Decision Support System for Controlling Thermal State of Blast Furnace Smelting

Development and Implementation of Decision Support Systems for Blast Smelting Control in the Conditions of PrJSC “Kamet-Steel”

“…This is the stability of the change in the RAFT (with an average value of over 2000 • C) and the parameters that determine it; the promptness of taking corrective actions at the first signs of a violation of the thermal state; change in the ore load in accordance with the trend of changes in the thermal state and with an increase step with an increase in the intensity of smelting, not exceeding 0.3 units; compliance with the release schedule; stability of coke quality indicators and chemical composition of agglomerate; satisfactory (without cluttering) condition of the hearths when coke screenings are used as part of the iron ore part of the charge; and the presence of heat reserves before the stops of the BF for repairs. In addition, it is necessary to take into account negative factors that exist at PrJSC "Kamet-steel", such as as the technical condition of the BFs (mainly the condition of the hearths), lack of automatic coke moisture control, no screening of agglomerate, instability of oxygen supplied for blast enrichment, instability of changes in the consumption of pulverized coal, high RMSD indicators of coke quality and chemical composition of the sinter, and delay in obtaining the results of the chemical analysis of melt products due to the peculiarities of the operation of the pneumomail [11].…”

“…In accordance with the knowledge base obtained during the research in 2018-2021 at PrJSC "Kamet-steel" [11,35,48,49], and as a result of the work performed in 2020-2021 (detailed analysis of violations of the thermal regime of BF smelting), a DSS was developed in the mode of an adviser for managing the thermal state of BFs of PrJSC "Kamet-steel". Screenshots of the DSS software are shown in Figure 1.…”

“…The third DSS (name adopted at PrJSC "Kamet-steel": "Expert Module"), implemented in 2021-2022 in the automatic control system of the BF shop in combination with the installation of means for measuring the temperatures of the gas flow above the surface of the charge materials (thermoprobes), will make it possible to reasonably and in a timely manner correct the parameters of the charging mode [2,11,12,[14][15][16]53].…”

“…Such computer systems are commonly referred to as decision support systems (DSS). An analysis of the literature data reveals a variety of both the use of mathematical models, model systems, expert modules, and monitoring systems [1][2][3][4][5][6][7] for decision support, as well as complex DSS in BF production [7][8][9][10][11].…”

“…Many years of experience in the theory and practice of controlling the thermal state of BF smelting, as a rule, were reduced to the development and implementation of forecasting methods [Si], but most of the known methods of forecasting in practice have not always had a high degree of accuracy due to the multifactorial influence of technological parameters on the thermal state of BF smelting and uncontrollability of some of them [11]. An analysis of literature sources reveals a variety of methods for predicting the silicon content of cast iron; there are methods based on support vector machines [23,24], neural networks [25][26][27], the partial least squares method [28], and other approaches to finding a solution for this problem [29][30][31][32].…”