Institute of Information Theory and Automation

Bayesian soft sensor: a tool for on-line estimation of the key process variable in cold rolling mills

One of the key objectives of any rolling mill control system is to keep the thickness of the processed material within the prescribed tolerance band, which can be as low as +-10 micrometers for thin strips. Failure to comply with the tolerances results in losses which, according to experts estimate, might go up to 10% of the profit for poorly equipped rolling mills. Unfortunately, no practical direct measurement of the gauge within the rolling gap is possible. Strip thickness can be measured 50--100cm after the rolling gap with a high transport delay (20--120 samples). Thickness measurement devices minimizing or eliminating the delay are very expensive, therefore other ways of output thickness estimation/prediction are utilized. The rolling process is modelled using either well-established time-proven principles or simple black-box linear regressive models. The models are treated as probablilistic mixture and their thickness predictions are merged by the dynamic model weights in the mixture, respecting instantaneous operating state of the plant. At the same time, measurement methodology of several quantities (speed, pressure, ...) has been revised and improvement in precision and reliability indication has been achieved.

This application is a joint effort of ÚTIA, Compureg Plzeň, Jožef Stefan Institute and INEA, both from Ljubljana, Slovenia.

Kontakt: Jirsa, Dedecius

Responsible for information: general
Last modification: 26.10.2012
Institute of Information Theory and Automation