| abstract |
A real-time, on-line nuclear magnetic resonance (NMR) system, and related method, can predict various properties of interest of a sample of polymer material. A regression or neural network technique is used to develop a model based upon manipulated NMR output and a resin age factor which compensates for time dependent aging phenomena and enhance predictive accuracy of the model. In a preferred embodiment, the resin age factor is a function of elapsed cycle time prior to sample measurement, sample temperature at time of measurement, and/or sample form. The polymer can be a plastic such as polyethylene, polypropylene, or polystyrene, or a rubber such as ethylene propylene rubber. |