Title :
Neural model predictive control of nonlinear chemical processes
Author :
Su, Hong-Te ; McAvoy, Thomas J.
Author_Institution :
Honeywell Inc., Minneapolis, MN, USA
Abstract :
There are two different neural network dynamic modeling approaches. A comparison of the two modeling approaches in terms of their model predictive control (MPC) performance is given. Two chemical processes, a polymer reactor and a distillation column, are studied
Keywords :
chemical industry; distillation; neural nets; nonlinear control systems; predictive control; process control; distillation column; dynamic modeling; neural model predictive control; neural network; nonlinear chemical processes; polymer reactor; process control; Artificial neural networks; Chemical processes; Distillation equipment; Inductors; Multilayer perceptrons; Neural networks; Polymers; Predictive control; Predictive models; Switches;
Conference_Titel :
Intelligent Control, 1993., Proceedings of the 1993 IEEE International Symposium on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-1206-6
DOI :
10.1109/ISIC.1993.397687
