Using neural networks for fault detection in a distillation column

被引：6
|
作者：
Manssouri, I. [1 ]
Chetouani, Y. [1 ]
El Kihel, B. [2 ]
机构：
[1] Univ Rouen France, Dept Genie Chim, Rue Lavoisier, F-76130 Mont St Aignan, France
[2] ENSA, Lab Genie Ind & Prod Mecan, Oujda 60000, Morocco
来源：
INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY | 2008年 / 32卷 / 03期
关键词：
classification; distillation column; fault detection; process safety; radial basis function; RBF; reliability;
D O I：
10.1504/IJCAT.2008.020953
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Several methods of fault detection have been put to testing with the purpose of securing the installations and reducing the risks of accidents. This paper presents a new approach of fault detection based on the realisation of a Bayesian neural separate at radial basis functions. In this paper, our contribution consists of demonstrating the way this kind of network can be used as faults separate, applied to a continuous distillation column containing a binary mixture of toluene/methylcyclohexane. The latter is carried out through the use of test base containing two operating modes: normal and abnormal.
引用
收藏
页码：181 / 186
页数：6
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