Detecting Changes in a Distillation Column by Using a Sequential Probability Ratio Test

In chemical plants, a reliable detection of anomalies is important for a safe operation. To this end, a fault detection (FD) method of abnormal operations applicable to a chemical process is presented in this paper. This method couples an Artificial Neural Network-Multi-Layer Perceptron (ANN-MLP) with a statistical module based on the sequential probability ratio test (SPRT) of Wald, for the analysis of the process residuals. To detect a change, this combination uses the mean and the standard deviation of the residual noise obtained from applying a NARX (Nonlinear Auto-Regressive with eXogenous input) model. The FD effectiveness is tested under real abnormal circumstances on a real plant as a distillation column. The experimental results obtained show the relevance of this method for the fast detection and the monitoring of this chemical process. (c) 2011 Published by Elsevier B.V. Selection and/or peer-review under responsibility of the Organising Committee of The International Conference of Risk and Engineering Management.