Control of a nine-stage three-component distillation column is considered. The control objective is achieved using a neural estimator and a neural controller. The neural estimator is trained to represent the chemical process accurately, and the neural controller is trained to give an input to the chemical process which will yield the desired output. Training of both the neural networks is accomplished using a recursive least squares training algorithm implemented on an Intel iPSC/2 multicomputer (hypercube). Simulated results are presented for a numerical example.
J. E. Steck et al., "Neural Modeling and Control of a Distillation Column," Proceedings of the International Joint Conference on Neural Networks (1991, Seattle, WA), pp. 771-774, Institute of Electrical and Electronics Engineers (IEEE), Jul 1992.
The definitive version is available at https://doi.org/10.1109/IJCNN.1991.155432
International Joint Conference on Neural Networks (1991: Jul. 8-12, Seattle, WA)
Mechanical and Aerospace Engineering
IEEE Technical Activities Board Council
International Neural Network Society
Keywords and Phrases
Control Systems, Nonlinear; Learning Systems; Neural Networks - Applications; Control Algorithms; Distillation Column Control; Learning Algorithms; Neural Network Controllers; Chemical Equipment
International Standard Book Number (ISBN)
Article - Conference proceedings
© 1992 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.