Neural networks and multivariate SPC

Recent developments in the instrumentation of plants has led to multivariate statistical process control (MSPC) techniques becoming increasingly popular for process monitoring in the chemical industry over the last few years. This paper examines one such algorithm, the partial least squares (PLS), and shows how the basic principles of this linear technique can be extended into the nonlinear domain via the application of radial basis function (RBF) neural networks. Results showing the successful application of these methods to fault detection in a validated model of an industrial overheads condenser and reflux drum plant are also given.