Assessment and Identification of Undesired States in Chemical Semibatch Reactors Using Neural Networks


Assessment and Identification of Undesired States in Chemical Semibatch Reactors Using Neural Networks

Hessel, G.; Kryk, H.; Schmitt, W.; Seiler, T.; Weiß, F.-P.; Deerberg, G.; Neumann, J.

This paper presents a neural-network approach to operator-independent assessing the operational states of chemical semibatch reactors. The suitability of neural networks for process monitoring was investigated in a miniplant in which strongly exothermic chemical reference processes were carried out. Before being applied to state classification, the neural network classifiers first have be trained using process data of normal and abnormal sequences of reaction to establish a nonlinear decision model between process parameters and state classification. Afterwards, the trained classifiers can be used for process monitoring. Best results were reached with three-layer perceptron networks. For assessing the danger potential of fault states, separate perceptron networks for danger classification and for fault isolation were used.

Keywords: Fault diagnosis; Process identification; Supervision; Artificial intelligence; Classifiers; Neural networks; Chemical industry

  • Lecture (Conference)
    Proceedings of IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes - SAFEPROCESS'2000, Budapest, 14-16 June, 2000, pp. 458-461
  • Contribution to proceedings
    Proceedings of IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes - SAFEPROCESS'2000, Budapest, 14-16 June, 2000, pp. 458-461

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Publ.-Id: 2905