Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf
2 PublicationsFault Diagnostics in Chemical Semibatch Reactors Using Neural Networks
Hessel, G.; Schmitt, W.; van der Vorst, K.; Weiß, F.-P.; Neumann, J.; Schlüter, S.
Abstract
This paper presents a neural-network approach to early identifying dangerous states in chemical semibatch reactors. Data sets which were supplied both from a process simulator and from measurements in a laboratory reactor were used to train and test neural networks and a fuzzy pattern classifier for different normal and faulty states. Three-layer perceptron networks were found to be best suited for classifying different normal and abnormal process states. Even multiple fault states can be recognized by the perceptron network correctly.
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Lecture (Conference)
Proc. of the 5th European Congress on Intelligent Techniques and Soft Computing EUFIT 97, Aachen, Germany, September 8 - 11, 1997, pp. 1704 - 1708 -
Contribution to proceedings
Proc. of the 5th European Congress on Intelligent Techniques and Soft Computing EUFIT 97, Aachen, Germany, September 8 - 11, 1997, pp. 1704 - 1708
Permalink: https://www.hzdr.de/publications/Publ-1618