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Optimization of sampling location in the ladle during RH vacuum refining process
Accepted Manuscript Optimization of sampling location in the ladle during RH vacuum refining process Bohong Zhu, Kinnor Chattopadhyay, Xunpu Hu, Bo Zhang, Qingcai Liu, Zhanbin Chen PII:
S0042-207X(18)30143-X
DOI:
10.1016/j.vacuum.2018.02.033
Reference:
VAC 7834
To appear in:
Vacuum
Received Date: 24 January 2018 Revised Date:
25 February 2018
Accepted Date: 26 February 2018
Please cite this article as: Zhu B, Chattopadhyay K, Hu X, Zhang B, Liu Q, Chen Z, Optimization of sampling location in the ladle during RH vacuum refining process, Vacuum (2018), doi: 10.1016/ j.vacuum.2018.02.033. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Optimization of sampling location in the ladle during
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RH vacuum refining process
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Bohong Zhu1,3*, Kinnor Chattopadhyay2, Xunpu Hu1, Bo Zhang1, Qingcai Liu3,
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and Zhanbin Chen4
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9 10 11 12 13 14
Zhuzhou, 412007, China
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1. College of Metallurgy and Materials Engineering, Hunan University of Technology,
2. Department of Materials Science and Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada
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3. College of Materials Science and Engineering, Chongqing University, Chongqing, 400044, China
4. College of Science, Hunan University of Technology, Zhuzhou, 412007, China *Corresponding email: [email protected]
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Abstract: Sampling location is indeed essential to determine whether the sample is representative in the
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RH vacuum refining process. In this study, in order to find a reasonable method to optimize the
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sampling location, both numerical method and water modeling technique have been employed.
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The results showed that the tracer experiment can be ingeniously treated as the method of
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optimizing sampling location. Using this method, the starting mixing time (SMT), circulation
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number and curve amplitude can be obtained from the tracer concentration curve to characterize
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the mixing process of molten steel at different positions of the ladle. Therefore, by comparing
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and analyzing these three parameters between a large number of potential sampling locations,
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some useful recommendations about the optimal sampling location were proposed.
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1. Introduction Ruhrstahl-Heraeus (RH) degasser has been widely used in secondary refining due to its
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manifold metallurgical functions, such as homogenizing and heating molten steel,
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decarburization, removal of inclusion, and vacuum degassing et al. [1-7]. Investing in a RH
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degasser means the steel plant has better chances of producing value added steel grades viz.
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automotive and pipeline grades. In a view of the fact that the property of each steel grade is
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sensitive to its chemical composition, it is necessary to strictly control the steel composition in a
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suitable range. Therefore, sampling and composition analysis are treated as two essential
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procedures for the RH process to monitor the quality of molten steel. To get the representative
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chemistry of the steel grade, it is essential to chose an appropriate sampling location. As most parts of the RH reactor are sealed, such as snorkels and vacuum chamber, it is quiet
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difficult or sometimes are impossible to take sample from these parts. Thus, the samples are
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usually taken in the ladle under the artificial sampling condition. In the actual RH process,
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steelmakers take the sample at the area between two snorkels. The main reason of their decision
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is that this area is very safe and convenient for crashing the top slag and taking the sample.
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Unfortunately, no one has thoroughly ensured whether this sampling location is appropriate or
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not, and the study of the RH sampling location has also not been reported. Significantly, it is of
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paramount importance to study and optimize the sampling location in the RH process, and give
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valuable insights to the actual production process.
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It is very cumbersome and expensive to optimize the sampling location by repeated trials in
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the actual plant because of trial approvals and production delays, and hence applying a robust
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and cost-effective alternative should be as a priority concern. In the RH reactor, the variation of
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chemical composition of molten steel is not only related to the refining reactions occurred some
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specific regions, but also is affected by its own mixing process. In general, the most of refining
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reactions, such as decarburization, denitrogenation as well as dehydrogenation, occur in the up-
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snorkel and vacuum chamber [3-5,8,9], as a result of the very low partial pressure in these parts.
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Therefore, the chemical reaction taking place in the ladle is seldom compared to that in the
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snorkel and vacuum chamber. Namely, the variation of chemical composition of molten steel in
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the ladle is basically determined by its mixing process. Currently, to deeply understand the
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mixing process of molten steel in different sizes of RH reactor, many researchers applied the
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method of tracer experiment through both the water model [7,10-14] and numerical simulation
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[15-20]. In the water model, the tracer experiment involves the use of the saturated solution,
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generally KCl [7,10,11,14] and NaCl [12,13], to inject the flow field. By tracking the solution
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conductivity, the mixing process of fluid can be reflected by the tracer concentration curve.
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Similarly, the tracer experiment also can be carried out in the numerical simulation using the
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transport model [23]. Yet, almost all of the previous researchers only concentrated on the
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investigation of mixing time using the tracer experiment, and none of them have tried to explore
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the other usage of this method.
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In this study, by comparing and analyzing a large number of potential sampling points, the
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optimal sampling locations were recommended by ingeniously applying the tracer experiment in
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both the water model and numerical simulation. The water model was established with a reduced
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scale (1:5) based on similarity principles. For the numerical simulation, the gas–liquid flow of an
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industrial scale RH reactor (210 t) was modeled using the Euler–Euler approach, because of the
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extreme high gas–holdup in the up-snorkel (>10 pct by volume) [17]. In addition, some key
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parameters for sampling optimization were introduced, and the obtained optimal sampling
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locations were validated by the industrial trials.
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2. Methodology
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2.1 Mathematical modeling
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The gas–liquid multiphase flow in an industrial scale RH reactor (210 t) has been predicted using the Euler–Euler approach based on the following assumptions: (a) During the process of vacuuming, a very small amount of the ladle slag will be dragged
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into vacuum chamber though the snorkels. This part of the slag almost floats on the free
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surface of the molten steel. However, due to the effect of the top slag on the turbulent flow
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is not significant, its effect was not considered [15-21].
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(b) The deformation process of gas bubbles in the up-snorkel is very complex, which involves
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growth, coalescence and breakup. For simplification, the shape of gas bubble was assumed
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to be spherical, and its size was treated as a constant. In addition, the interactions between
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gas bubbles, such as coalescence and breakup, were not taken into account [3,4,6,15-21].
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(c) Although there is a temperature gradient of 30 to 50 degrees in the real RH refining
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process, the fluid flow was considered under isothermal conditions to simplify the problem.
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The Eulerian modeling framework describes each phase as interpenetrating continua and
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incorporates the concept of phasic volume fractions (
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in the governing equations.
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Continuum equation: ∙
=0
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Momentum equation:
7
+ ,
∙
=−
8
where
and
9
is the pressure, and
,
+
+
∙
for gas phase)
+
,
are the density, velocity and effective viscosity of phase
+
(1)
(2)
respectively.
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+
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, for liquid phase and
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is gravity acceleration.
is the total interphase force between liquid and
gas phase, which only includes the drag, virtual mass and turbulent dispersion forces. The details
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of forces description and relevant formulas can be found in the previous work [17].
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The diameter of gas bubbles was treated as a constant, and calculated using the Eq.(3) based on the experimental data [16,22]. # '.(
= 0.091 " &
%$) '.**
(3)
is the diameter of gas bubbles, and + is the surface tension coefficient. ) is the initial
where
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velocity of gas bubbles at the nozzle, which is related to gas flow rate and nozzle diameter.
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Turbulence model:
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The k-ɛ turbulence model was applied for the liquid phase based on the Eq.(4) and Eq.(5), while the dispersed phase zero equation was utilized for the gas phase based on the Eq.(6). ,
- - ./ +
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,
- - 7/
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=
∙,
- - - ./
=
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$
+
∙,
- - - 7/
=
12
& , . /4 +
∙0
-
"
-
+
∙0
-
"
-
+ 2& , 7 /4 +
#3 1
#8
1%2
- 56
−
- -7
9 - 6 ,:9; 56
− :9< - 7/
(4) (5)
(6)
$% =>2
is the turbulence viscosity. 56 is the generation of turbulence kinetic energy. The
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where
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parameter values of :9; =1.44, :9< =1.92, +6 =1and +9 =1.3 were used [23]. ? is the turbulent
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Prandtl number of the gas phase.
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To consider the effect of bubbles on the liquid turbulence, a bubble induced turbulent viscosity
1
3
model according to Sato et al. [24] was employed: -
= :1
6%@ 9%
A
+ :1,
−
-A
(7)
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where the first term in the right hand side in Eq.(7) is the shear induced turbulence, and the
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second term is the extra bubble induced contribution. :1 =0.09, and :1, =0.6. Tracer transport model:
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To understand the mixing process of molten steel in the RH reactor, the tracer transport model
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was applied using the following equation: ,
- - C/ +
∙,
- - - C/
=
∙"
-
" - CD +
12,% EF
& C&
(8)
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where C is the tracer. CD is the kinematic diffusivity, Sc is the turbulent Schmidt number.
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2.2 Water modeling
A water model was established to validate the numerical simulation results with a scale factor
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(I) of 1:5 based on similarity principles, and the dimensions and material properties for both
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prototype and water model are shown in Table 1. The modified Froude number was satisfied
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between water model and its prototype, which can be expressed as:
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@ KLM $LM NO P$LM / QR
? J = ,$
YZX ,
@ KSTM $STM USO P$STM / QV
= ,$
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where
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)WX and )YZX are the velocity of argon and air at the nozzle exit. In addition, the value of ^_ /^a
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is expressed as the similarity ratio (I).
[\
and
(9)
]Y\
represent the density of argon, air, steel and water, respectively.
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According to the previous work [], the temperature difference between the actual and
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laboratory test was considered. Thus, the gas flow rate can be translated using the following
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equation.
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5WX = 0.817 · ,1/I/(/< 5YZX
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2.3 Boundary condition and measure method
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(10)
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Numerical calculations were performed through the use of the commercial computational fluid
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dynamics ANSYS-CFX16.2. The walls of the ladle, snorkel, and vacuum chamber were
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considered to be no-slip, and the scalable wall function was applied for modeling turbulence in
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the near-wall region. The liquid surface of the ladle was considered as free surface. The liquid
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surface of the vacuum chamber was applied the degassing boundary condition, where dispersed
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bubbles are permitted to escape, but the liquid phase is not. The inlet boundary condition of
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model was used the normal velocity. The volume fraction of gas phase at the inlet was set at 1,
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while the volume fraction of liquid phase was set at 0.
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In water model experiment, the blue ink was injected into the stable flow field through the up-
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snorkel, and the flow pattern in the water model was observed by tracking the injected blue ink.
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To measure the circulation flow rate, the ultrasonic flowmeter installed on the down-snorkel was
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applied. Additionally, the saturated NaCl solution was treated as tracer to inject into the flow
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field through the up-snorkel, and the fluid mixing process in the water model was reflected by
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the fluid conductivity, which is measured by a digital conductivity meter.
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3. Results and discussions
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3.1 Validation of the mathematical model
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3.1.1 Flow pattern
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The simulation results of molten steel flow pattern in an industrial scale RH reactor are shown
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in Fig.1. As shown, the molten steel in the up-snorkel firstly enters the vacuum chamber due as it
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is driven by the injected gas. In the vacuum chamber, because most of molten steel has a
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tendency to flow along the side wall of vacuum chamber, the velocity of molten steel along the
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wall is higher than that in the middle region (Fig.1(b)). Subsequently, the molten steel flows
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back to the ladle through the down-snorkel, and touches the bottom of the ladle with a high
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velocity. It should be noticed that there are two huge eddy zones apparently generated at two
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sides of the downward stream (Fig.1(a)). After that, the molten steel in the ladle flows into the
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up-snorkel again, and the next circulation continues. The predicted flow pattern of molten steel
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matches well with many published numerical simulation results [4, 15-17, 20]. In addition, the
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fluid flow pattern was also observed by tracking the blue ink in the water model. As shown in
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Fig.2, the flow patterns of blue ink in the vacuum chamber and ladle are corresponded with the
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numerical simulation results. Significantly, two mentioned eddy zones exist at right and left sides
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of the downward stream in the ladle.
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3.1.2 Circulation flow rate and mixing time
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Fig.3 shows variety of circulation flow rate at different gas flow rates. As seen, the circulation
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flow rate increases markedly with the increase of gas flow rate, but its increasing tendency 7
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gradually slows down when the gas flow rate reaches to the higher values. When the gas flow
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rate is very small, the value of circulation flow rate of water model is slightly higher than the
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numerical predicted value. This may be because that, at the very low gas flow rate condition, the
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gas bubbles in the numerical simulation basically rise up along the wall, but in the water model
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experiment, there always exist some gas bubble flowing to the center part of the up-snorkel
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during rising process, resulting in the larger circulation flow rate. However, with the gas flow
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rate increasing, the differences between the measured values and numerical predicted values are
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not significant.
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The mixing time is an another important parameter for reflecting the mixing process of fluid in
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the RH reactor. Usually, the time when the tracer concentration (e,f/) achieves a narrow range
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of ±5 pct of the final concentration (e,∞/) is defined as the mixing time [7,12,15-20]. Fig.4
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shows comparison of mixing time between numerical simulation and water model experiment at
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the same gas flow rate. Here, monitor point B and C are below the ladle free surface with a
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reduced size (120 mm), and the diagrammatic sketch of location can be found in Fig.5.
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Apparently, the mixing time from both numerical simulation and water model experiment are
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basically synchronized, proving that the numerical simulation results match well with the water
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model results. It should be noticed that the concentration curve amplitude of water model of each
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case is slightly greater than that of numerical simulation. This phenomenon can be explained as
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follows: The saturated NaCl solution was used as the tracer in the water model experiment.
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Although the fluid flow pattern is the dominated factor to determine the concentration curve, the
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diffusion process of NaCl solution also affect its concentration change process. But in the
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numerical simulation, due to the diffusion coefficient of tracer was set at a very small value, the
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diffusion process of tracer was ignored.
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3.2 Method of optimizing sampling location
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In this section, the tracer experiment has been proved to be treated as the method of
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optimizing sampling location. Some key parameters for the follow-up optimization study were
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obtained from the tracer concentration curve, and the details of optimization scheme were
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introduced.
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3.2.1 Characteristics of tracer concentration curve
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In this study, the tracer experiments were investigated in a full size RH using the verified
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mathematical model. Fig.6 shows the variety of tracer concentration of point A with different 8
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computational time. At first, the concentration of tracer at point A is zero till the tracer flow to
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this point. Subsequently, the tracer concentration of curve sharply increases, reaches a peak value,
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and then decreases. The oscillation phenomena repeats, but the amplitude finally decreases with
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the computational time. Here, the first turning point of concentration curve is defined as starting
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mixing time (SMT). Between the starting mixing time with mixing time, the concentration curve
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of point A has several waves. The time interval between two troughs is defined as the time for
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circulation between the ladle and vacuum. Zhang and Li [7] pointed out that the mixing time
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typically equals to three to four times of the circulation time. It can be seen in Fig.6 that, at point
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A, the tracer completes three circulations to reach the mixing time, and its dose that flow to this
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point at each circulation can be reflected by the amplitude of each wave.
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3.2.2 Comparison of tracer concentration curve between the difference monitor points
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Fig.7 shows the comparison of tracer concentration curve between the difference monitor
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points. As the location of each monitor point is different, the mixing time of each point exists the
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certain difference. In addition, before the mixing time, the features of concentration curve
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between different monitor points are also different. All of these results indicate that the mixing
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processes of molten steel at different locations of ladle are extremely different. It should be
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emphasized that some key parameters for the follow-up optimization study can be obtained from
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Fig.7. The first key parameter is the starting mixing time (SMT). Fig.7 shows that point A has
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the shortest SMT value, whereas the SMT value of point C is largest. In the present study, the
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tracer was added into the RH reactor from the nozzles, and began to flow from the up-snorkel. If
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the tracer can be represented as the refined molten steel, which reacts in the up-snorkel and
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vacuum chamber, it is not hard to image that the refined molten steel from the top parts should
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first flow to point A compared to point B, C and D. With divergent consideration of it, if the
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sampling point which has the shortest SMT value in the ladle can be found, this point can be
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reasonably used to monitor the refining effect of molten steel in the up-snorkel and vacuum
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chamber. In contrary, if the sampling point which has the largest SMT value in the ladle, the
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mixing process of molten steel at this point is slowest in the ladle.
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An another interesting result can be seen in Fig.7 that the change rates of tracer concentration
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at different sampling points are different. To reach the mixing time, point A goes through three
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circulations, point B has two circulations, and only one circulation for point C. However, for
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point D, it has no the complete wave. This is because that point D is located in the left eddy zone, 9
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and the existence of eddy zone retards the mixing process of tracer. Therefore, this interesting
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result represents that the change rate of chemical composition of molten steel at different
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sampling points is related to its circulation number. The more the circulation number, the higher
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the change rate of chemical composition. Moreover, Fig.7 shows that the curve amplitude of
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point A is significant larger than that of other’s points, because the point A is just located in the
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bottom of the down-snorkel, indicating the tracer dose flow to point A is higher than that of
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other’s points. It means that, if the tracer can be treated as the refined molten steel, getting the
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sample from point A is more representative than from other’s points.
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From the above discussions, it is clear that the mixing process of molten steel at different
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points in the ladle can be intuitively reflected by the tracer concentration curve, and the starting
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mixing time, circulation number and curve amplitude can be treated as three key parameters to
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characterize the mixing process.
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3.2.3 Descriptions of optimization scheme
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In the industrial sampling process, steelmakers usually use the sampling rod to take the
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lollipop sample from the ladle. As shown in Fig.5, the molten steel can be collected in the
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lollipop mold, which is installed at the top of the sampling rod. Owing to the limitation of length
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of sampling rod, the actual sampling depth is in a range of 300-400 mm below the slag layer.
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Fig.8 shows the flow pattern and steel velocity at different sampling layers. As shown, the
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differences of flow pattern and steel velocity between different layers are not significant. Also,
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the flow pattern of molten steel at side I and II can be regarded as symmetrical. Therefore, in this
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study, the sampling layer was selected at the intermediate value (350 mm), and all potential
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sampling points were chosen at one side, as shown in Fig.9. In order to better distinguish the
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sampling points, the sampling layer was divided into three sub-regions: up-snorkel region, down-
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snorkel region and middle region, and each of sub-region has its own potential sampling points.
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3.3 Comparison of the mixing process between different sampling points
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3.3.1 Optimal sampling location
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Fig.10 shows the variety of tracer concentration at each sampling point in different sub-
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regions. In the up-snorkel sub-region, the difference of SMT value between each sampling point
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is not significant, and it proves that the molten steel basically flows to this region at the same
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time. The SMT value of point 12 is lowest, whereas point 13 and point 14 have the largest SMT
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value, as shown in Fig.10(a). Fig.10(b) shows the difference of SMT value between each 10
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sampling point in the middle sub-region tends to increase. In this region, the SMT value
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gradually increases along the reverse direction of Y axis (Fig.9). Point 8 and point 9 have the
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lowest value, and point 5 has the largest value. Similarly, in the down-snorkel sub-region, point 3
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holds the lowest SMT value, but point 1 has an extremely high value of SMT, as shown in
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Fig.10(c).
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Fig.11 (a) shows the comparison of the minimum SMT value between each sub-region.
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Apparently, both point 8 and point 9 have the lowest SMT value. It represents that the refined
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molten steel from the top sealed parts (vacuum chamber and snorkels) firstly flows to point 8 and
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point 9 compared to all sampling points in the ladle. Moreover, the curve amplitude of point 9 is
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significantly greater than that of point 8, indicating that getting the sample from point 9 is more
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representative than from point 8. Therefore, to make a correct and timely test of the molten steel
12
chemistry in the up-snorkel and vacuum chamber for monitoring the refining effect, point 9 is the
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optimal sampling location. In addition, Fig.11 (b) shows that, compared to all sampling points,
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point 1 not only holds the largest value of SMT, but also only has one circulation number. It can
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be seen in Fig.12 that the downward stream of molten steel touches the base of the ladle with a
16
high velocity, and leads to two eddy regions developing in the ladle. Special attention should be
17
paid to the “right eddy”, which is only located in the middle and lower part of the ladle. After the
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molten steel flows out of the “right eddy, it does not go straight up to the ladle free surface, but
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meets the other molten steel flowing from ladle free surface. Obviously, two new small eddies
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generate at two sides. Here, the region located between these two eddies is defined as the “dead
21
region”. As mentioned before, the existence of eddy could retard the mixing process of molten
22
steel. Due to the point 1 is just in this “dead region”, the concentration curve of point 1 has
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smallest curve amplitude and the least circulation number compared to all sampling points. All
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of these results represent that, at point 1, the molten steel has the latest starting mixing time, the
25
least amount of updates and the slowest change rate of chemical composition. Therefore, if
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steelmakers need to obtain a representative sample of molten steel for ensuring that all of molten
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steel in the ladle are up to the standard before the end of the RH refining process, it is better to
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take samples from point 1.
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3.3.2 Effect of the operation parameters
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In the actual RH process, the operation parameters, such as gas flow rate, vacuum pressure and
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immersion depth of snorkel, can not be always maintained at the certain value. These operation 11
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parameters usually can be adjusted in a proper range, and hence the effect of operation
2
parameters on the obtained sampling locations should be investigated. For example, for the 210 t
3
size of RH reactor, the reasonable gas flow rates are usually controlled at 90-180 m3/h. Fig.13
4
exhibits that the flow pattern of molten steel in the ladle changes with its velocity when the gas
5
flow rate is low (30-60 m3/h). The velocity of molten steel increases with the higher gas flow rate
6
(60-120 m3/h). However, when the velocity of molten steel reaches a certain value, the molten
7
steel will maintain the steady pattern to flow in the ladle. In this study, this certain value is within
8
the range of 30-60 m3/h.
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Fig.14 shows the comparison of tracer concentration curve between different gas flow rates.
10
As shown, under different gas flow rate conditions, the concentration curve just change its
11
distribution, but has no significant change on its shape. It proves that the obtained optimal
12
sampling locations are valid within the actual gas flow rates. In addition, for other operation
13
parameters, such as vacuum pressure and immersion depth of snorkel, the effects of these two
14
parameter are same to that of gas flow rate, due to these two parameters only affect the
15
circulation flow rate of molten steel [25-27].
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3.4 Industrial trials
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In this study, industrial trials were carried out to validate the obtained optimal sampling
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locations during the RH refining of IF steel. The [C] content of sample was used as the indicator,
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and the whole decarburization process of IF steel (12-15 min) was monitored. During the
20
decarburization process, the vacuum pressure was set at 133 Pa, and the gas flow rates injected
21
into the up-snorkel were controlled at 90-120 m3/h. The lollipop samples were taken from the
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corresponding sampling location every 2.0-3.0 min until the end of decarburization treatment.
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The samples were taken at a depth of 300-400 mm below the ladle free surface after the crushing
24
slag process, and quickly cooled in water. Therefore, the [C] content in the sample was
25
representative of the melt at each of sampling time. Moreover, it should be noticed that only one
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sample at the beginning or ending of decarburization process was analyzed in some heats, and
27
some of the deviation data were not used.
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Fig.15 shows the varieties of [C] content of IF steel during the RH refining process at different
29
sampling locations. Here, point 1 and point 9 are the optimal sampling locations mentioned
30
above, and point 3 and Point 12 are treated as contrast. Obviously, the decreasing rate of [C]
31
content at point 1 is slowest, indicating that the amount of the refined molten steel from the top 12
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sealed parts flows to this point is few, especially in 1-2 minutes. It once again proves that the
2
sample taken from point 1 can completely ensure the whole ladle of molten steel reaches the
3
standard. In addition, the decreasing rate of [C] content at point 9 is fastest compared to that of
4
point 3 and point 12, although its advantage is not significant in some heats. Therefore, point 9 is
5
the optimal sampling location for getting the representative composition to monitor the refining
6
effects.
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4. Conclusions
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The analyses of the flow and mixing characteristic of fluid from both mathematical and water
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modeling allowed the present researchers to come up with some useful recommendations about
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the importance of the sampling location. The crucial conclusions include:
1. Numerical results of flow pattern, circulation flow rate, and mixing time agree well with the
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water model results, so the gas–liquid flow in the RH reactor can be predicted using the present
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mathematical model.
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2. The tracer experiment has been proved to be treated as the method of optimizing sampling
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location. The starting mixing time (SMT), circulation number and curve amplitude are three key
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parameters to characterize the mixing process.
3. By comparing a large number of potential sampling points, point 9 has been treated as the
18
optimal sampling location for monitoring the refining process of molten steel in the up-snorkel
19
and vacuum chamber. In addition, point 1 is the best sampling location in the steel plant as it will
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guarantee a homogenous representative sample of molten steel and ensure that it is up to the
21
standard before the end of the RH refining process.
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REFERENCES
23
[1] H. Maas, P. Hupfer. The significant of the circulation rate on the vacuum treatment of liquid
25 26
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24
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steel with the RH process, Vacuum 19 (1969) 199-203. [2] V. Presen. Recent trends and the future of vacuum in steel making, Vacuum 43 (1992) 373379.
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[3] D.Q. Geng, J.X. Zheng, K. Wang, P. Wang, R.Q. Liang, H.T. Liu, H. Lei, J.C. He.
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Simulation on decarburization and inclusion removal process in the Ruhrstahl-Heraeus (RH)
29
process with ladle bottom blowing. Metall. Mater. Trans. B 46 (2015) 1484-1493.
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1 2
[4] J. Zhang, L. Liu, X. Zhao, S. Lei, Q. Dong. Mathematical model for decarburization process in RH refining process, ISIJ Int. 54 (2014) 1560-1569. [5] K. Steneholm, M. Andersson, ATilliander, P.G. Jonsson. Removal of hydrogen, nitrogen
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and sulphur from tool steel during vacuum degassing, Ironmak. Steelmak. 40 (2013) 199-
5
205.
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[6] J. Han, X.D. Wang, D.C. Ba. Coordinated analysis of multiple factors of argon blowing
7
parameters on the effect of circulation flow rate in RH vacuum refining process, Vacuum
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109 (2014) 68-73.
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[7] L. Zhang, F. Li. Investigation on the fluid flow and mixing phenomena in a Ruhrstahl-
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9
Heraeus (RH) steel degasser using physical modeling, JOM 66 (2014) 1227-1240. [8] M.A. Van Ende, Y.M. Kim, M.K. Cho, J. Choi, I.H. Jung. A kinetic model for the
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Ruhrstahl-Heraeus (RH) degassing process, Metall. Mater. Trans. B 42 (2011) 477-489.
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[9] B. Kleimt, S. Kohle, K.P. Johann, A. Jungreithmeier, J. Molinero. Dynamic process model
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for denitrogenation and dehydrogenation by vacuum degassing, Scand. J. Metall. 29 (2000)
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194-205.
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[10] Y. Li, Y. Bao, R. Wang, M. Wang, Q. Huang, Y. Li. Modeling of liquid level and bubble behavior in vacuum chamber of RH process, J. Iron Steel Res. Int. 23 (2016) 305-313.
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[11] H. Ling, C. Guo, A.N. Conejo, F. Li, L. Zhang. Effect of snorkel shape and number of
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nozzles on mixing phenomena in the RH process by physical modeling, Metall. Res.
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Technol. 114 (2017) 111.
[12] D. Mukherjee, A.K. Shukla, D.G. Senk. Cold model-based investigations to study the effects
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of operational and nonoperational parameters on the Ruhrstahl–Heraeus degassing process,
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Metall. Mater. Trans. B 48 (2017) 1-7.
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[13] W. Zheng, Z. He, G. Li, H. Tu, Z. Zhang. Physical simulation of effect of injection
25
parameters on circulation flow rate and mixing time in RH vacuum refining process, J. Iron
26
Steel Res. 29 (2017) 373-381.
27 28 29 30
[14] K. Yoshitomi, M. Nagase, M.A. Uddin, Y. Kato. Fluid mixing in ladle of RH degasser induced by down flow, ISIJ Int. 56 (2016) 1119-1123. [15] D.Q. Geng, H. Lei, J.C. He. Simulation on flow field and mixing phenomenon in RH degasser with ladle bottom blowing, Ironmak. Steelmak. 39 (2012) 431-438.
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[16] D.Q. Geng, H. Lei, J.C. He. Numerical simulation of the multiphase flow in the RheinsahlHeraeus (RH) system, Metall. Mater. Trans. B 41 (2010) 234-247. [17] B. Zhu, Q. Liu, M. Kong, J. Yang, D. Li, K. Chattopadhyay. Effect of interphase forces on
4
gas-liquid multiphase flow in RH degasser, Metall. Mater. Trans. B 48 (2017) 2620-2630.
5
[18] G. Chen, S. He. Mixing behavior in the RH degasser with bottom gas injection, Vacuum 130
6
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(2016) 48-55.
[19] K. Feng, A. Liu, K. Dai, S. Feng, J. Ma, J. Xie, B. Wang, Y. Yu, J. Zhang. Effect of gas rate
8
and position of powder injection on RH refining process using numerical simulation, Powder
9
Tech. 314 (2017) 649-659.
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[20] G. Chen, S. He, Y. Li, Y. Guo, Q. Wang. Investigation of gas and liquid multiphase flow in
11
the Rheinsahl–Heraeus(RH) reactor by using the Euler–Euler approach, JOM 68 (2016)
12
2138-2148.
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[21] B. Zhu, Q. Liu, D. Zhao, S. Ren, M. Xu, B. Yang, B. Hu. Effect of nozzle blockage on
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circulation flow rate in up-snorkel during the RH degasser process, Steel Res. Int. 87 (2016)
15
136-145.
17
[22] M. Sano, K. Mori, Y. Fujita. Dispersion of gas injected into liquid metal, Tetsu-to-Hagane 65 (1980) 1140-1148.
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[23] Ansys Inc.: ANSYS CFX-Solver Theory Guide, Release 12.1, Canonsburg, 2009, 101–150.
19
[24] Y. Sato, M. Sadatomi, K. Sekoguchi. Momentum and heat transfer in two-phase bubble
20
flow-II. A comparison between experimental data and theoretical calculations, Int. J.
21
Multiphas. Flow 7 (1981) 179-190.
23 24 25 26 27
[25] Y.G. Park, K.W. Yi, S.B. An. The effect of operating parameters and dimensions of the RH system melt circulation using numerical calculations, ISIJ Int. 41 (2001) 403-409.
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[26] P.A. Kishan, S.K. Dash. Prediction of circulation flow rate in the RH degasser using discrete phase particle modeling, ISIJ Int. 49 (2009) 495-504. [27] S.K. Ajmani, S.K. Dash,S.Chandra, C. Bhanu. Mixing evaluation in the RH process using mathematical modelling, ISIJ Int. 44 (2004) 82-90.
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Table List
Table 1 Parameters of prototype and water model Prototype
Water model
Upper/bottom ladle diameter, mm
3772/3200
754/640
Diameter of vacuum chamber, mm
2150
Snorkels length/diameter, mm
1650/600
Distance between longitudinal axes, mm
1500
Nozzle diameter, mm
5
Viscosity of liquid, Pa·s
0.006
Surface tension, N/m
1.5
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Parameters
430
310 1
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330/120
8.9E-5 0.06
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Table list Fig.1 Simulation results of molten steel flow pattern in an industrial scale RH reactor, at the gas flow rate of 120 m3/h.
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Fig.2 Flow process of blue ink in the water model, at gas flow rate 2.63 m3/h. Fig.3 Variety of circulation flow rate at different gas flow rates.
Fig.4 Comparison of mixing time between numerical simulation and water model experiment, at gas flow rate of 2.63 m3/h.
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Fig.5 Locations of monitor points and industrial sampling tool.
Fig.6 Variety of tracer concentration of point A with different computational time, at
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the gas flow rate of 120 m3/h, and monitor point A can be found in Fig.5. Fig.7 Comparison of tracer concentration curve between the difference monitor locations, at the gas flow rate of 120 m3/h, and each monitor point can be seen in Fig.5.
Fig.8 Flow pattern and steel velocity at different sampling layers, at the gas flow rate
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of 120 m3/h, (a) 300 mm, (b) 350 mm and (c) 400 mm.
Fig.9 Locations of sampling point in different sub-regions. Fig.10 (a, b, c) Variety of tracer concentration at each sampling point in different
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sub-regions, (a) up-snorkel region, (b) middle region and (c) down-snorkel region. Fig.11 (a, b) Comparisons of the maximum and minimum SMT value between each
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sub-region: (a) lowest SMT value, (b) largest SMT value. Fig.12 Flow pattern of molten steel in the ladle. Fig.13 Flow pattern of molten steel in the ladle at different gas flow rates, (a) 30 m3/h, (b) 60 m3/h, (c) 90 m3/h and (d)120 m3/h. Fig.14 Comparison of tracer concentration curve between the different gas flow rates. Fig.15 Varieties of [C] content of IF steel during the RH refining process at different sampling locations.
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Fig.1 Simulation results of molten steel flow pattern in an industrial scale RH reactor,
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at the gas flow rate of 120 m3/h.
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Fig.2 Flow process of blue ink in the water model, at gas flow rate 2.63 m3/h.
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Fig.3 Variety of circulation flow rate at different gas flow rates.
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Fig.4 Comparison of mixing time between numerical simulation and water model
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experiment, at gas flow rate of 2.63 m3/h.
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Fig.5 Locations of monitor points and industrial sampling tool.
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Fig.6 Variety of tracer concentration of point A with different computational time, at
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the gas flow rate of 120 m3/h, and monitor point A can be found in Fig.5.
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Fig.7 Comparison of tracer concentration curve between the difference monitor
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Fig.5.
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Fig.8 Flow pattern and steel velocity at different sampling layers, at the gas flow rate
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of 120 m3/h, (a) 300 mm, (b) 350 mm and (c) 400 mm.
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Fig.9 Locations of sampling point in different sub-regions.
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Fig.10 (a, b, c) Variety of tracer concentration at each sampling point in different sub-regions, (a) up-snorkel region, (b) middle region and (c) down-snorkel region.
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Fig.11 (a, b) Comparisons of the maximum and minimum SMT value between each sub-region: (a) lowest SMT value, (b) largest SMT value.
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Fig.12 Flow pattern of molten steel in the ladle.
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Fig.13 Flow pattern of molten steel in the ladle at different gas flow rates, (a) 30 m3/h,
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(b) 60 m3/h, (c) 90 m3/h and (d)120 m3/h.
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Fig.14 Comparison of tracer concentration curve between the different gas flow rates.
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Fig.15 Varieties of [C] content of IF steel during the RH refining process at different
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sampling locations.
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Highlights
1) The fluid flow and mixing behavior in the RH degasser were investigated in both numerical method and water modeling technique.
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2) The tracer experiment is ingeniously treated as the method of optimizing sampling location.
3) Some optimal sampling locations were proposed, and verified by the industrial
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trials.
S0042-207X(18)30143-X
DOI:
10.1016/j.vacuum.2018.02.033
Reference:
VAC 7834
To appear in:
Vacuum
Received Date: 24 January 2018 Revised Date:
25 February 2018
Accepted Date: 26 February 2018
Please cite this article as: Zhu B, Chattopadhyay K, Hu X, Zhang B, Liu Q, Chen Z, Optimization of sampling location in the ladle during RH vacuum refining process, Vacuum (2018), doi: 10.1016/ j.vacuum.2018.02.033. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Optimization of sampling location in the ladle during
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RH vacuum refining process
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Bohong Zhu1,3*, Kinnor Chattopadhyay2, Xunpu Hu1, Bo Zhang1, Qingcai Liu3,
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and Zhanbin Chen4
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9 10 11 12 13 14
Zhuzhou, 412007, China
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1. College of Metallurgy and Materials Engineering, Hunan University of Technology,
2. Department of Materials Science and Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada
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3. College of Materials Science and Engineering, Chongqing University, Chongqing, 400044, China
4. College of Science, Hunan University of Technology, Zhuzhou, 412007, China *Corresponding email: [email protected]
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Abstract: Sampling location is indeed essential to determine whether the sample is representative in the
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RH vacuum refining process. In this study, in order to find a reasonable method to optimize the
4
sampling location, both numerical method and water modeling technique have been employed.
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The results showed that the tracer experiment can be ingeniously treated as the method of
6
optimizing sampling location. Using this method, the starting mixing time (SMT), circulation
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number and curve amplitude can be obtained from the tracer concentration curve to characterize
8
the mixing process of molten steel at different positions of the ladle. Therefore, by comparing
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and analyzing these three parameters between a large number of potential sampling locations,
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some useful recommendations about the optimal sampling location were proposed.
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1. Introduction Ruhrstahl-Heraeus (RH) degasser has been widely used in secondary refining due to its
3
manifold metallurgical functions, such as homogenizing and heating molten steel,
4
decarburization, removal of inclusion, and vacuum degassing et al. [1-7]. Investing in a RH
5
degasser means the steel plant has better chances of producing value added steel grades viz.
6
automotive and pipeline grades. In a view of the fact that the property of each steel grade is
7
sensitive to its chemical composition, it is necessary to strictly control the steel composition in a
8
suitable range. Therefore, sampling and composition analysis are treated as two essential
9
procedures for the RH process to monitor the quality of molten steel. To get the representative
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chemistry of the steel grade, it is essential to chose an appropriate sampling location. As most parts of the RH reactor are sealed, such as snorkels and vacuum chamber, it is quiet
12
difficult or sometimes are impossible to take sample from these parts. Thus, the samples are
13
usually taken in the ladle under the artificial sampling condition. In the actual RH process,
14
steelmakers take the sample at the area between two snorkels. The main reason of their decision
15
is that this area is very safe and convenient for crashing the top slag and taking the sample.
16
Unfortunately, no one has thoroughly ensured whether this sampling location is appropriate or
17
not, and the study of the RH sampling location has also not been reported. Significantly, it is of
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paramount importance to study and optimize the sampling location in the RH process, and give
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valuable insights to the actual production process.
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It is very cumbersome and expensive to optimize the sampling location by repeated trials in
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the actual plant because of trial approvals and production delays, and hence applying a robust
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and cost-effective alternative should be as a priority concern. In the RH reactor, the variation of
23
chemical composition of molten steel is not only related to the refining reactions occurred some
24
specific regions, but also is affected by its own mixing process. In general, the most of refining
25
reactions, such as decarburization, denitrogenation as well as dehydrogenation, occur in the up-
26
snorkel and vacuum chamber [3-5,8,9], as a result of the very low partial pressure in these parts.
27
Therefore, the chemical reaction taking place in the ladle is seldom compared to that in the
28
snorkel and vacuum chamber. Namely, the variation of chemical composition of molten steel in
29
the ladle is basically determined by its mixing process. Currently, to deeply understand the
30
mixing process of molten steel in different sizes of RH reactor, many researchers applied the
31
method of tracer experiment through both the water model [7,10-14] and numerical simulation
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[15-20]. In the water model, the tracer experiment involves the use of the saturated solution,
2
generally KCl [7,10,11,14] and NaCl [12,13], to inject the flow field. By tracking the solution
3
conductivity, the mixing process of fluid can be reflected by the tracer concentration curve.
4
Similarly, the tracer experiment also can be carried out in the numerical simulation using the
5
transport model [23]. Yet, almost all of the previous researchers only concentrated on the
6
investigation of mixing time using the tracer experiment, and none of them have tried to explore
7
the other usage of this method.
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In this study, by comparing and analyzing a large number of potential sampling points, the
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optimal sampling locations were recommended by ingeniously applying the tracer experiment in
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both the water model and numerical simulation. The water model was established with a reduced
11
scale (1:5) based on similarity principles. For the numerical simulation, the gas–liquid flow of an
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industrial scale RH reactor (210 t) was modeled using the Euler–Euler approach, because of the
13
extreme high gas–holdup in the up-snorkel (>10 pct by volume) [17]. In addition, some key
14
parameters for sampling optimization were introduced, and the obtained optimal sampling
15
locations were validated by the industrial trials.
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2. Methodology
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2.1 Mathematical modeling
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The gas–liquid multiphase flow in an industrial scale RH reactor (210 t) has been predicted using the Euler–Euler approach based on the following assumptions: (a) During the process of vacuuming, a very small amount of the ladle slag will be dragged
21
into vacuum chamber though the snorkels. This part of the slag almost floats on the free
22
surface of the molten steel. However, due to the effect of the top slag on the turbulent flow
23
is not significant, its effect was not considered [15-21].
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(b) The deformation process of gas bubbles in the up-snorkel is very complex, which involves
25
growth, coalescence and breakup. For simplification, the shape of gas bubble was assumed
26
to be spherical, and its size was treated as a constant. In addition, the interactions between
27
gas bubbles, such as coalescence and breakup, were not taken into account [3,4,6,15-21].
28
(c) Although there is a temperature gradient of 30 to 50 degrees in the real RH refining
29
process, the fluid flow was considered under isothermal conditions to simplify the problem.
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The Eulerian modeling framework describes each phase as interpenetrating continua and
2
incorporates the concept of phasic volume fractions (
3
in the governing equations.
4
Continuum equation: ∙
=0
6
Momentum equation:
7
+ ,
∙
=−
8
where
and
9
is the pressure, and
,
+
+
∙
for gas phase)
+
,
are the density, velocity and effective viscosity of phase
+
(1)
(2)
respectively.
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+
5
, for liquid phase and
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is gravity acceleration.
is the total interphase force between liquid and
gas phase, which only includes the drag, virtual mass and turbulent dispersion forces. The details
11
of forces description and relevant formulas can be found in the previous work [17].
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The diameter of gas bubbles was treated as a constant, and calculated using the Eq.(3) based on the experimental data [16,22]. # '.(
= 0.091 " &
%$) '.**
(3)
is the diameter of gas bubbles, and + is the surface tension coefficient. ) is the initial
where
16
velocity of gas bubbles at the nozzle, which is related to gas flow rate and nozzle diameter.
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Turbulence model:
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The k-ɛ turbulence model was applied for the liquid phase based on the Eq.(4) and Eq.(5), while the dispersed phase zero equation was utilized for the gas phase based on the Eq.(6). ,
- - ./ +
21
,
- - 7/
22
=
∙,
- - - ./
=
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$
+
∙,
- - - 7/
=
12
& , . /4 +
∙0
-
"
-
+
∙0
-
"
-
+ 2& , 7 /4 +
#3 1
#8
1%2
- 56
−
- -7
9 - 6 ,:9; 56
− :9< - 7/
(4) (5)
(6)
$% =>2
is the turbulence viscosity. 56 is the generation of turbulence kinetic energy. The
23
where
24
parameter values of :9; =1.44, :9< =1.92, +6 =1and +9 =1.3 were used [23]. ? is the turbulent
25
Prandtl number of the gas phase.
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To consider the effect of bubbles on the liquid turbulence, a bubble induced turbulent viscosity
1
3
model according to Sato et al. [24] was employed: -
= :1
6%@ 9%
A
+ :1,
−
-A
(7)
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2
4
where the first term in the right hand side in Eq.(7) is the shear induced turbulence, and the
5
second term is the extra bubble induced contribution. :1 =0.09, and :1, =0.6. Tracer transport model:
7
To understand the mixing process of molten steel in the RH reactor, the tracer transport model
9
was applied using the following equation: ,
- - C/ +
∙,
- - - C/
=
∙"
-
" - CD +
12,% EF
& C&
(8)
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where C is the tracer. CD is the kinematic diffusivity, Sc is the turbulent Schmidt number.
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2.2 Water modeling
A water model was established to validate the numerical simulation results with a scale factor
13
(I) of 1:5 based on similarity principles, and the dimensions and material properties for both
14
prototype and water model are shown in Table 1. The modified Froude number was satisfied
15
between water model and its prototype, which can be expressed as:
16
@ KLM $LM NO P$LM / QR
? J = ,$
YZX ,
@ KSTM $STM USO P$STM / QV
= ,$
17
where
18
)WX and )YZX are the velocity of argon and air at the nozzle exit. In addition, the value of ^_ /^a
19
is expressed as the similarity ratio (I).
[\
and
(9)
]Y\
represent the density of argon, air, steel and water, respectively.
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According to the previous work [], the temperature difference between the actual and
21
laboratory test was considered. Thus, the gas flow rate can be translated using the following
22
equation.
23
5WX = 0.817 · ,1/I/(/< 5YZX
24
2.3 Boundary condition and measure method
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(10)
25
Numerical calculations were performed through the use of the commercial computational fluid
26
dynamics ANSYS-CFX16.2. The walls of the ladle, snorkel, and vacuum chamber were
27
considered to be no-slip, and the scalable wall function was applied for modeling turbulence in
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the near-wall region. The liquid surface of the ladle was considered as free surface. The liquid
2
surface of the vacuum chamber was applied the degassing boundary condition, where dispersed
3
bubbles are permitted to escape, but the liquid phase is not. The inlet boundary condition of
4
model was used the normal velocity. The volume fraction of gas phase at the inlet was set at 1,
5
while the volume fraction of liquid phase was set at 0.
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In water model experiment, the blue ink was injected into the stable flow field through the up-
7
snorkel, and the flow pattern in the water model was observed by tracking the injected blue ink.
8
To measure the circulation flow rate, the ultrasonic flowmeter installed on the down-snorkel was
9
applied. Additionally, the saturated NaCl solution was treated as tracer to inject into the flow
10
field through the up-snorkel, and the fluid mixing process in the water model was reflected by
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the fluid conductivity, which is measured by a digital conductivity meter.
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3. Results and discussions
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3.1 Validation of the mathematical model
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3.1.1 Flow pattern
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The simulation results of molten steel flow pattern in an industrial scale RH reactor are shown
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in Fig.1. As shown, the molten steel in the up-snorkel firstly enters the vacuum chamber due as it
17
is driven by the injected gas. In the vacuum chamber, because most of molten steel has a
18
tendency to flow along the side wall of vacuum chamber, the velocity of molten steel along the
19
wall is higher than that in the middle region (Fig.1(b)). Subsequently, the molten steel flows
20
back to the ladle through the down-snorkel, and touches the bottom of the ladle with a high
21
velocity. It should be noticed that there are two huge eddy zones apparently generated at two
22
sides of the downward stream (Fig.1(a)). After that, the molten steel in the ladle flows into the
23
up-snorkel again, and the next circulation continues. The predicted flow pattern of molten steel
24
matches well with many published numerical simulation results [4, 15-17, 20]. In addition, the
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fluid flow pattern was also observed by tracking the blue ink in the water model. As shown in
26
Fig.2, the flow patterns of blue ink in the vacuum chamber and ladle are corresponded with the
27
numerical simulation results. Significantly, two mentioned eddy zones exist at right and left sides
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of the downward stream in the ladle.
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3.1.2 Circulation flow rate and mixing time
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Fig.3 shows variety of circulation flow rate at different gas flow rates. As seen, the circulation
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flow rate increases markedly with the increase of gas flow rate, but its increasing tendency 7
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gradually slows down when the gas flow rate reaches to the higher values. When the gas flow
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rate is very small, the value of circulation flow rate of water model is slightly higher than the
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numerical predicted value. This may be because that, at the very low gas flow rate condition, the
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gas bubbles in the numerical simulation basically rise up along the wall, but in the water model
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experiment, there always exist some gas bubble flowing to the center part of the up-snorkel
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during rising process, resulting in the larger circulation flow rate. However, with the gas flow
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rate increasing, the differences between the measured values and numerical predicted values are
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not significant.
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The mixing time is an another important parameter for reflecting the mixing process of fluid in
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the RH reactor. Usually, the time when the tracer concentration (e,f/) achieves a narrow range
11
of ±5 pct of the final concentration (e,∞/) is defined as the mixing time [7,12,15-20]. Fig.4
12
shows comparison of mixing time between numerical simulation and water model experiment at
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the same gas flow rate. Here, monitor point B and C are below the ladle free surface with a
14
reduced size (120 mm), and the diagrammatic sketch of location can be found in Fig.5.
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Apparently, the mixing time from both numerical simulation and water model experiment are
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basically synchronized, proving that the numerical simulation results match well with the water
17
model results. It should be noticed that the concentration curve amplitude of water model of each
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case is slightly greater than that of numerical simulation. This phenomenon can be explained as
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follows: The saturated NaCl solution was used as the tracer in the water model experiment.
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Although the fluid flow pattern is the dominated factor to determine the concentration curve, the
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diffusion process of NaCl solution also affect its concentration change process. But in the
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numerical simulation, due to the diffusion coefficient of tracer was set at a very small value, the
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diffusion process of tracer was ignored.
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3.2 Method of optimizing sampling location
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In this section, the tracer experiment has been proved to be treated as the method of
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optimizing sampling location. Some key parameters for the follow-up optimization study were
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obtained from the tracer concentration curve, and the details of optimization scheme were
28
introduced.
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3.2.1 Characteristics of tracer concentration curve
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In this study, the tracer experiments were investigated in a full size RH using the verified
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mathematical model. Fig.6 shows the variety of tracer concentration of point A with different 8
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computational time. At first, the concentration of tracer at point A is zero till the tracer flow to
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this point. Subsequently, the tracer concentration of curve sharply increases, reaches a peak value,
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and then decreases. The oscillation phenomena repeats, but the amplitude finally decreases with
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the computational time. Here, the first turning point of concentration curve is defined as starting
5
mixing time (SMT). Between the starting mixing time with mixing time, the concentration curve
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of point A has several waves. The time interval between two troughs is defined as the time for
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circulation between the ladle and vacuum. Zhang and Li [7] pointed out that the mixing time
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typically equals to three to four times of the circulation time. It can be seen in Fig.6 that, at point
9
A, the tracer completes three circulations to reach the mixing time, and its dose that flow to this
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point at each circulation can be reflected by the amplitude of each wave.
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3.2.2 Comparison of tracer concentration curve between the difference monitor points
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Fig.7 shows the comparison of tracer concentration curve between the difference monitor
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points. As the location of each monitor point is different, the mixing time of each point exists the
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certain difference. In addition, before the mixing time, the features of concentration curve
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between different monitor points are also different. All of these results indicate that the mixing
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processes of molten steel at different locations of ladle are extremely different. It should be
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emphasized that some key parameters for the follow-up optimization study can be obtained from
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Fig.7. The first key parameter is the starting mixing time (SMT). Fig.7 shows that point A has
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the shortest SMT value, whereas the SMT value of point C is largest. In the present study, the
20
tracer was added into the RH reactor from the nozzles, and began to flow from the up-snorkel. If
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the tracer can be represented as the refined molten steel, which reacts in the up-snorkel and
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vacuum chamber, it is not hard to image that the refined molten steel from the top parts should
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first flow to point A compared to point B, C and D. With divergent consideration of it, if the
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sampling point which has the shortest SMT value in the ladle can be found, this point can be
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reasonably used to monitor the refining effect of molten steel in the up-snorkel and vacuum
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chamber. In contrary, if the sampling point which has the largest SMT value in the ladle, the
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mixing process of molten steel at this point is slowest in the ladle.
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An another interesting result can be seen in Fig.7 that the change rates of tracer concentration
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at different sampling points are different. To reach the mixing time, point A goes through three
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circulations, point B has two circulations, and only one circulation for point C. However, for
31
point D, it has no the complete wave. This is because that point D is located in the left eddy zone, 9
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and the existence of eddy zone retards the mixing process of tracer. Therefore, this interesting
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result represents that the change rate of chemical composition of molten steel at different
3
sampling points is related to its circulation number. The more the circulation number, the higher
4
the change rate of chemical composition. Moreover, Fig.7 shows that the curve amplitude of
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point A is significant larger than that of other’s points, because the point A is just located in the
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bottom of the down-snorkel, indicating the tracer dose flow to point A is higher than that of
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other’s points. It means that, if the tracer can be treated as the refined molten steel, getting the
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sample from point A is more representative than from other’s points.
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From the above discussions, it is clear that the mixing process of molten steel at different
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points in the ladle can be intuitively reflected by the tracer concentration curve, and the starting
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mixing time, circulation number and curve amplitude can be treated as three key parameters to
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characterize the mixing process.
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3.2.3 Descriptions of optimization scheme
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In the industrial sampling process, steelmakers usually use the sampling rod to take the
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lollipop sample from the ladle. As shown in Fig.5, the molten steel can be collected in the
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lollipop mold, which is installed at the top of the sampling rod. Owing to the limitation of length
17
of sampling rod, the actual sampling depth is in a range of 300-400 mm below the slag layer.
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Fig.8 shows the flow pattern and steel velocity at different sampling layers. As shown, the
19
differences of flow pattern and steel velocity between different layers are not significant. Also,
20
the flow pattern of molten steel at side I and II can be regarded as symmetrical. Therefore, in this
21
study, the sampling layer was selected at the intermediate value (350 mm), and all potential
22
sampling points were chosen at one side, as shown in Fig.9. In order to better distinguish the
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sampling points, the sampling layer was divided into three sub-regions: up-snorkel region, down-
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snorkel region and middle region, and each of sub-region has its own potential sampling points.
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3.3 Comparison of the mixing process between different sampling points
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3.3.1 Optimal sampling location
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Fig.10 shows the variety of tracer concentration at each sampling point in different sub-
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regions. In the up-snorkel sub-region, the difference of SMT value between each sampling point
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is not significant, and it proves that the molten steel basically flows to this region at the same
30
time. The SMT value of point 12 is lowest, whereas point 13 and point 14 have the largest SMT
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value, as shown in Fig.10(a). Fig.10(b) shows the difference of SMT value between each 10
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sampling point in the middle sub-region tends to increase. In this region, the SMT value
2
gradually increases along the reverse direction of Y axis (Fig.9). Point 8 and point 9 have the
3
lowest value, and point 5 has the largest value. Similarly, in the down-snorkel sub-region, point 3
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holds the lowest SMT value, but point 1 has an extremely high value of SMT, as shown in
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Fig.10(c).
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Fig.11 (a) shows the comparison of the minimum SMT value between each sub-region.
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Apparently, both point 8 and point 9 have the lowest SMT value. It represents that the refined
8
molten steel from the top sealed parts (vacuum chamber and snorkels) firstly flows to point 8 and
9
point 9 compared to all sampling points in the ladle. Moreover, the curve amplitude of point 9 is
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significantly greater than that of point 8, indicating that getting the sample from point 9 is more
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representative than from point 8. Therefore, to make a correct and timely test of the molten steel
12
chemistry in the up-snorkel and vacuum chamber for monitoring the refining effect, point 9 is the
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optimal sampling location. In addition, Fig.11 (b) shows that, compared to all sampling points,
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point 1 not only holds the largest value of SMT, but also only has one circulation number. It can
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be seen in Fig.12 that the downward stream of molten steel touches the base of the ladle with a
16
high velocity, and leads to two eddy regions developing in the ladle. Special attention should be
17
paid to the “right eddy”, which is only located in the middle and lower part of the ladle. After the
18
molten steel flows out of the “right eddy, it does not go straight up to the ladle free surface, but
19
meets the other molten steel flowing from ladle free surface. Obviously, two new small eddies
20
generate at two sides. Here, the region located between these two eddies is defined as the “dead
21
region”. As mentioned before, the existence of eddy could retard the mixing process of molten
22
steel. Due to the point 1 is just in this “dead region”, the concentration curve of point 1 has
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smallest curve amplitude and the least circulation number compared to all sampling points. All
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of these results represent that, at point 1, the molten steel has the latest starting mixing time, the
25
least amount of updates and the slowest change rate of chemical composition. Therefore, if
26
steelmakers need to obtain a representative sample of molten steel for ensuring that all of molten
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steel in the ladle are up to the standard before the end of the RH refining process, it is better to
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take samples from point 1.
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3.3.2 Effect of the operation parameters
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In the actual RH process, the operation parameters, such as gas flow rate, vacuum pressure and
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immersion depth of snorkel, can not be always maintained at the certain value. These operation 11
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parameters usually can be adjusted in a proper range, and hence the effect of operation
2
parameters on the obtained sampling locations should be investigated. For example, for the 210 t
3
size of RH reactor, the reasonable gas flow rates are usually controlled at 90-180 m3/h. Fig.13
4
exhibits that the flow pattern of molten steel in the ladle changes with its velocity when the gas
5
flow rate is low (30-60 m3/h). The velocity of molten steel increases with the higher gas flow rate
6
(60-120 m3/h). However, when the velocity of molten steel reaches a certain value, the molten
7
steel will maintain the steady pattern to flow in the ladle. In this study, this certain value is within
8
the range of 30-60 m3/h.
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Fig.14 shows the comparison of tracer concentration curve between different gas flow rates.
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As shown, under different gas flow rate conditions, the concentration curve just change its
11
distribution, but has no significant change on its shape. It proves that the obtained optimal
12
sampling locations are valid within the actual gas flow rates. In addition, for other operation
13
parameters, such as vacuum pressure and immersion depth of snorkel, the effects of these two
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parameter are same to that of gas flow rate, due to these two parameters only affect the
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circulation flow rate of molten steel [25-27].
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3.4 Industrial trials
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In this study, industrial trials were carried out to validate the obtained optimal sampling
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locations during the RH refining of IF steel. The [C] content of sample was used as the indicator,
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and the whole decarburization process of IF steel (12-15 min) was monitored. During the
20
decarburization process, the vacuum pressure was set at 133 Pa, and the gas flow rates injected
21
into the up-snorkel were controlled at 90-120 m3/h. The lollipop samples were taken from the
22
corresponding sampling location every 2.0-3.0 min until the end of decarburization treatment.
23
The samples were taken at a depth of 300-400 mm below the ladle free surface after the crushing
24
slag process, and quickly cooled in water. Therefore, the [C] content in the sample was
25
representative of the melt at each of sampling time. Moreover, it should be noticed that only one
26
sample at the beginning or ending of decarburization process was analyzed in some heats, and
27
some of the deviation data were not used.
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Fig.15 shows the varieties of [C] content of IF steel during the RH refining process at different
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sampling locations. Here, point 1 and point 9 are the optimal sampling locations mentioned
30
above, and point 3 and Point 12 are treated as contrast. Obviously, the decreasing rate of [C]
31
content at point 1 is slowest, indicating that the amount of the refined molten steel from the top 12
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sealed parts flows to this point is few, especially in 1-2 minutes. It once again proves that the
2
sample taken from point 1 can completely ensure the whole ladle of molten steel reaches the
3
standard. In addition, the decreasing rate of [C] content at point 9 is fastest compared to that of
4
point 3 and point 12, although its advantage is not significant in some heats. Therefore, point 9 is
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the optimal sampling location for getting the representative composition to monitor the refining
6
effects.
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4. Conclusions
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The analyses of the flow and mixing characteristic of fluid from both mathematical and water
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modeling allowed the present researchers to come up with some useful recommendations about
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the importance of the sampling location. The crucial conclusions include:
1. Numerical results of flow pattern, circulation flow rate, and mixing time agree well with the
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water model results, so the gas–liquid flow in the RH reactor can be predicted using the present
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mathematical model.
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2. The tracer experiment has been proved to be treated as the method of optimizing sampling
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location. The starting mixing time (SMT), circulation number and curve amplitude are three key
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parameters to characterize the mixing process.
3. By comparing a large number of potential sampling points, point 9 has been treated as the
18
optimal sampling location for monitoring the refining process of molten steel in the up-snorkel
19
and vacuum chamber. In addition, point 1 is the best sampling location in the steel plant as it will
20
guarantee a homogenous representative sample of molten steel and ensure that it is up to the
21
standard before the end of the RH refining process.
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REFERENCES
23
[1] H. Maas, P. Hupfer. The significant of the circulation rate on the vacuum treatment of liquid
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steel with the RH process, Vacuum 19 (1969) 199-203. [2] V. Presen. Recent trends and the future of vacuum in steel making, Vacuum 43 (1992) 373379.
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[3] D.Q. Geng, J.X. Zheng, K. Wang, P. Wang, R.Q. Liang, H.T. Liu, H. Lei, J.C. He.
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Simulation on decarburization and inclusion removal process in the Ruhrstahl-Heraeus (RH)
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process with ladle bottom blowing. Metall. Mater. Trans. B 46 (2015) 1484-1493.
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[4] J. Zhang, L. Liu, X. Zhao, S. Lei, Q. Dong. Mathematical model for decarburization process in RH refining process, ISIJ Int. 54 (2014) 1560-1569. [5] K. Steneholm, M. Andersson, ATilliander, P.G. Jonsson. Removal of hydrogen, nitrogen
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and sulphur from tool steel during vacuum degassing, Ironmak. Steelmak. 40 (2013) 199-
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205.
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[6] J. Han, X.D. Wang, D.C. Ba. Coordinated analysis of multiple factors of argon blowing
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parameters on the effect of circulation flow rate in RH vacuum refining process, Vacuum
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109 (2014) 68-73.
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[7] L. Zhang, F. Li. Investigation on the fluid flow and mixing phenomena in a Ruhrstahl-
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Heraeus (RH) steel degasser using physical modeling, JOM 66 (2014) 1227-1240. [8] M.A. Van Ende, Y.M. Kim, M.K. Cho, J. Choi, I.H. Jung. A kinetic model for the
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Ruhrstahl-Heraeus (RH) degassing process, Metall. Mater. Trans. B 42 (2011) 477-489.
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[9] B. Kleimt, S. Kohle, K.P. Johann, A. Jungreithmeier, J. Molinero. Dynamic process model
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for denitrogenation and dehydrogenation by vacuum degassing, Scand. J. Metall. 29 (2000)
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194-205.
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[10] Y. Li, Y. Bao, R. Wang, M. Wang, Q. Huang, Y. Li. Modeling of liquid level and bubble behavior in vacuum chamber of RH process, J. Iron Steel Res. Int. 23 (2016) 305-313.
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[11] H. Ling, C. Guo, A.N. Conejo, F. Li, L. Zhang. Effect of snorkel shape and number of
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nozzles on mixing phenomena in the RH process by physical modeling, Metall. Res.
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Technol. 114 (2017) 111.
[12] D. Mukherjee, A.K. Shukla, D.G. Senk. Cold model-based investigations to study the effects
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of operational and nonoperational parameters on the Ruhrstahl–Heraeus degassing process,
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Metall. Mater. Trans. B 48 (2017) 1-7.
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[13] W. Zheng, Z. He, G. Li, H. Tu, Z. Zhang. Physical simulation of effect of injection
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parameters on circulation flow rate and mixing time in RH vacuum refining process, J. Iron
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Steel Res. 29 (2017) 373-381.
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[14] K. Yoshitomi, M. Nagase, M.A. Uddin, Y. Kato. Fluid mixing in ladle of RH degasser induced by down flow, ISIJ Int. 56 (2016) 1119-1123. [15] D.Q. Geng, H. Lei, J.C. He. Simulation on flow field and mixing phenomenon in RH degasser with ladle bottom blowing, Ironmak. Steelmak. 39 (2012) 431-438.
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[16] D.Q. Geng, H. Lei, J.C. He. Numerical simulation of the multiphase flow in the RheinsahlHeraeus (RH) system, Metall. Mater. Trans. B 41 (2010) 234-247. [17] B. Zhu, Q. Liu, M. Kong, J. Yang, D. Li, K. Chattopadhyay. Effect of interphase forces on
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gas-liquid multiphase flow in RH degasser, Metall. Mater. Trans. B 48 (2017) 2620-2630.
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[18] G. Chen, S. He. Mixing behavior in the RH degasser with bottom gas injection, Vacuum 130
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(2016) 48-55.
[19] K. Feng, A. Liu, K. Dai, S. Feng, J. Ma, J. Xie, B. Wang, Y. Yu, J. Zhang. Effect of gas rate
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and position of powder injection on RH refining process using numerical simulation, Powder
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Tech. 314 (2017) 649-659.
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[20] G. Chen, S. He, Y. Li, Y. Guo, Q. Wang. Investigation of gas and liquid multiphase flow in
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the Rheinsahl–Heraeus(RH) reactor by using the Euler–Euler approach, JOM 68 (2016)
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2138-2148.
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[21] B. Zhu, Q. Liu, D. Zhao, S. Ren, M. Xu, B. Yang, B. Hu. Effect of nozzle blockage on
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circulation flow rate in up-snorkel during the RH degasser process, Steel Res. Int. 87 (2016)
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136-145.
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[22] M. Sano, K. Mori, Y. Fujita. Dispersion of gas injected into liquid metal, Tetsu-to-Hagane 65 (1980) 1140-1148.
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[23] Ansys Inc.: ANSYS CFX-Solver Theory Guide, Release 12.1, Canonsburg, 2009, 101–150.
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[24] Y. Sato, M. Sadatomi, K. Sekoguchi. Momentum and heat transfer in two-phase bubble
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flow-II. A comparison between experimental data and theoretical calculations, Int. J.
21
Multiphas. Flow 7 (1981) 179-190.
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[25] Y.G. Park, K.W. Yi, S.B. An. The effect of operating parameters and dimensions of the RH system melt circulation using numerical calculations, ISIJ Int. 41 (2001) 403-409.
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[26] P.A. Kishan, S.K. Dash. Prediction of circulation flow rate in the RH degasser using discrete phase particle modeling, ISIJ Int. 49 (2009) 495-504. [27] S.K. Ajmani, S.K. Dash,S.Chandra, C. Bhanu. Mixing evaluation in the RH process using mathematical modelling, ISIJ Int. 44 (2004) 82-90.
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Table List
Table 1 Parameters of prototype and water model Prototype
Water model
Upper/bottom ladle diameter, mm
3772/3200
754/640
Diameter of vacuum chamber, mm
2150
Snorkels length/diameter, mm
1650/600
Distance between longitudinal axes, mm
1500
Nozzle diameter, mm
5
Viscosity of liquid, Pa·s
0.006
Surface tension, N/m
1.5
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430
310 1
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330/120
8.9E-5 0.06
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Table list Fig.1 Simulation results of molten steel flow pattern in an industrial scale RH reactor, at the gas flow rate of 120 m3/h.
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Fig.2 Flow process of blue ink in the water model, at gas flow rate 2.63 m3/h. Fig.3 Variety of circulation flow rate at different gas flow rates.
Fig.4 Comparison of mixing time between numerical simulation and water model experiment, at gas flow rate of 2.63 m3/h.
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Fig.5 Locations of monitor points and industrial sampling tool.
Fig.6 Variety of tracer concentration of point A with different computational time, at
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Fig.8 Flow pattern and steel velocity at different sampling layers, at the gas flow rate
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Fig.9 Locations of sampling point in different sub-regions. Fig.10 (a, b, c) Variety of tracer concentration at each sampling point in different
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sub-regions, (a) up-snorkel region, (b) middle region and (c) down-snorkel region. Fig.11 (a, b) Comparisons of the maximum and minimum SMT value between each
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sub-region: (a) lowest SMT value, (b) largest SMT value. Fig.12 Flow pattern of molten steel in the ladle. Fig.13 Flow pattern of molten steel in the ladle at different gas flow rates, (a) 30 m3/h, (b) 60 m3/h, (c) 90 m3/h and (d)120 m3/h. Fig.14 Comparison of tracer concentration curve between the different gas flow rates. Fig.15 Varieties of [C] content of IF steel during the RH refining process at different sampling locations.
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Fig.1 Simulation results of molten steel flow pattern in an industrial scale RH reactor,
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at the gas flow rate of 120 m3/h.
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Fig.2 Flow process of blue ink in the water model, at gas flow rate 2.63 m3/h.
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Fig.3 Variety of circulation flow rate at different gas flow rates.
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Fig.4 Comparison of mixing time between numerical simulation and water model
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experiment, at gas flow rate of 2.63 m3/h.
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Fig.5 Locations of monitor points and industrial sampling tool.
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Fig.6 Variety of tracer concentration of point A with different computational time, at
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Fig.7 Comparison of tracer concentration curve between the difference monitor
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Fig.8 Flow pattern and steel velocity at different sampling layers, at the gas flow rate
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Fig.9 Locations of sampling point in different sub-regions.
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Fig.10 (a, b, c) Variety of tracer concentration at each sampling point in different sub-regions, (a) up-snorkel region, (b) middle region and (c) down-snorkel region.
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Fig.11 (a, b) Comparisons of the maximum and minimum SMT value between each sub-region: (a) lowest SMT value, (b) largest SMT value.
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Fig.13 Flow pattern of molten steel in the ladle at different gas flow rates, (a) 30 m3/h,
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(b) 60 m3/h, (c) 90 m3/h and (d)120 m3/h.
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Fig.14 Comparison of tracer concentration curve between the different gas flow rates.
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Fig.15 Varieties of [C] content of IF steel during the RH refining process at different
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sampling locations.
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Highlights
1) The fluid flow and mixing behavior in the RH degasser were investigated in both numerical method and water modeling technique.
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2) The tracer experiment is ingeniously treated as the method of optimizing sampling location.
3) Some optimal sampling locations were proposed, and verified by the industrial
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trials.