Optimization and simulation of quality properties in paper machine with neural networks

The final quality of paper depends on many quality and process variables. It is very difficult to find theoretical rules of the behavior of paper properties when variables depend from each other and when the interdependencies are not linear. In this paper we present a neural network based system for estimating the final quality of paper from process measurements. Inverse computation of the network model is used to find a control action that will produce the desired quality. A separate self-organizing map is used to monitor the movement of the operating point of the process and to give a hint of the estimation error of the network. >