Data‐driven modeling for crystal size distribution parameters in cane sugar crystallization process. (18th January 2021)

• Record Type: Journal Article
• Authors: Meng, Yanmei; Yao, Tao; Yu, Shuangshuang; Qin, Johnny; Zhang, Jinlai; Wu, Jianfan
• Is Part Of: Journal of food process engineering. Volume 44:Number 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-18
• Subjects: Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; 664.005
• DOI: 10.1111/jfpe.13648
• Languages: English
• ISSNs: 0145-8876

Abstract

Abstract: Crystal size distribution (CSD) is important in evaluating crystal quality in cane sugar crystallization process. Due to the complex non‐linearity, time‐delay and strong coupling in cane sugar crystallization process, it is difficult to directly modeling in the mechanism of cane sugar crystallization process to obtain CSD parameters. In order to obtain two main CSD parameters so that to achieve better control and production of cane sugar, this article constructs a data‐driven model based on least squares support vector regression (LSSVR) and particle swarm optimization (PSO). Based on LSSVR, the model takes the easy to measureable variables (massecuite brix, massecuite level, massecuite temperature, steam pressure, feeding volume, and vacuum degree) as input variables, and outputs sugar CSD parameters (crystal average size, coefficient of variation of crystal size). PSO algorithm is used to optimize the key parameters of primary model to get better modeling performance. Compared with other modeling methods such as back propagation, extreme learning machine, radial basis function, and SVR, the constructed PSO‐LSSVR model has obvious advantages over other models in learning speed and predictive effect, generalization ability. This model has potential to be applied to the control system of cane sugar crystallization process and get better product of sugar. Practical applications: The constructed model uses the measureable variables to obtain main crystal size distribution (CSD) parameters (which is not easy to be measured online directly), and the measureable variables can be easy to control. According the relation between the measureable variables and CSD parameters, the model is useful to set up better operation conditions, and form better crystallization environment, and improve the quality of sugar crystal. So, this method will benefit the control system of cane sugar crystallization process and improve the quality and efficiency of cane sugar crystallization process, and the model is also useful for the study of continuous sugar crystallization process. Abstract : A model between the measurable parameters and sugar crystal size distribution parameters in cane sugar crystallization is constructed. The data‐driven modeling is based on least squares support vector regression (LSSVR) and particle swarm optimization (PSO) algorithm. The constructed PSO–LSSVR model has obvious advantages in learning speed and predictive effect, generalization ability.