State Feedback Control for the Steering Process of Aluminum Hot Rolling Mills

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...

Neural Network Control for Rolling Mills

Worldwide, steel and aluminum production and manufacturing is still one of the major basic industries with a huge amount of material and energy consumption. Hence, optimization of the various process control schemes which are involved can lead to signiicant savings. Artiicial Neural Networks are a new information processing technique which provides a novel approach to process control problems a...

A Survey of the Looper-tension Control Technology in Hot Rolling Mills

Looper and tension control is important in hot strip mills because it affects the strip quality as well as strip threading. Moreover, the most difficult challange in the controller design and control performance comes from the interaction between looper angle and strip tension. Disturbances from several sources also cause a deterioration in control performance, and thus they should be rejected ...

Research on Crown & Flatness Allocation Strategy of Hot Rolling Mills

In order to present the reasonable crown of hot rolling mills, the bend force and shift position should be pre-computed in a reasonable manner, while maintaining a good profile. How to search the optimal crown trajectory in ratio crown range is the key problem. Crown and flatness allocation strategy is researched based on dynamic programming method which has been improved and optimized by the a...

GE Energy Mass Flow Control for Intercritical Rolling in Hot Mills