Change Detection in a Distillation Column Based on the Generalized Likelihood Ratio Approach

Yahya Chetouani

Abstract

With increasing demands for efficiency, product quality, reliability and process safety, the field of fault detection (FD) plays an important role in chemical industries. This paper deals with a FD method based on the combination of Generalized Likelihood Ration Test (GLRT) and Artificial Neural Networks (ANNs). A reliable neural model in normal conditions, under all regimes (i.e. steady-state and dynamic conditions), is found by means of a NARX (Nonlinear Auto-Regressive with eXogenous input) model and by an experimental design. The efficiency of the combination of these two approaches used for detecting faults has been tested under real anomalous conditions on a real plant as a distillation column. From the experimental results, it is observed that the proposed FD is able to detect the process status effectively.