Abstract: 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 a

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.