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Program complex for the state intellectual analysis and wear dynamics forecasting of the oxygen converter refractory lining

Keywords:

T.B. Chistyakova – Dr. Sc. (Eng.), Head of Department of Computer Design and Control, St. Petersburg State Institute of Technology (Technical University)
E-mail: nov@technolog.edu.ru
I.V. Novozhilova – Ph. D. (Eng.), Associate Professor, Department of Computer Design and Control, St. Petersburg State Institute of Technology (Technical University)
E-mail: novozhilova@bk.ru
V.A. Kudlay – Post-graduate Student, Department of Computer Design and Control, St. Petersburg State Institute of Technology (Technical University)
E-mail: viktor.kudlay@gmail.com
V.V. Kozlov – Ph. D. (Eng.), Associate Professor, Department of Chemical Technology of Refractory Non-metallic and Silicate Materials, St. Petersburg State Institute of Technology (Technical University)
E-mail: chemic@yandex.ru


The solution of the current problem of trouble-free operation of steel-smelting converters leads to the need for carrying out predictive and operational calculations that allow the most thorough investigation of the cause and effect relationships of the main technological parameters of the converter process, and also analyze the state of the refractory lining in order to assess compliance with the criteria for safe operation of the process. The aim of the work is to create a problem-oriented program complex that allows analyzing the results of laser scanning of the steelmaking converter refractory lining on the basis of a neural network, determining areas subject to the most severe wear, calculating the amount of materials needed for repair work to increase the working layer of the lining, and also to predict the maximum the duration of the converter during one campaign. The core of the program complex is a neural-fuzzy model that allows you to analyze the results of laser scanning for the current liner conversion campaign taking into account the choice of the type of hot repair. As a method of modeling the wear dynamics of converter lining wear, the method of artificial neural networks is used, which has a lower labor-consuming preparation of recommendations for the operation of converter lining and high accuracy in relation to the operator's work on manual analysis of scans. The proposed algorithm allows to determine the average residual thickness of the working layer of the converter lining, to localize the places of increased wear, to determine their area and volume, the recommended expense of repair materials, and also to keep a log of general and local wear of the refractory lining of the converter. The obtained estimates can be used to obtain statistical data and to reveal the patterns of destruction of the refractory lining of the converter, a comparative analysis of the quality of various refractory lining. The program complex is developed using modern information technologies and is oriented to work in various operating systems, including using modern mobile devices via the web interface. The use of the program complex in modern steelmaking plants makes it possible to increase the life of accident-free operation of converters, to significantly reduce the time spent processing laser scanning results and studies of the working layer of refractory lining, and to improve the professional level of the man-agement and production personnel of steelmaking. The program complex was successfully tested in the framework of the educational program for advanced training in the production and operation of innovative refractory materials developed by SPbSTI (TU) in cooperation with NUST MISiS, commissioned by the Fund for Infrastructure and Educational Programs of RUSNANO. The program complex can be implemented on foreign (more than 50) and Russian (more than 20) metallurgical plants that have both small and extra-large converters (Cherepovetsky, Magnitogorsk, Novolipetsk and other mills). Efficiency from the implementation of the program complex is achieved through solving the problem of resource-saving and safe control of the converter steelmaking process as part of the metallurgical production.

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