Monitoring of abnormal situations in continuous industrial processes. Case study: Multiple effect evaporation system

Monitoring abnormal situations in chemical industries is a worldwide challenge. The occurrence of this kind of event is common, however, its detection is generally after its development into a faulty condition. The earlier it is detected, the greater the chance to guarantee safe, economical and clean operations. This study aims to develop a reliable and automatic system to detect and diagnose abnormal situations. The monitoring system acts as a temporal pattern classifier, which is based on a dynamic neural network, namely a Time Delay Neural Network (TDNN). The proposed methodology was tested on a real benchmark from an evaporation station. An initial comparison showed its better performance over the static MultiLayer Perceptron (MLP) neural network. Its generalization capacity in distinguishing normal and abnormal operating conditions was attested, and a final inspection showed its capacity to absorb transitions between operating regions. The global average rates of correct classifications amount to 94.9% and 94.1%, respectively.