Optimization and simulation of quality properties in paper machine with neural networks

Author

Lampinen, Jouko ; Taipale, Ossi

Author_Institution

Dept. of Inf. Technol., Lappeenranta Univ. of Technol., Finland

Volume

6

fYear

1994

fDate

27 Jun- 2 Jul 1994

Firstpage

3812

Abstract

The final quality of paper depends on many quality and process variables. It is very difficult to find theoretical rules of the behavior of paper properties when variables depend from each other and when the interdependencies are not linear. In this paper we present a neural network based system for estimating the final quality of paper from process measurements. Inverse computation of the network model is used to find a control action that will produce the desired quality. A separate self-organizing map is used to monitor the movement of the operating point of the process and to give a hint of the estimation error of the network

Keywords

optimisation; paper industry; process control; quality control; self-organising feature maps; estimation error; inverse computation; neural network based system; nonlinear interdependencies; optimization; paper machine; process measurements; quality properties; self-organizing map; simulation; Computer networks; Control systems; Estimation error; Feedforward systems; Intelligent networks; Monitoring; Neural networks; Numerical models; Paper making machines; Proposals;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374818

Filename

374818