Neural-Networks-Based Fault Detection and Accommodation in a Chemical Reactor

Abstract This paper discusses how recurrent neural networks can be successfully used for the modelling, constrained multivariable predictive control and fault detection of a non-linear dynamic process. A chemical reactor is modelled via recurrent neural networks. This model is used to build a predictive controller and to detect and to accommodate sensor failures without physical redundancy. The residuals based on this model and the implementation of the incidence matrix detect and diagnosis the fault, whereas the output of the ith neural model accommodates for the failure by replacing the signal from the failed ith sensor with its estimate.