Applications of dynamic artificial neural networks in state estimation and nonlinear process control

Abstract

Since the mid-1980s interest in a major area of strategic research has emerged: that of artificial neural networks. The much wider availability and power of computing systems, together with new theoretical research studies, is resulting in expanding areas of 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 described here focus on 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 on-line estimation of polymer properties in an industrial continuous polymerisation reactor is presented. This approach leads to the implementation of an inferential control scheme that significantly improves process performance to market-driven grade changes. The generic properties of the approach are then demonstrated by transferring the technology to a totally different plant. The application is to the nonlinear predictive control of the pressure of a highly nonlinear, high purity distillation tower.