Neural networks in dynamic process state estimation and non-linear predictive control

The much wider availability and power of computing systems, together with new theoretical research studies, is resulting in expanding areas of neural network application. It is particularly significant in these circumstances that the extremely important aspects involved in developing complex industrial process applications is emphasised, especially where safety critical perspectives are prominent. Additionally, in complex processes it is important to understand that conventional feedforward networks imply that the manipulated process inputs directly affect the plant outputs. This is not true in complex processes where some manipulated inputs affect internal states that go on to affect the system outputs. A further complication in complex industrial processes is the display of direction dependent dynamics. The studies discussed in this paper describe the application of a dynamic network topology that is capable of representing the directional dynamics of a complex chemical process. An application of neural networks to the online estimation of polymer properties in an industrial continuous polymerisation reactor is presented.