BAYES FOR ROLLING MILLS: FROM PARAMETER ESTIMATION TO DECISION SUPPORT

Bayes for Rolling Mills: from Parameter Estimation to Decision Support

The paper shares experience gained in application of dynamic Bayesian approach to control problems in the field of metal rolling. The contribution introduces basic notions of theory applied and provides the algorithmic as well as application-oriented solutions developed. Specifically, the consistent use of the approach resulted in an advanced decision support system for operators of a reversing...

Decision Support for Pond Aquaculture: Parameter Estimation for Simulation Models

Users of the POND decision support system may often be interested in tailoring the models to one or more fish species at a given location. Manual calibration of the fish bioenergetics model in POND for Nile tilapia (Oreochromis niloticus) growth has previously been reported (Bolte et al., 1995). However, estimating suitable parameters for complex, non-linear simulation models by manual calibrat...

Bayes Parameter Estimation for the Bivariate

Incremental Support Vector Regression for Steering Hot Rolling Mills

Machine learning concepts, such as the support vector machine (SVM), have been theoretically discussed in great detail, but in industrial applications still rather old-fashioned solutions are preferred. To show that learning concepts improve industrial planning, scheduling, and steering processes, a cooperation with a steel manufacturer was initiated to build a prototype pass schedule calculato...

Reeling Mechanism for Wire Rod Rolling Mills