A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing

To overcome the large time-delay in measuring the hardness of mixed rubber, rheological parameters were used to predict the hardness. A novel Q-based model updating strategy was proposed as a universal platform to track time-varying properties. Using a few selected support samples to update the model, the strategy could dramatically save the storage cost and overcome the adverse influence of low signal-to-noise ratio samples. Moreover, it could be applied to any statistical process monitoring system without drastic changes to them, which is practical for industrial practices. As examples, the Q-based strategy was integrated with three popular algorithms (partial least squares (PLS), recursive PLS (RPLS), and kernel PLS (KPLS)) to form novel regression ones, QPLS, QRPLS and QKPLS, respectively. The applications for predicting mixed rubber hardness on a large-scale tire plant in east China prove the theoretical considerations.

The hardness of mixed rubber is predicted by rheological parameters to overcome its large time-delay in measurement. A novel Q-based model updating strategy is proposed to track the time-varying properties. The application on a large-scale tire plant in east China shows that, in addition to improve the prediction performance, the strategy can reduce the time-delay for measurement of hardness from about 2–4 h to 2 min.Figure optionsDownload as PowerPoint slide