The present invention concerns a method for conducting a process in an automatically controlled system. A mathematical model of the process, with at least one variable model parameter, is implemented in a computing means. Before the process is started, the model precomputes at least one selected process parameter according to input values supplied to it for presetting the system. The input values and the process parameter are measured during the process and the precomputed value of the process parameter is adaptively improved after the process based on the measured input values. Furthermore, the present invention concerns a device for implementing a method for conducting a process.
A method and a device for conducting a quasi-continuous process in an automatically controlled system are discussed in German Patent Application No. 40 40 360. Such processes typically include rolling trains where each pass of a rolled strip forms a process cycle (hereinafter, "process run"). Like all actual industrial processes, these process runs are time-variable. To conduct such processes, the system controlling the process must be preset before each run. That is, unlike traditional closed-loop control, control must precede the actual process because in industrial processes, controlled values can often be measured only indirectly and not at the point of affection of the process. Consequently, direct closed-loop control is not possible.
The system controlling the process is preset in a known manner by precomputing selected process parameters according to pre-established input values, or initially estimated input values, or both, based on a pool of relevant mathematical models of the process and by presetting the system using the precomputed parameters. Since mathematical models of the process to be conducted can only approximate the actual process, the model must be adaptively matched to the actual process events. For this, the process parameters and the input values are measured directly or indirectly by determining other measured values during each process run. After the process run is complete, the precomputation performed with the mathematical models is repeated within the framework of a postcomputation done based on the input values subsequently measured. The variable model parameters are adaptively modified based on the deviation between the process parameters thus computed and the measured process parameters to reduce the deviations obtained. The model parameters thus adapted are available at the beginning of the following process run for precomputing the process parameters.
With very complex relationships, partial aspects of the process to be modeled are usually described in partial models, and the interaction among the partial aspects of the process is described by a higher order model that links the partial models. While the partial models are still capable of describing the respective partial aspects of the process with sufficient accuracy, model assumptions for linking the partial models are difficult to make and such models may be subject to serious errors. In particular, adapting the higher-order model in addition to adapting the partial models is very difficult because the partial models provide no exact measured values, but only estimated values as input values for the higher-order model.
The goal of the present invention is to improve the precomputed value of the process parameter in the case of a very complex relationship between the process parameter and the input values.