AUTOMATED INFORMATION CONTROL SYSTEM FOR OPTIMAL PLANNING OF BLAST-FURNACE IRONMAKING

Abstract

Increasing competition in the markets for metallurgical products issues a challenge for enterprises to increase production efficiency in economic term. One of the most important segments of the metallurgical industry is considered to be blast furnace ironmaking, which accounts for more than 50% of the energy costs of a metallurgical enterprise; in addition, there is a large consumption of resources for manufacturing. Aim. The article is aimed at increasing the efficiency of blast furnace smelting both from an economic point of view and from the technological process itself through the introduction of an automated information-control system for optimal planning of the work of a blast furnace shop. The system is capable of recommending optimal production of a blast furnace shop in terms of the minimum cost of cast iron, as well as making recommendations to personnel to correct operating parameters of blast furnaces in order to increase productivity and reduce coke rate. Materials and methods. It is proposed to use artificial intelligence methods in work to achieve these goals. In particular, clustering and dimensionality reduction methods are used to determine the optimal operating modes of technological equipment, and the problem of increasing the efficiency of the blast furnace shop is solved using nonlinear programming methods to plan blast furnace ironmaking, taking into account technological and economic limitations. The initial data for setting up and operating of optimal planning system is the production statistics of a metallurgical enterprise, the sources of which are monitoring and control systems for technological processes, the laboratory analyses of charge materials and smelting products, as well as expert assessments of the technological equipment state. Results. The results of the optimal value of blast furnace ironmaking seeking in terms of the minimum cost of cast iron and maximum efficiency of blast furnace smelting as well as the coefficients of the influence of production factors on the main indicators of blast furnace ironmaking: the productivity of blast furnaces and coke rate are shown. Conclusion. The developed automated information-control system for optimal planning of blast furnace ironmaking can be used as a decision support tool for both foremen, the head of the blast furnace shop, and the economic services of an industrial enterprise. Specialists can apply the obtained results for master production schedule of the sinter-coke-blast furnace ironmaking.