DOI: 10.52150/2522-9117-2023-37-184-200

Kislyakov Volodymyr Hennadiiovych, Ph. D. (Tech.), Senior Researcher, Head of Department, Iron and Steel Institute of Z. I. Nekrasov National Academy of Sciences of Ukraine, Academican Starodubova Square, 1, Dnipro, Ukraine, 49107. ORCID: 0000-0002-1775-5050. E-mail: ovoch-isi@outlook.com

Togobitskaya Daria Mykolaivna, D. Sc. (Tech.), Professor, Head of Department, Iron and Steel Institute of Z. I. Nekrasov National Academy of Sciences of Ukraine, Academican Starodubova Square, 1, Dnipro, Ukraine, 49107. ORCID: 0000-0001-6413-4823. E-mail: dntog@ukr.net

Molchanov Lavr Serhiiovych, Ph. D. (Tech.), Head of Department, Senior Researcher, Iron and Steel Institute of Z. I. Nekrasov National Academy of Sciences of Ukraine, Academican Starodubova Square, 1, Dnipro, Ukraine, 49107. ORCID: 0000-0001-6139-5956. E-mail: metall729321@gmail.com

Yelisieiev Volodymyr Ivanovych, Ph. D. (Pys.-Math.), Senior Researcher, Iron and Steel Institute of Z. I. Nekrasov National Academy of Sciences of Ukraine, Academican Starodubova Square, 1, Dnipro, Ukraine, 49107. ORCID: 0000-0003-4999-8142. E-mail: ovoch-isi@outlook.com

Likhachov Yurii Mykhailovych, Researcher, Iron and Steel Institute of Z. I. Nekrasov National Academy of Sciences of Ukraine, Academican Starodubova Square, 1, Dnipro, Ukraine, 49107. ORCID: 0000-0003-3168-7813

ANALYSIS OF MODELS OF NON-AGGREGATE CAST IRON PROCESSING PROCESSES

Abstract. The aim of this paper is to perform a generalized analysis of studies on modeling the processes of out-of-furnace treatment of cast iron. Mathematical models are classified according to the basic principles of modeling. A description of different models based on different principles is given, depending on their type and the differences between them. A more detailed analysis of some of the fundamental models and expressions obtained in their construction is carried out. An example of models built on experimental data is given. Neural network models consist of artificial neurons that are connected to each other by means of connecting weights, i.e., model parameters, in the form of layers. Neurons are a set of mathematical functions that modify the input data to obtain an estimate of the desired result. A large number of network parameters makes training a neural network a cumbersome computational process. The large number of network connection weights that need to be optimized when training such models usually requires a large amount of input data. The paper presents domestic achievements in the construction of mathematical models of the out-of-furnace iron treatment process. The principles of creating an integrated database that summarizes information on the parameters of various technologies for desulphurization of cast iron, including the developed system unit of the information retrieval system, are described; the concept of an expert system for making decisions on process control and selection of a rational technology for out-of-furnace desulphurization of cast iron is developed; the variant of the developed information and mathematical support of the expert system with the module for out-of-furnace treatment of cast iron with granular magnesium and coinjection of magnesium and lime is described; the results of the study are presented. The paper describes the models devoted to numerical and physical modeling of the phenomena that occur in the ladle during injection, as well as to the study of the regularities of the gas-powder flow.

Key words: desulfurization, cast iron, model, classification, analysis.

DOI: https://doi.org/10.52150/2522-9117-2023-37-184-200

For citation: Kislyakov, V. H., Togobitskaya, D. M., Molchanov, L. S., Yelisieiev, V. I., & Likhachov, Y. M. (2023). Analysis of models of non-aggregate cast iron processing processes. Fundamental and applied problems of ferrous metallurgy, 37, 184-200. https://doi.org/10.52150/2522-9117-2023-37-184-200

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