Data-driven soft sensor modeling based on twin support vector regression for cane sugar crystallization. (January 2019)

• Record Type: Journal Article
• Authors: Meng, Yanmei; Lan, Qiliang; Qin, Johnny; Yu, Shuangshuang; Pang, Haifeng; Zheng, Kangyuan
• Is Part Of: Journal of food engineering. Volume 241(2019)
• Page Start: 159
• Page End: 165
• Publication Date: 2019-01
• Subjects: Data-driven -- Twin support vector regression -- Soft sensor -- Particle swam optimization -- Model parameters optimization -- Crystallization; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2018.07.035
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: Cane sugar crystallization is a complex physical and chemical process and is related with many parameters. Due to the restriction of technical condition, some key parameters such as mother liquor purity and supersaturation, cannot be measured directly by existing sensors. This hinders the implementation of automatic control in cane sugar crystallization seriously. To handle this problem, a data-driven soft sensor modeling based on twin support vector regression is proposed to estimate the mother liquor purity and supersaturation. Seven easy-to-measure variables are chosen as input, including vacuum degree, temperature, massecuite level, steam pressure, steam temperature, feeding rate and massecuite brix. Two difficult-to-measure variables are chosen as output, including mother liquor supersaturation and mother liquor purity. The model parameters are optimized by combining the particle swarm optimization and the ten-fold cross-validation method. Experimental result indicates that this method performs well in aspects of prediction, approximation, learning speed, and generalization ability compared with BP, RBF and ELM, and is proved to have great effectiveness and reliability in cane sugar crystallization control. Highlights: A data-driven soft sensor is constructed to measure the purity and supersaturation of the mother liquor of cane sugar. The model is based on twin support vector regression. The parameters are optimized by combining the particle swarm optimization and the 90% off cross validation method. Experiments indicates the model performs well in learning and prediction and is effective and reliable in crystallization control.