Siemens Aktiengesellschaft

Siemens Aktiengessellscaft

Frank-Oliver Malisch

Otto Gramckow

Martin Schlang

Einar Broese

Method for controlling and preconfiguring a steelworks or parts of a steelworks, the rolling stand or the rolling mill train being controlled and preconfigured by means of a model of the rolling stand or the rolling mill train, the model having at least one neural network whose parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, the rate at which the parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, being varied.

Method for controlling and pre-setting a steelworks or parts of a steelworks

A learning process for a neural network for open-loop or closed-loop control of an industrial process with time-variable parameters. The neural network is configured either as an open-loop or closed-loop-control network with which the process is controlled. The neural network is trained with the current process data so that it builds a model of the current process. The neural network can also be configured as a background network which is trained during operation with representative process data so that it builds an averaged model of the process over a longer period of time. After a certain learning time or upon the occurrence of an external event, the open-loop or closed-control network is replaced by the background network.

Growing community of inventors

Neubiberg, Germany

Frank-Oliver Malisch