Parameters soft-sensing based on neural network in crystallizing process of cane sugar

Author

Lu, Ting ; Luo, Fei ; Mao, Zongyuan ; Wen, Shaochun

Author_Institution

Electron. & Inf. Eng. Inst., South China Univ. of Technol., Guangzhou, China

Volume

3

fYear

2002

fDate

2002

Firstpage

1944

Abstract

Due to the online measurement difficulties of some parameters in the crystallizing process of cane sugar, parameter soft-sensing methods based on neural network are proposed. We provide a back-propagation neural network and a recurrent neural network to respectively build the density of boiling sugar juice and the speed of sucrose crystallizing soft-sensing models. The simulation results are allowed to carry out comparisons of running time, approximation capability and generalization capability between these two kinds of network. The results suggest that these two kinds of soft-sensing models based on neural networks are all able to provide good approximations to actual process. Finally, we discuss the influence of sample data on our soft-sensing models.

Keywords

backpropagation; food processing industry; generalisation (artificial intelligence); recurrent neural nets; approximation capability; backpropagation neural network; boiling sugar juice; cane sugar; crystallization process; generalization capability; parameters soft-sensing; recurrent neural network; running time; soft-sensing models; sucrose crystallization; Automation; Crystallization; Electronic mail; Intelligent control; Intelligent networks; Neural networks; Recurrent neural networks; Sugar industry;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on

Print_ISBN

0-7803-7268-9

Type

conf

DOI

10.1109/WCICA.2002.1021423

Filename

1021423