Siemens Aktiengesellschaft

Einar Broese

Otto Gramckow

Thomas Martinetz

Guenter Soergel

Martin Schlang

Thomas Poppe

Thomas Peuker

Frank-Oliver Malisch

Klaus Weinzieri

Bjoern Feldkeller

The invention relates to a method for controlling an industrial process for manufacturing or processing a product, in particular for controlling the cooling line of a hot-rolling mill, said cooling line being simulated by a physical model. To optimize the cooling process, a correction factor is determined in a simple manner, whereby the product data records are saved with the correction factors that have been determined previously. For a new strip to be cooled, the saved product data records are compared with the product data of the new strip and similar data records are extracted. A new correction factor is then determined, e.g. by means of linear regression, from the correction factors of the similar product data records.

Method for controlling an industrial process

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.

Learning process for a neural network

In a method for determining the rolling force in a roll stand for rolling metallic material which is to be rolled, the rolling force is determined by means of at least one neural network. According to the invention, the neural network is trained using, in particular measured, values for the rolling force under different operating conditions with a view to improving the determination of the rolling force. The neural network is advantageously trained using values for the rolling force and values for the different operating conditions for roll stands of different rolling trains.

Method and device for determining the rolling force in a roll stand

In known processes for conducting a process in an automatically controlled system, the system is preset at the beginning of each process run based on at least one process parameter. The at least one process parameter is precomputed with a model of the process, containing at least one model parameter and input values supplied to the model. During the process, the input values and the process parameter are measured and used to adaptively improve the precomputed process parameter after the process run. A neural network is used to determine the model parameters whose dependence on the input values is unknown or insufficiently known. Network parameters of the neural network are modified after each process run to adapt the model to the actual process events.

Method and device for conducting a process in a controlled system with

In known methods for conducting a process in an automatically controlled system, the system is preset at the beginning of each process run according to at least one process parameter. The at least one process parameter is precomputed with a model of the process which is supplied with input values. During the process, the input values and the process parameters are measured and are used after the process run to adaptively improve the precomputed value of the process parameters. To simplify and improve the precomputed value of a model having a plurality of partial models, computed results of the partial models are supplied to a neural network. The neural network produces the process parameters to be precomputed as a network response. The network parameters of the neural network are modified after each process run to adapt the precomputed value to the actual process events.

Known methods for conducting a process in an automatically controlled system preset the system at the beginning of each process run based on at least one process parameter. The process parameter is precomputed with a model of the process which is supplied with input values. During the process the input values and the process parameter are measured and are used to adaptively improve the precomputed process parameter after the process run. The present invention simplifies and improves the precomputed value of the process parameter by supplying at least part of the input values to a neural network. The network response of the neural network forms a correction value for the approximate value delivered by the model for the process parameter to be precomputed. The network parameters of the neural network are modified after each process run to adapt the precomputed value to the actual process events.

Growing community of inventors

Erlangen, Germany

Einar Broese