Monitoring of an industrial liquid-liquid extraction system with kernel-based methods
Date
2005
Authors
Jemwa G.T.
Aldrich C.
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
The behaviour of liquid-liquid extraction systems can be complex and as a result linear methods of process condition monitoring such as principal component analysis or partial least squares may not be able to detect and identify process faults when they occur. In contrast, kernel-based methods represent a general framework that can be used where linear methods do not perform satisfactorily. This is demonstrated in a case study on an industrial liquid-liquid extraction system, where a linear discriminant analysis problem is recast as a support vector machine learning problem. The support vector machine is subsequently used to extract features from the plant data that can be used for considerably more accurate fault detection than is possible with its linear equivalent or with other nonlinear methods. Fault identification can be accomplished from an analysis of the residuals of models using the features to reconstruct the original plant data. © 2005 Elsevier B.V. All rights reserved.
Description
Keywords
Correlation methods, Least squares approximations, Mathematical models, Monitoring, Neural networks, Nonlinear systems, Principal component analysis, Rheology, Vectors, Fault detection, Fault identification, Kernel-based methods, Liquid-liquid extraction, Process monitoring, Support vector machines, Solvent extraction
Citation
Hydrometallurgy
78
1-2 SPEC. ISS.
78
1-2 SPEC. ISS.