DocumentCode :
381061
Title :
Heat integration of the azeotropic distillation system with ANN and GA
Author :
Ying, Li ; Yao, Wang ; Yan-min, Wang ; Ping-jing, Yao
Author_Institution :
Inst. of Process Syst. Eng., Dalian Univ. of Technol., China
Volume :
2
fYear :
2002
fDate :
2002
Firstpage :
1573
Abstract :
In this paper, an effective method with artificial neural networks (ANN) and a genetic algorithm (GA) was suggested for modeling the azeotropic distillation system with unknown or complex mechanisms and optimizing its operating parameters to save energy. The satisfactory results of this investigation demonstrated the feasibility and effectiveness of the suggested method. Furthermore, the azeotropic distillation system after optimization results in reduction of heat use by 54.03%. Thus, the study provides means for further optimization of the azeotropic distillation system, and directs practical production for process optimization.
Keywords :
backpropagation; chemical engineering computing; distillation; genetic algorithms; optimal control; ANN; artificial neural networks; azeotropic distillation system; complex mechanisms; genetic algorithm; heat integration; heat use reduction; operating parameters optimization; process optimization; robust optimization algorithm; three-layer backpropagation network; Artificial neural networks; Chemical industry; Energy consumption; Food industry; Fuel economy; Heat engines; Industrial economics; Mining industry; Modeling; Power generation economics;
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.2002.1020851
Filename :
1020851
Link To Document :
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