DocumentCode :
693158
Title :
Research of process parameters of molecular distillation on product purity based on rough sets neural network
Author :
Ke-Ping Liu ; Jian-Peng Zeng ; Quan Tao ; Chang-Hong Jiang
Author_Institution :
Coll. of Electr. & Electron. Eng., Changchun Univ. of Technol., Changchun, China
Volume :
01
fYear :
2013
fDate :
14-17 July 2013
Firstpage :
320
Lastpage :
326
Abstract :
Molecular distillation is a complex nonlinear chemical production process of which its mechanism is complex and contains several tight coupling variables, therefore it is difficult to establish precise mathematical model. Based on on product yield factors of the molecular distillation, this paper proposes a product purity prediction model based on rough sets and neural network theory, also the realization process is given. And the specific effects on manufacturing process from molecular distillation parameters are analyzed by simulation, the simulation result proves that the product purity prediction model based on rough sets and neural network theory accelerates the learning speed and improves prediction accuracy.
Keywords :
chemical engineering computing; chemical industry; distillation; neural nets; product development; production engineering computing; rough set theory; complex nonlinear chemical production process; molecular distillation; neural network theory; product purity prediction model; product yield factor; rough sets; DH-HEMTs; Erbium; Lenses; BP neural network; Molecular distillation; Prediction model; Rough sets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2013 International Conference on
Conference_Location :
Tianjin
Type :
conf
DOI :
10.1109/ICMLC.2013.6890488
Filename :
6890488
Link To Document :
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