Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification. Issue 5 (9th April 2019)

• Record Type: Journal Article
• Authors: Meng, Yanmei; Yu, Shuangshuang; Wang, Hui; Qin, Johnny; Xie, Yanpeng
• Is Part Of: Food science & nutrition. Volume 7:Issue 5(2019)
• Page Start: 1606
• Page End: 1614
• Publication Date: 2019-04-09
• Subjects: color value -- extreme learning machine -- gravity purity -- particle swarm optimization -- sugarcane juice clarification; Food industry and trade -- Periodicals; Food -- Periodicals; Nutrition -- Periodicals; 664
• DOI: 10.1002/fsn3.985
• Languages: English
• ISSNs: 2048-7177

Abstract

Abstract: Clarification of sugarcane juice is an important operation in the production process of sugar industry. The gravity purity and the color value of juice are the two most important evaluation indexes in the cane sugar production using the sulphitation clarification method. However, in the actual operation, the measurement of these two indexes is usually obtained by offline experimental titration, which makes it impossible to timely adjust the system indicators. A data‐driven modeling based on kernel extreme learning machine is proposed to predict the gravity purity of juice and the color value of clear juice. The model parameters are optimized by particle swarm optimization. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Compared with BP neural network, radial basis neural network, and support vector machine, the model has a good performance, which proves the reliability of the model. Abstract : A kernel extreme learning machine is used to predict the process of sugarcane juice clarification. The model parameters are optimized by particle swarm optimization. Experiments verify the effectiveness and superiority of the model.