Authors:
J. Fernandez de Cañete
;
P. del Saz-Orozco
and
S. Gonzalez-Perez
Affiliation:
University of Malaga, Spain
Keyword(s):
Distillation control, software sensors, neural networks, genetic algorithms.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
;
Soft Computing
Abstract:
Many industrial processes are difficult to control because the product quality cannot be measured rapidly and reliably. One solution to this problem is neural network based control, which uses an inferential estimator (software sensor) to infer primary process outputs from secondary measurements, and control these outputs. This paper proposes the use of adaptive neural networks applied both to the prediction of product composition from temperature measurements, and to the dual control of distillate and bottom composition for a continuous high purity distillation column. Genetic algorithms are used to automatically choice of the optimum control law based on the neural network model of the plant. The results obtained have shown the proposed method gives better or equal performances over other methods such fuzzy, or adaptive control.