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

Abstract 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 non-linear 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 identification were used.