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Neural network models of the influence of the chemical composition of ore raw materials and parameters of melting processes on the properties of products

Studies of the influence of the chemical composition of ore raw materials on the properties of foundry products, as a rule, do not consider the features of production processes. The main reasons for this are that, firstly, the results of such research are usually limited to laboratory studies, secondly, the models do not take into account the specifics of the structure of ore raw materials, since these data can be obtained only after the completion of all thermophysical and chemical-energy-technological processes of heat treatment, thirdly, the use of models is difficult due to the need to promptly account for changes in the composition and values of systemic and external factors during heat treatment. The purpose of the paper is to design neural network models and tools that provide the possibility of adaptive structural and parametric adjustment to changes in the parameters of the analyzed processes of heat treatment of ore raw materials. Data on the chemical composition of ore raw materials and information on heat treatment processes are used as parameters of the projected neural network models. As a result of the research, the hypothesis about the possibility of indirect accounting for the influence of structural features of ore raw materials on the quality of products has been confirmed. Confirmation of this hypothesis will allow us to offer effective tools for operational management of thermophysical and chemical-energy-technological processes of heat treatment of ore raw materials. Read more...