Title :
Neural networks based chemical process models
Author :
Hashem, Sherif ; Mathur, Anoop ; Famouri, Pariz
Author_Institution :
Fac. of Eng., Cairo Univ., Egypt
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.830788
