Artificial neural network approach for flow regime classification in gas–liquid–fiber flows based on frequency domain analysis of pressure signals-Reference-Cited by-同舟云学术

Artificial neural network approach for flow regime classification in gas–liquid–fiber flows based on frequency domain analysis of pressure signals

Published:2004-06 Issue:11 Volume:59 Page:2241-2251
ISSN:0009-2509
Container-title:Chemical Engineering Science
language:en
Short-container-title:Chemical Engineering Science

Author:

Xie T.,Ghiaasiaan S.M.,Karrila S.

Publisher

Elsevier BV

Subject

Applied Mathematics,Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Reference39 articles.

1. Transportability of data between electronic noses;Balaban;Sensors and Actuators B,2000

2. The yield-stress of medium- and high-consistency mechanical pulp fiber suspensions at high gas contents;Bennington;Journal of Pulp and Paper Science,1995

3. Neural network based objective flow regime identification in air–water two-phase flow;Cai;Canadian Journal of Chemical Engineering,1994

4. Application of chaos theory in identification of two-phase flow patterns and transitions in a small, horizontal, rectangular channel;Cai;Journal of Fluids Engineering,1996

5. Diagnostics of gas–liquid flow patterns in chemical engineering systems;Drahos;Chemical Engineering and Processing,1989

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