Siemens Aktiengesellschaft

Siemens Aktiengessellscaft

Otto Gramckow

Thomas Martinetz

Guenter Soergel

Einar Broese

Martin Schlang

Thomas Poppe

Klaus Weinzierl

Friedemann Schmid

Gunter Sorgel

G&uuml;nter S&ouml;rgel

Frank-Oliver Malisch

Einar Br&ouml;se

Peter Protzel

Birger Schmidt

Hans-Herbert Welker

Thomas Heimke

R&uuml;diger D&ouml;ll

Guenter S&ouml;rgel

Markus Schubert

Michiaki Taniguchi

Martin Post

Franz Hartleb

Michael Jansen

Friedmann Schmid

Rolf-Martin Rein

Lars Kindermann

A method and device for the automation of a cooling section in a hot strip rolling mill, wherein an individual course of cooling over time for each strip point of the metal strip is specified and whereby the cooling specifications can be determined from the desired properties of the steel, independent of variable process value.

Method for controlling and/or regulating the cooling stretch of a hot strip rolling mill for rolling metal strip, and corresponding device

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.

Learning process for a neural network

Method for influencing relevant quality parameters of a rolling strip, particularly the profile or flatness of the rolling strip, in a roll stand with rolls, by adjusting the crownings of the rolls, i.e., the surface geometry of the rolls in the longitudinal direction of the rolls, wherein the crowning of the rolls is adjusted by an adjustable cooling of the rolls or of their surfaces in longitudinal direction of the rolls. The cooling of the rolls is adjusted by a controller ( ) as a function of the actual value (p ) of the crowning and a predetermined setpoint value (p ) of the crowning.

Method and device for influencing relevant quality parameters of a rolling strip

A method for determining the thermal material properties of metal shaped parts from a model is disclosed, which describes the thermal material properties of the metal shaped part. At least one thermodynamic parameter (p) is formed as a linear combination consisting of at least one base function (hi) and of at least one weighting factor (gi), whereby the base function (hi) describes the thermal material properties, and the weighting factor (gi) takes the influence of the alloying elements on at least one thermodynamic parameter (p) into account. The method makes it possible to conduct a sufficiently precise determination of the thermal material properties with a smaller time requirement.

Method for determining the thermal materials properties of shaped metal parts

Method for determining an intermediary profile of a metal strip between an upstream and a downstream roll stand in a mill train having at least two roll stands is described intermediary profile (p ) and the final profile (p ) of the metal strip downstream from the downstream roll stand are determined using an analytical model with parameters. The final profile is determined using a final profile model. At least one parameter is modified so that the difference between the final profile, determined using an analytical model, and the final profile, determined using the final profile model, is reduced.

Method and device for determining an intermediary profile of a metal strip

A process for controlling a metallurgical plant for producing steel or aluminum, in particular a rolling mill, steel or aluminum having specific material properties that depend on the structure of the steel or aluminum being produced in the metallurgical plant from input materials, and the material properties of the steel or aluminum being a function of operating parameters with which the plant is operated, the operating parameters being determined by a structure optimizer as a function of the desired material properties of the steel or aluminum.

Process and device for controlling a metallurgical plant

The invention relates to an automation system for the erection and operation of industrial plants, in particular for the design, project engineering, implementation, commissioning, maintenance and optimization of individual plant components or complete plants in the basic materials industry, which plants have a computer-based control system which, for a description of the process in control engineering terms, has recourse to process models, for example in he form of mathematical/physical models, neural network models or knowledge-based system, in order to develop the system to the extent that straightforward and cost-effective decentral process management and optimization may be achieved remote from the plant, decentralized process management and optimization by means of one or more interlinked control points is proposed, process changes are continuously monitored online or offline or at least checked by modelling, using modern, public communication means, and the process models, parameters and software are adaptable specifically to the plant.

Method for automatic operation of industrial plants

A method for identifying or predicting process parameters of an industrial process, in particular a primary-industry plant, having especially quickly varying process parameters or disturbances affecting the process, with the process parameters to be identified being determined by a process model as a function of measured values from the process, and with the process model having at least one time-invariant or one largely time-invariant process model which represents an image of the process averaged over time, and at least one time-variant process model that is adjusted to at least one time constant of a disturbance or of a variation in parameters of the process.

Process and device for identification or pre-calculation of parameters of a time-variant industrial process

The width of bands to be laminated on a mill train is adjusted by vertical upsetting rollers, resulting, however, in a narrowing at the band ends due to the asymetric material flow there. In order to solve the problem, the upsetting rollers are so designed as to move at the passage of the band ends in keeping with a curve defined according to specified parameters. The parameters are based on neuro-computer made predictions related to the milling process.

Optimizing the band width at the band ends on a mill train

Method and apparatus for controlling the cooling of a strand in a continuous casting installation, in which the cooling or, respectively, the solidification behavior of the strand ensures by means of the quantity of cooling agent used for cooling the strand, whereby the required quantity of cooling agent is determined, constantly and in real time, dependent on a predetermined target temperature distribution. A cooling model is used with which a temperature distribution in the strand can be calculated from a quantity of cooling agent.

Method and apparatus for controlling the cooling of a strand in a

A system for calculating an output of a multi-stage forming process using a model of the forming process with which the output of the forming process is determined as a function of properties of the forming process. Selected properties of the forming process are determined using a neural network-based information processing arrangement.

System for calculating an output of a multi-stage forming process

Process and device for influencing the profile of a rolled strip in a roll stand by affecting the camber of the rolls, i.e., the surface geometry of the rolls in the longitudinal direction of the rolls. The camber of the rolls is affected by influencing the temperature profile of the rolls or their surface in the longitudinal direction of the rolls. In one embodiment, the temperature profile of the surfaces of the rolls is controlled by cooling the surfaces of the rolls.

Process and device for influencing a profile of a rolled strip

In the control of a material-processing process in a regulated system, a preliminary adjustment of the system takes place at the beginning of each process cycle as a function of a precalculated process parameter. A material characteristic which is relevant for the process and which in turn is dependent on state variables (such as the composition of the material and its temperature), is included in an advance calculation of the process parameter. The relationship between the state variables and the material property is modelled in a neural network which forms a prediction value for the material property on its output side. As a function of the deviation between the prediction value and an actual value for the material property which is determined based on measuring the process parameter during the process cycle, an adaptive change of the network parameters takes place in the sense of reducing this deviation.

Method and control device for controlling a material-processing process

In rolling a metal strip in a roughing line and a finishing line, the rolling process in the roughing line is adjusted as a function of a predicted value for the change in width of the metal strip in the finishing line such that the metal strip has a given specified finished strip width on leaving the finishing line. In order to permit a reliable prediction of the change in width despite the lack of accurate information regarding the dependence of the change in width on influencing parameters that affect the process, this dependence is simulated in a neural network whose network parameters are adapted after each passage of a metal strip through the finishing line as a function of the influencing parameters measured or calculated during the passage and the measured actual change in width.

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Uttenreuth, Germany

Otto Gramckow