Application of Artificial Neural Networks to Improve Steel Production Process

Igor Grešovnik, Tadej Kodelja, Robert Vertnik, and Božidar Šarler


Artificial neural networks, response approximation, continuous casting, steel manufacturing


The current work outlines application of a framework based on artificial neural networks and an integrated optimization module to adjustment of process parameters in steel production. The framework was originally developed for adjustment of parameters of material production processes in order to obtain desired outcomes, and was primarily intended for use in production of carbon nanomaterials in arc discharge reactors. Further development lead to more generalized procedures applicable to material production and processing. An example of optimizing process parameters in continuous casting of steel on basis of expert knowledge is presented. Further steps are made towards modeling of the whole process chain in the steel plant, rather than just the casting process. Such models are in the development stage, and some preliminary results are shown where the model is used for perform some parametric studies.

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