High efficiency and clean emission operation of a blast furnace are mainly determined by its operating stability, which is closely related to the position of the cohesive zone (CZ) in the blast furnace. In order to monitor the CZ position in real time, a prediction model of the CZ was proposed by combining an off-line computational fluid dynamics (CFD) calculation and support vector machine (SVM) modelling. An axisymmetric two-dimensional steady-state CFD model was established to simulate the fluid flow, heat and mass transfer in the blast furnace shaft. The sample dataset of the CZ information was established based on the CFD off-line calculation, and the prediction of the CZ position was realized by SVM modelling. The results show that the established model can predict the CZ position in real time with high accuracy, providing effective guidance to the blast furnace operation and control.
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References
1.
CrewPCharltonM.The anatomy of a furnace and some of its ramifications. Metals Mines Studies Archaeometallurgy. 2007: 219–225.
2.
WrightBZulliPZhouZYet al.Gas–solid flow in an ironmaking blast furnace—I: physical modelling. Powder Technol. 2011;208(1):86–97. doi: 10.1016/j.powtec.2010.12.006
3.
ShenYGuoBChewSet al.Modelling of internal state and performance of an ironmaking blast furnace: slot vs sector geometries. Metall Mater Trans B. 2016;47(2):1052–1062. doi: 10.1007/s11663-015-0557-x
4.
ShiLZhaoGLiMet al.A model for burden distribution and gas flow distribution of bell-less top blast furnace with parallel hoppers. Appl Math Model. 2016;40(23–24):10254–10273. doi: 10.1016/j.apm.2016.07.024
5.
NickRSTillianderAJonssonLTIet al.Mathematical model of solid flow behavior in a real dimension blast furnace. ISIJ Int. 2013;53(6):979–987. doi: 10.2355/isijinternational.53.979
6.
ZhouZYZhuHPWrightBet al.Gas-solid flow in an ironmaking blast furnace–II: discrete particle simulation. Powder Technol. 2011;208(1):72–85. doi: 10.1016/j.powtec.2010.12.005
7.
FuDChenYZhaoYet al.CFD modelling of multiphase reacting flow in blast furnace shaft with layered burden. Appl Therm Eng. 2014;66(1–2):298–308. doi: 10.1016/j.applthermaleng.2014.01.065
8.
DongXFYuABChewSJet al.Modelling of blast furnace with layered cohesive zone. Metall Mater Trans B. 2010;41(2):330–349. doi: 10.1007/s11663-009-9327-y
9.
ZhouPLiHLShiPYet al.Simulation of the transfer process in the blast furnace shaft with layered burden. Appl Therm Eng. 2016;95:296–302. doi: 10.1016/j.applthermaleng.2015.11.004
10.
YilmazCWendelstorfJTurekT.Modelling and simulation of hydrogen injection into a blast furnace to reduce carbon dioxide emissions. J Clean Prod. 2017;154:488–501. doi: 10.1016/j.jclepro.2017.03.162
11.
WangHChuMZhaoWet al.Mathematical simulation on blast furnace operation of coke oven gas injection in combination with top gas recycling. Steel Res Int. 2016;87(5):539–549. doi: 10.1002/srin.201500372
12.
KuangSLiZYanDLet al.Numerical study of hot charge operation in ironmaking blast furnace. Miner Eng. 2014;63(8):45–56. doi: 10.1016/j.mineng.2013.11.002
13.
GuoTChuMSLiuZGet al.Numerical simulation on blast furnace operation with hot burden charging. J Iron Steel Res Int. 2014;21(8):729–736. doi: 10.1016/S1006-706X(14)60134-5
14.
ChuMSGuoXZShenFMet al.Numerical analysis of blast furnace performance under charging iron bearing burdens with high reducibility. J Iron Steel Res Int. 2007;14(2):13–19. doi: 10.1016/S1006-706X(07)60020-X
LiCLiSLiuY.A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting. Appl Intell. 2016;45(4):1–13. doi: 10.1007/s10489-016-0810-2
17.
WangHCFangHRMengLet al.A pre-warning system of abnormal energy consumption in lead smelting based on LSSVR-RP-CI. J Cent South Univ. 2019;26(8):2175–2184. doi: 10.1007/s11771-019-4164-x
18.
LiLLiHJ.Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry. J Cent South Univ. 2015;22:1437–1447. doi: 10.1007/s11771-015-2661-0
19.
ChenNLvJ.Study on boiler combustion optimization based on sparse least squares support vector machine. International Symposium on computational Intelligence & Design. 2016. p. 489–492.
20.
ShiPZhouPFuDet al.Mathematical model for burden distribution in blast furnace. Ironmak Steelmak. 2016;43(1):74–81. doi: 10.1179/1743281215Y.0000000052
21.
ZhangYZAiLQ.Iron and steel metallurgy process of mathematic analysis and simulation. Beijing: Metallurgical Industry Press; 1997; (in Chinese).
22.
AustinPRNogamiHYajiJ.A mathematical model of four phase motion and heat transfer in the blast furnace. ISIJ Int. 1997;37(5):458–467. doi: 10.2355/isijinternational.37.458
23.
FuYWBhatiaSK.A generalised dynamic model for char particle gasification with structure evolution and peripheral fragmentation. Chem Eng Sci. 2001;56(12):3683–3697. doi: 10.1016/S0009-2509(01)00060-4
24.
BiggsMJAgarwalPK.The ja:math product ratio for a porous char particle within an incipiently fluidized bed: a numerical study. Chem Eng Sci. 1997;52(52):941–952. doi: 10.1016/S0009-2509(96)00489-7
25.
ZhouPLiJLWenQQet al.Soft-sensing method of cohesive zone shape and position in blast furnace shaft. IFAC-PapersOnLine. 2018;51(21):48–52. doi: 10.1016/j.ifacol.2018.09.391
26.
ShiPYFuDZhouPet al.Evaluation of stock profile models for burden distribution in blast furnace. Ironmak Steelmak. 2015;42(10):756–762. doi: 10.1179/1743281215Y.0000000017
27.
ZhouPShiPYSongYPet al.Evaluation of burden descent model for burden distribution in blast furnace. J Iron Steel Res Int. 2016;23(8):765–771. doi: 10.1016/S1006-706X(16)30118-2
28.
FuDChenYZhouCQ.Mathematical modelling of blast furnace burden distribution with non-uniform descending speed. Appl Math Model. 2015;39(23):7554–7567. doi: 10.1016/j.apm.2015.02.054
29.
WuDLZhouPYanHJet al.Numerical investigation of the effects of size segregation on pulverized coal combustion in a blast furnace. Powder Technol. 2019;342:41–53. doi: 10.1016/j.powtec.2018.09.067
30.
RenYHuFXMiaoHP.The optimization of kernel function and its parameters for SVM in well-logging. International Conference on Service systems & Service management. 2016. p. 1–5.
