate for numerical computation. An illustrative example of seeded cooling crystallization process of β-LGA is studied, which demonstrates that the proposed MHE algorithm could obtain better accuracy compared to the classical state estimation methods of extended Kalman filter (EKF) and MHE. Moreover, the optimized result of target crystal size distribution (CSD) by the proposed method is significantly better than those of the conventional linear or programming cooling strategies in practice.