DocumentCode
1803555
Title
Neural networks based chemical process models
Author
Hashem, Sherif ; Mathur, Anoop ; Famouri, Pariz
Author_Institution
Fac. of Eng., Cairo Univ., Egypt
Volume
6
fYear
1999
fDate
36342
Firstpage
3948
Abstract
Efficient process design and online process control to within statistical limits play vital roles in quality improvement, and often offer a competitive edge in today´s industry. We here investigate the use of artificial neural network (ANN) as a dynamic modeling tool. The ANN models are compared to traditional parametric regression models. The comparison covers various features offered by each modeling technique including model structure and accuracy measures
Keywords
chemical engineering computing; digital simulation; neural nets; process control; production engineering computing; statistical analysis; ANN; artificial neural network; chemical process models; dynamic modeling tool; efficient process design; online process control; parametric regression models; quality improvement; statistical limits; Artificial neural networks; Chemical processes; Chemical technology; Computer industry; Design engineering; Industrial control; Neural networks; Process control; Process design; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.830788
Filename
830788
Link To Document