Thermodynamic Calculation of Hydrogen Chloride Generation in Blast Furnace Smelting Process

2014 ◽  
Vol 1033-1034 ◽  
pp. 1300-1304
Author(s):  
Bin Sheng Hu ◽  
Xiao Guang Liu ◽  
Yong Liang Gui ◽  
Kai Lv
Keyword(s):  
Blast Furnace ◽  
Sodium Chloride ◽  
Sulfur Dioxide ◽  
Nitrogen Dioxide ◽  
Hydrogen Chloride ◽  
Thermodynamic Calculation ◽  
Smelting Process ◽  
Blast Furnace Smelting ◽  
Furnace Shaft ◽  
Furnace Smelting

The generating pathways of hydrogen chloride in blast furnace smelting process may have two pathways: 1.sodium chloride and water react with phosphorus pentaoxide forming hydrogen chloride gas.2. Sodium chloride and water react with sulfur dioxide and nitrogen dioxide forming hydrogen chloride gas. Based on Thermodynamic calculation of hydrogen chloride generation in blast furnace smelting process, hydrogen chloride gas is generated in the upper part of blast furnace shaft ,the generating temperature main range from 300°C too 800°C.

scholarly journals Predictive control of Si content in blast furnace smelting based on improved SA-BP

2021 ◽  
Vol 69 (5) ◽  
pp. 155
Author(s):  
Wenhan Liu ◽  
Hongli Wang ◽  
Liye Shi
Keyword(s):  
Blast Furnace ◽  
Prediction Model ◽  
Lag Time ◽  
Point Of View ◽  
Smelting Process ◽  
High Dimensional ◽  
Furnace Temperature ◽  
Blast Furnace Smelting ◽  
Si Content ◽  
Furnace Smelting

Blast furnace smelting process is a highly complex nonlinear dynamic process, its purpose is to refining the quality of liquid iron. From the point of view of chemical reaction kinetics, the main chemical reactions in the blast furnace are as many as 108 kinds, and the high complexity is obvious. From the point of view of hydrodynamics, there are three-phase mixed compressible viscous fluids in the blast furnace smelting process. The hydrodynamic equation is nonlinear, high-dimensional and high-coupling. In addition, the blast furnace smelting process has the characteristics of time-varying, high-dimensional, distributed parameters and other characteristics of the complex conditions and the failure of the operation under the conditions of the test, making the blast furnace smelting process automation and furnace temperature precision control to become metallurgical workers face the problem. In this study, from the data point of view, to explore the furnace temperature can be characterized by [Si] content prediction. Based on the data of 1000 furnace blast furnace, an accurate and reasonable prediction model of Si content in blast furnace is established. First of all, through the calculation of correlation coefficients of various factors and the time trend diagram, the general lag time between each parameter and Si content in the process of blast furnace production is obtained. Through correlation and analysis of the correlation between lag time, before and after the two largest furnace, with improved simulated annealing algorithm to determine the optimal initial weights, the content of Si, the N furnace of S content, the quantity of coal and air PML FL as input, the contents of Si in n+1 furnace as output to establish dynamic prediction model of improved SA-BP neural network based. Finally, the data used to detect the model is brought into the model to be tested, the error is analyzed, and the error local map is used to express the error visually. The model is tested.

Efficiency Upgrading for Blast Furnace Smelting through Use of Iron Ore Raw Materials with Various Fluxing Degree

2020 ◽  
Vol 299 ◽  
pp. 681-686
Author(s):  
Boris Yur'ev ◽  
Vladimir A. Gol'tsev ◽  
Vyacheslav Dudko
Keyword(s):  
Blast Furnace ◽  
Iron Ore ◽  
Gas Permeability ◽  
Raw Materials ◽  
Raw Material ◽  
Smelting Process ◽  
Blast Furnace Smelting ◽  
Failure Characteristics ◽  
Optimal Values ◽  
Furnace Smelting

Firing of various basicity pellets and sintering of Kachkanar concentrates were evaluated on a pilot plant. Mineral analysis was carried out for fired pellets and those reduced in a derivatograph in gas with composition close to that of blast furnace gas. Structure and metallurgical property dependence of pellets and agglomerate on their basicity was studied. Failure mechanism of fluxed pellets was considered. Blast furnace smelting process was developed for Kachkanar iron ore raw material with various fluxing degree, consisting of low-basic pellets, and high basic agglomerate characterized by low failure characteristics at reduction and ensuring high gas permeability in blast furnace top. Optimal values of agglomerate basicity, carbon content in burden and ferrous oxide in agglomerate, ensuring its high reducibility and adequate reduction degree index, were determined. Optimal ratio of pellets and agglomerate in a blast furnace, which implementation contributes to improved furnace performance, was detected.

Blast Furnace Smelting by Water Gas

1900 ◽  
Vol 50 (1296supp) ◽  
pp. 20771-20772
Author(s):  
C. C. Longridge
Keyword(s):  
Blast Furnace ◽  
Blast Furnace Smelting ◽  
Water Gas ◽  
Furnace Smelting

Recycling metallurgical waste in blast-furnace smelting

2007 ◽  
Vol 37 (10) ◽  
pp. 898-899 ◽  
Author(s):  
L. D. Nikitin ◽  
V. A. Dolinskii ◽  
S. A. Kudashkina ◽  
L. V. Portnov ◽  
S. F. Bugaev
Keyword(s):  
Blast Furnace ◽  
Metallurgical Waste ◽  
Blast Furnace Smelting ◽  
Furnace Smelting

Influence of the consumption of pellets with different basicity on indicators of blast-furnace smelting

2021 ◽  
pp. 4-10
Author(s):  
R. R. Dema ◽  
◽  
A. N. Shapovalov ◽  
S. N. Baskov ◽  
◽  
...  
Keyword(s):  
Blast Furnace ◽  
High Temperature ◽  
Pressure Drop ◽  
Optimal Level ◽  
Coke Rate ◽  
Specific Pressure ◽  
Blast Furnace Smelting ◽  
Gas Dynamic ◽  
Fluxed Pellets ◽  
Furnace Smelting

The results of the analysis of production data on the operation of blast furnace No. 1 (useful volume 1007 m3) of Ural Steel JSC for the period from 2013 to 2018 are presented. During this period, pellets from the Mikhailovsky GOK were used with varying degrees of fluxing: pellets of natural basicity in the ratio of CaO/SiO2 equal to 0.08 ± 0.02 units. (2013-2015) and partially fluxed pellets with a basicity of 0.52 ± 0.05 units. (from 2016 to the present). It has been established that the effectiveness of the use of pellets of various basicities is determined by their behavior in the blast furnace and depends on the proportion of pellets in the iron ore part of the charge. The gas-dynamic conditions of the smelting worsen with an increase in the proportion of pellets in the charge, which is accompanied by an increase in the specific pressure drop and forces the flow rate to be adjusted. There is an optimal level of specific pressure drop (53–55 Pa per 1 m3 of blast per minute) for the operating conditions of blast furnace No. 1 of Ural Steel, which ensures the optimum combination of the melting characteristics. Deviation from the optimal level of pressure drop leads to an increase in coke rate and a decrease in the degree of CO use, which is associated with gas distribution disturbance. Due to the increase in high-temperature properties, the replacement of non-fluxed pellets with off-fluxed pellets improves the gas-dynamic conditions in the lower part of the mine (in the cohesive zone). This leads to a decrease in the total pressure drop and specific pressure drop at a constant flow rate of the blast, and is a reserve for melting intensification. To minimize coke rate and maintain the high-performance operation of blast furnaces of Ural Steel JSC, it is necessary to work on 40–45 % of fluxed or 20–25 % acid pellets in a charge. An increase in pellet consumption while maintaining the efficiency of blast-furnace smelting is possible only if their high-temperature properties are improved. The improvement of these properties is possible as a result of optimizing the basicity and increasing the MgO content, which affects the structure and properties of the silicate bond. This work is carried out within a framework of the government order (No. FZRU-2020-0011) of the Ministry of Science and Higher Education of the Russian Federation.

Influence of Partially Reduced Pellets on Technical and Economic Features of Blast-Furnace Smelting

2021 ◽  
Vol 51 (7) ◽  
pp. 461-467
Author(s):  
I. S. Bersenev ◽  
I. S. Vokhmyakova ◽  
A. V. Borodin ◽  
Yu. A. Chesnokov ◽  
S. P. Pigarev ◽  
...  
Keyword(s):  
Blast Furnace ◽  
Blast Furnace Smelting ◽  
Reduced Pellets ◽  
Furnace Smelting
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