Using neural networks for fault detection in a distillation column
Source: International Journal of Computer Applications in Technology, Volume 32, Number 3, 26 October 2008 , pp. 181-186(6)
Publisher: Inderscience Publishers
Abstract: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.
Document Type: Research Article
Affiliations: 1: Departement Genie Chimique, Universite de Rouen France, Rue Lavoisier, 76130 Mont Saint Aignan Cedex, France. 2: Laboratoire de Genie Industriel et Production Mecanique, ENSA, BP 669, 60000 Oujda, Maroc
Publication date: October 26, 2008
- The International Journal of Computer Applications in Technology addresses issues of computer applications, information and communication systems, software engineering and management, CAD/CAM/CAE, numerical analysis and simulations, finite element methods and analyses, robotics, computer applications in multimedia and new technologies, computer aided learning and training.