Data-driven modeling of product crystal size distribution and optimal input design for batch cooling crystallization processes. (December 2020)

• Record Type: Journal Article
• Authors: Liu, Jingxiang; Liu, Tao; Chen, Junghui; Yue, Hong; Zhang, Fangkun; Sun, Feiran
• Is Part Of: Journal of process control. Volume 96(2020)
• Page Start: 1
• Page End: 14
• Publication Date: 2020-12
• Subjects: Batch cooling crystallization processes -- Data-driven model building -- Double-layer parameter estimation -- Crystal size distribution -- Optimal input design; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.10.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In this paper, a novel data-driven model building method is proposed for predicting one-dimensional product crystal size distribution (CSD) or chord length distribution (CLD) of batch cooling crystallization processes, based on only batch run data. The proposed model relating the manipulated variable of cooling rate to the product CSD are constructed by two classes of basis functions, one is the wavelet basis function for reshaping the CSD and the other is the polynomial basis function for weighting the chosen wavelet basis functions to reflect the nonlinear relationship between the input and the density of individual crystal size among the product crystals. Correspondingly, a double-layer least-squares algorithm is established to estimate the model parameters, along with an adaptive strategy to determine the location and number of wavelet basis functions. By introducing an objective function that combines the information entropy of product CSD and the sample deviation of product crystals in each batch with respect to the target crystal size, the optimal input design of cooling rate for the desired product CSD is carried out by using a particle swarm optimization (PSO) algorithm to solve the non-convex optimization problem with the established CSD model. Simulation tests on the hen-egg-white lysozyme crystallization process along with experiments on the L-glutamic acid cooling crystallization process are performed to demonstrate the effectiveness and advantage of the proposed method. Highlights: Novel data-driven model for predicting product crystal size distribution (CSD). Double-layer model building by two classes of basis functions. An adaptive strategy for choosing the basis functions. Optimal input design for obtaining the desired product CSD. A synthetic quality index combining the information entropy of CSD and target size.