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70
The Effects of Silicon on Desulfurization in 
Aluminum-Killed Steels
Author
A. Pitts-Baggett 
melting and casting process 
metallurgist, Nucor Steel Tuscaloosa 
Inc., Tuscaloosa, Ala., USA
april.baggett@nucor.com
In Al-killed steels, the ability to remove sulfur is frequently studied. It has been determined that 
the presence of silicon affects the sulfur equilibrium. This aids in the ability for the slag to capture 
sulfur more effectively. A comparison of equations for sulfide capacities in slags was evaluated. 
The ability to predict final sulfur in an addition to sulfur equilibrium was also investigated. A new 
equation was suggested to calculate the sulfur equilibrium to be able to predict final sulfur more 
accurately for the current data set. Also, the accepted conditions for desulfurization were explored. 
The data set showed good agreement with the accepted conditions needed for desulfurization.
Aluminum-killed steels allow for low oxygen potential in second-
ary refining, thus resulting in the 
ability to remove the sulfur in the 
steel to the slag. This reduction 
ultimately has an effect on the inclu-
sion composition and morphology 
throughout the secondary refining 
process.1 Experimentally, in both 
the laboratory and in industry, it 
has been shown that the sulfur con-
tent of the steel bath at the time of 
calcium treatment can result in a 
different reaction other than modi-
fying alumina.2 CaS was formed 
after wire treatment when the sul-
fur content of the steel was high 
(>40 ppm). For low-sulfur heats 
(approximately 7 ppm), CaS forma-
tion was not initially seen after wire 
treatment; however, it formed CaO 
or CaAl. This validates the critical-
ity of when calcium is introduced 
into the heat. Calcium can be intro-
duced in the form of wire treatment, 
as well as in a residual elemen-
tal form, in particular with FeSi. 
During wire treatment, if the sulfur 
is high, the Ca attempts to react/
bond with sulfur and alumina simul-
taneously. In a production environ-
ment, this results in the inability 
to uniformly modify the alumina-
based inclusions. This illustrates 
the reason that desulfurization is 
critical, as one-way silicon can affect 
sulfur removal. As shown in Eq. 1, 
the desulfurization process occurs 
by the dissolved lime (CaO) in the 
slag interacting with the sulfur in 
the steel during slag/steel mixing. 
However, it has also been seen that 
the presence of silicon (Si) can also 
aid the ability to remove sulfur:3
3 (CaO) + 2[Al] + 
3[S] → (Al2O3) + 3(CaS)
(Eq. 1)
where ( ) represents the component 
in the slag and [ ] represents the 
component in the steel.
The conditions needed to achieve 
a high desulfurization rate are:4
 • High temperature.
 • Fluid slag.
 • High mixing conditions at 
the slag/steel interface.
 • Aluminum.
 • Low oxygen potential.
For efficient sulfur removal, all of 
these conditions should be present. 
If these conditions are departed, con-
densed or altered, it will ultimately 
affect the desulfurization rate of the 
steel as well as the time required to 
desulfurize the steel to the desired 
target. Desulfurization is not just 
dependent on thermodynamics, but 
This article is available online at AIST.org for 30 days following publication.
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mailto:April.Baggett@nucor.com
71
OCT 2018 I IRON &
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also on kinetics.5 Therefore, producing an accurate 
and efficient industrial model that can predict desul-
furization removal rates and sulfur capacities of slags 
is very difficult. A few models have been developed to 
calculate the sulfide capacity of slags.6–9 The sulfide 
capacity of the slag is dependent on several variables, 
including the oxides in the slag such as iron oxide 
(FeO) and manganese oxide (MnO), the volume 
and viscosity of the slag, the arrival sulfur wt.%, the 
temperature (most models assume a constant tem-
perature), etc.10,11 It has also been seen that when 
the FeO and MnO content of the slag decreases, the 
desulfurization rate is, in turn, increased.12 This is 
related to both the kinetic and the thermodynamic 
relationships. The slag/steel interface is a critical part 
of the secondary refining process, including its ability 
to capture alumina inclusions and sulfides. However, 
to achieve the ideal properties of the slag for inclu-
sion capture, the availability of deoxidants such as 
aluminum at the slag/steel interface have to be pres-
ent in order to remove the oxygen from iron (Fe) and 
manganese (Mn), and hence “killing the slag.” After 
the steel and slag are “killed,” the steel has a low oxy-
gen potential, which prompts desulfurization. The stir 
rate, along with the porous plug design in the ladle, 
has a direct impact on the slag/steel interface interac-
tions. A quicker desulfurization rate is achieved when 
the flowrate is higher due to the increased surface 
area created at the slag/steel interface. It has also 
been shown that the lower the optical basicity, the 
higher the sulfide capacity of the slag. This is shown 
in Fig. 1.
Multiple equations exist to calculate sulfide capaci-
ty, CS. For this study, the equation developed by Young 
et al. and Somerville et al. will be compared using the 
experimental results. Young’s calculation is shown in 
Eq. 2.9 It incorporates optical basicity (Λ) as well as 
Al2O3 and SiO2 wt.% from the slag. Somerville’s calcu-
lation is a more condensed version only incorporating 
optical basicity and temperature (T), which is shown 
in Eq. 3.14
log . . .
. % .
C
T
SiO
S      
  
13 913 42 84 23 82
11710
0 02223 0 022
2
2
Λ Λ
775 2 3%Al O 
(Eq. 2)
log . .C
TS  
       
22690 54640
43 6 25 2
Λ
Λ
(Eq. 3)
Being able to determine the sulfide capacity of the 
slag is important, especially in understanding which 
components improve the slag’s ability to capture sul-
fur. It is also important to be able to calculate the pre-
dicted final sulfur percentage. To be able to do this, 
the equilibrium sulfur value is needed since the heat 
approaches equilibrium toward the end of the pro-
cessing time. The following series of equations can be 
used to predict the final sulfur (or sulfur equilibrium) 
of the steel. Eq. 4 calculates the mass transfer coef-
ficient. This equation is used to calculate the kinetic 
constant, k, in Eq. 5.15 After k has been determined, it 
is placed into Eq. 6 to solve for the sulfur equilibrium. 
Turkdogan also developed an equation for predicting 
the final sulfur as shown in Eq 7,16 where Si is the ini-
tial sulfur percentage, Seq is the sulfur content that is 
in equilibrium with the slag, and Sf is the final sulfur 
content of the heat. The final sulfur and equilibrium 
sulfur will be compared to the data set in this study.
m
W
A
W
As
  
 
  
 
  5 49 10 8 27 10 1 499 1010
2
7
2
. . .
(Eq. 4)
k
m
W
W
W
Ls
slag
S
 
 

  








ρ Α
1
3000 24
ln
(Eq. 5)
Sulfide capacity vs. optical basicity.13
Figure 1
2.0
3.0
4.0
5.0
0.55 0.60 0.65 0.70 0.75 0.80 0.85
T=1,500°C
Ʌ, Optical Basicity
-lo
g 
C
s
CaO-Al2O3
CaO-SiO2
CaO-Al2O3-SiO2
CaO-MgO-Al2O3
CaO-SiO2-B2O3
CaO-MgO-SiO2
CaO-MgO-Al2O3-SiO2
2pts.
2pts.
2pts.
2pts.
2pts.
2pts.
2pts.
2pts.
2pts.
2pts.
2pts.
2pts.
2pts.
2pts.
4pts.
3pts.
3pts.
3pts.
3pts.
3pts.
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72 Technical Article
%S
S W
W L W
eq
i
S slag



 
 
  
(Eq. 6)
% % % %S S S S ef eq i eq
k t     
(Eq. 7)
where 
W = weight of the metal,
A = surface area of the slag/steel interface, 
ρ = density of the steel,
Wslag = weight of the slag, 
LS = sulfur distribution ratio,
%Si = initial sulfur content,
k' = rate constant and
t = time.
Though it is usually considered ideal to have the 
lowest sulfur value possible, it has beenseen that when 
the sulfur wt.% drops below a particular threshold, it 
makes predicting accurate calcium treatment more 
challenging. This topic is discussed more in-depth in 
Pitts-Baggett et al.17
Experimental Procedure 
The sample location used in this study included a steel 
sample taken from a robot to ensure repetitive depth, 
which was also the location where temperatures were 
collected. A slag sample was also taken during the 
same sampling time. The slag sample was taken from 
the side of the ladle metallurgy furnace (LMF) oppo-
site the stir eye, as shown in previous studies to be the 
most representative of the bulk slag.18 All of the slag 
samples were prepped and then analyzed in a Bruker 
AXS machine to determine slag composition. The 
components used in this study from the slag samples 
were CaO, Al2O3, Fe2O3, MnO, SO3, SiO2 and TiO2. 
The lollipop samples are analyzed by using a Thermo 
Fisher Scientific spectrometer for chemistry analysis.
Targeting sampling times of the LMF process was 
also of importance. The key sampling times chosen 
throughout the heat were heat arrival/starting (S), 
after alloying (A), after desulfurization/the start of 
pre-rinse (R), prior to calcium treatment/end of pre-
rinse (C) and after the end of post-rinse/end of heat 
(E). A depiction of this is shown in Fig. 2. This sample 
set was taken over nine different heats to be able to 
compare the effects of silicon throughout the process: 
four restricted-silicon heats (RSI), one open-tapped 
RSI and four Si-bearing (SI).
Results and Discussion 
Slag is one of the major components responsible for 
removing sulfur. It is the only phase that is capable of 
removing sulfur from the steel. Fig. 3 shows the abil-
ity of the slag to capture sulfur. This figure shows the 
sulfide percentage in the slag increasing throughout 
the heat, resulting in a decrease in the sulfur in the 
steel. It also shows that the sulfur removal in the steel 
tends to level off roughly about 10 minutes into the 
heat (sample A). To depict the sulfur removal from 
the steel in conjunction with the sulfide increase in 
the slag, a graph of heat RSI2 has the two mechanisms 
plotted together in Fig. 4.
The ability of a slag to capture sulfur is of interest 
to industry. The sulfide capacity of a slag (Cs) has 
been studied by several researchers. This value is typi-
cally reported as the log (Cs). It represents how well 
a slag can capture sulfur rather than specifically how 
much sulfur can be captured. To calculate Cs, the 
optical basicity (Λ) is used. The values used for Λ are 
Key process sampling locations.19
Figure 2
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OCT 2018 I IRON &
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shown in Table 1. Fig. 5a is used to compare Eqs. 2 
and 3. The RSI heats align closely together with the 
exception of the open-tapped heat (RSIOT). However, 
the Si-bearing heats showed a significant amount of 
variation. Fig. 5b uses the data from this study dis-
played in the same format as Fig. 1; the data set aligns 
reasonably well with the data shown in Fig. 1 for CaO-
Al2O3 slags. Though the sulfide capacity of the slags 
in this data set did not vary greatly, it was seen that 
Young’s model had better agreement across all heats, 
including Si-bearing heats.
Sulfide capacity of the slag is only one piece of sul-
fur removal. The rate with which a heat can remove 
sulfur is also valuable to steel producers. Eq. 5 from 
Fruehan was used to calculate the desulfurization 
rate of each heat. This then allowed for the %Seq to 
be determined. However, it was noted that the sulfur 
equilibrium for Si-bearing heats predicted a sulfur 
value that was higher than the final sulfur value of 
the heat, which cannot be correct. A plot was created 
for each heat using Eq. 8. These plots were created by 
adjusting the %Seq to create the best possible R2 value 
Sulfide concentration of slag throughout key LMF process 
steps.18
Figure 3
RSI1 
E S A R C 
SI4 
RSI4 
SI3 
SI2 
RSIOT 
SI1* 
RSI3 
RSI2 
1.5 
1.2 
0.9 
0.6 
0.3 
0 
SO3 
%
 S
O
3 
Depictions of sulfur transfer from the steel to the slag vs. time 
from RSI2.18
Figure 4
S SO3
Time (minutes)
 
60 50 40 30 
 
0 10 20 
0
0.01 
 
0 
0.5 
0.02 
1 
0.03 
1.5 0.04 
%
 S
 in
 t
he
 s
te
el 
%
 S
O
3 
in
 t
he
 s
la
g 
Table 1
Optical Basicity of Slag Components Used13
Oxide Optical basicity
CaO 1.00
MgO 0.78
TiO2 0.61
Al2O3 0.61
MnO 0.59
FeO 0.51
SiO2 0.48
Comparing Young and Somerville’s equations for sulfide capacity (a) and sulfide capacity vs. optical basicity (b).18
Figure 5
 
-4 
-3.5 
-3 
-2.5 
-2 
-1.5 
-1 
-0.5 
0 
Young Somerville 
 
-4 
-3.5 
-3 
-2.5 
-2 
-1.5 
-1 
Optical Basicity, Λ 
Young Somerville 
 
 
 
 
 
Lo
g 
(C
s)
 
Lo
g 
(C
s)
 
SI
2
0.
75
0.
76
0.
77
0.
78
0.
79
0.
80
0.
81
0.
82
0.
83
0.
84SI
1*
RS
IO
T
SI
3
SI
4
RS
I1
RS
14
RS
I3
RS
I2
(a) (b)
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74 Technical Article
on the trend line. An example of this for heat RSI2 is 
shown in Fig. 6. This was completed for all the heats 
in the data set. Table 2 shows the comparison of the 
experimental data set versus the calculated values for 
%Seq and k' along with their coinciding R2 values.
ln
% %
% %
S S
S S
k teq
i eq









  

(Eq. 8)
where 
%S = sulfur at time (t),
%Seq = sulfur equilibrium,
%Si = initial sulfur content and
k' = rate constant.
The rate constant, k', is the slope of the trend 
line created for each heat. k' is equivalent to the 
desulfurization rate, k, multiplied by the area of the 
slag/steel interface, and divided by the volume of the 
steel. It is assumed that the volume of the steel as 
well as the area of the slag/steel interface is constant; 
therefore, in this study the experimental k' will be 
used as the desulfurization rate.
It was determined that for this data set the pre-
dicted final sulfur value could not be calculated for 
the Si-bearing heats as shown in Table 3. This was 
due to the [%Seq] prediction. A set of equations was 
developed with the current data set and are shown 
in Eqs. 9 and 10. These equations incorporate the Si 
wt.% of the steel, which is believed to alter the sulfur 
equilibrium at the slag/steel interface during the 
LMF processing. The results of these equations as well 
as the predicted results from Eq. 7 are plotted against 
the actual final S wt.% content in the steel shown in 
Fig. 7. These equations need further validation and 
verification. They were built based only on the data 
set from this paper.
(Eq. 9)
(Eq. 10)
Incoming conditions to the LMF can have an influ-
ence on the ability to remove sulfur throughout a heat. 
As discussed, conditions such as aluminum, low oxy-
gen potential, temperature, stir and slag can have a 
dramatic effect on the desulfurization rate. This data 
set was also used to validate the criteria needed for 
desulfurization. At Nucor Steel Tuscaloosa Inc., alu-
minum is added at tap. This is to deoxidize the steel 
Sulfur equilibrium prediction plot for RSI2.18
Figure 6
0 
-1 
-2 
-3 
-4 
-5 
-6 
-7 
0 10 20 30 
Time (minutes) 
40 50 60 
 
 
 
 
 
 
 
ln
(S
-S
eq
/S
i-
S
eq
) 
y = -0.1189x
R2 = 0.9719
Table 2
%Seq and k' Comparison18
Experimental Fruehan
Heat name %Seq R2 k' %Seq R2 k'
RSI1 0.0020 0.85 –0.09 0.0021 0.84 –0.095
RSI2 0.0016 0.97 –0.12 0.001 0.83 –0.087
RSI3 0.0015 0.98 –0.11 0.0015 0.98 –0.11
SI1* 0.0002 0.98 –0.05 0.0038 0.1 –0.039
RSIOT 0.0037 0.99 –0.076 0.0022 0.85 –0.042
SI2 0.0043 0.97 –0.14 0.0069 –0.21 –0.003
SI3 0.0013 0.95 –0.175 0.0034 –0.22 –0.004
RSI4 0.0027 0.99 –0.083 0.0034 0.99 –0.091
SI4 0.0004 0.99 –0.067 0.0046 0.12 –0.066
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OCT 2018 I IRON &
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prior to the arrival at the LMF. The total arrival alu-
minumis a reflection of the tap oxygen present as well 
as any newly introduced oxygen from the Mn addition 
(also completed at tap). As shown in Fig. 8, the arrival 
aluminum does play a role in the desulfurization rate 
as indicated by the R2 value. This data set was also 
confirmed by the F-test significance factor of 0.043. 
The F-test significance factor should be below 0.15 to 
validate the significance. Eq. 1 shows aluminum must 
be present to remove sulfur; therefore, this result 
can be logically followed. Having a high aluminum 
wt.% present also ensures a lower dissolved oxygen. 
By use of FactSage, it is seen that the increase in wt.% 
aluminum results in a decrease in oxygen concentra-
tion as shown in Fig. 8b. This ultimately promotes 
desulfurization.
At increased temperatures the slag layer is more 
fluid, therefore promoting a faster desulfurization 
reaction. However, FactSage shows that desulfuriza-
tion is more favorable at lower temperatures. This is 
related to the kinetics of the slag/steel interface hav-
ing a much greater effect than the difference in ΔG 
as a function of temperature. The sulfur equilibrium 
is affected by the incoming temperature through the 
partition coefficient, LS. By plotting the incoming 
temperature versus the %Seq, as shown in Fig. 9, a cor-
relation is seen. This was also confirmed by the F-test 
significance factor being equivalent to 0.077 as well 
as a p-value of 0.069. The higher temperatures also 
allow for a slightly larger chemistry range where the 
slag layer is fluid, which can have a direct impact as 
well. Higher temperatures are not always favorable, as 
Comparison to actual final sulfur concentration vs. different 
models.18
Figure 7
Pitts-Baggett %Sf Turkdogan %Sf Actual %Sf 
0.006 
0.005 
0.004 
0.003 
0.002 
0.001 
0 
Prediction Versus Actual %Sf
 
%
 S
f 
RS
I1
RS
I2
RS
I3
SI
1*
RS
IO
T
SI
2
SI
3
RS
I4 SI
4
Table 3
Comparison of Actual %Sf to Turkdogan’s Equation and 
Eq. 1018
Heat
Actual 
%Sf
Turkdogan 
%Sf
Time step 
predicted
Eq. 10 
%Sf
Time step 
predicted
RSI1 0.0023 0.00231 12 0.00236 10
RSI2 0.0017 0.00168 10 0.00154 9
RSI3 0.0016 0.001612 16 0.00165 11
SI1* 0.0036 — — 0.00352 12
RSIOT 0.0038 0.00386 6 0.00392 6
SI2 0.0040 — — 0.00397 11
SI3 0.0014 — — 0.00121 8
RSI4 0.0049 0.00469 9 0.00488 8
SI4 0.0036 — — 0.0038 8
% Arrival Al vs. k' (a) and FactSage Al wt.% vs. oxygen ppm (b).18
Figure 8
0.20
 
0.15
 
0.10
 
0.05
 
0.00
 0.00 0.02 0.04 0.06 0.08 0.10
 
% Arrival Aluminum
 
250 
 
200 
 
150 
 
100 
 
50 
 
0 
0.00%
 
0.10% 0.20% 0.30%
 Aluminum wt.% 
 
 
R² = 0.6465 
 
 
 
 
 D
es
ul
fu
riz
at
io
n 
R
at
e,
 k
' 
O
xy
ge
n 
P
P
M
 
(a) (b)
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76 Technical Article
they can have a negative consequence on refractories 
if the slag layer is not saturated with lime.
Stirring conditions are also critical. If the stirring 
conditions in the ladle are maximized, the ability 
for the slag/steel interface mixing will be enhanced. 
This will promote the ability for the steel to reduce 
sulfur. Fig. 10 shows the correlation of the initial 
stir rate versus the desulfurization rate. Turkdogan 
previously studied and validated stir energy having a 
direct correlation to desulfurization rate.16 Therefore, 
the results shown below were expected. The F-test 
value for significance was 0.0049 along with a p-value 
of 0.035, which aligned well with the R2 value shown 
in the chart.
This study also confirms that a more fluid arrival slag 
will help with increased sulfur removal. Fig. 11 shows 
a linear correlation between total sulfur removed and 
the arrival slag B3. This observation was also validated 
using regression analysis, which showed a 0.021 F-test 
significance as well as a p-value of 0.0033.
The LMF secondary refining process is structured 
to have ideal conditions to desulfurize early in the 
process due to the temperature, fluid slag and ability 
to have a high stir rate. After the sulfur drops below 
a threshold, the removal rate slows down significantly. 
This threshold can vary by process parameters such 
as sulfide capacity in the slag, slag/steel interaction, 
oxygen presence, stir rate, etc. To investigate the 
removal of sulfur throughout the heat, the sulfur of 
the steel samples was plotted against the sampling 
time for that particular heat. To evaluate the effects 
of Si on the desulfurization process, the Si-bearing 
heats were plotted separately, Fig. 12a for RSI heats 
and Fig. 12b for Si-bearing heats. It was seen that the 
initial desulfurization removal rate is fairly similar 
for RSI heats. One anomaly that stands out is the 
dotted (yellow) line heat, RSIOT. This heat was the 
only open-tap (OT) heat sampled. This heat had 
the lowest arrival sulfur of the data set. This was due 
to the scrap mix being used at the EAF during that 
particular sampling time, and not related to the heat 
being open-tapped. The remaining heats were able 
to take advantage of the block tap additions, which 
allowed desulfurization to start prior to arrival at the 
LMF. These graphs also illustrate the arrival sulfur 
varies a considerable amount (0.02–0.045 wt.%). This 
is also correlated to the scrap mix being used during 
its particular sampling time. Another detail that can 
affect the total sulfur removal is the processing time. 
For these heats, the processing time ranged from 
38 to 70 minutes, which shows how important quick 
desulfurization is to the LMF process. The charts 
illustrate the desulfurization rate (slope of the line) is 
steep/quick within the first 10–20 minutes of the heat, 
then it levels off.
Investigating the differences between the two plots 
in Fig. 12, it was noticed that all the plots follow a simi-
lar pattern. However, it was noticed that the Si-bearing 
heats had much more variability and did not follow 
as tight of a pattern as the RSI heats. The Si-bearing 
heats had a broad range of arrival sulfur levels, 
0.048–0.027%, in addition to a slightly wide range for 
a processing time of 36–57 minutes. However, further 
investigation was completed to determine the unex-
pected variability seen in the Si-bearing chart.
Arrival temperature vs. %Seq.
18
Figure 9
 
0.0050 
 
0.0040 
 
0.0030 
 
0.0020 
 
0.0010 
 
0.0000 
1820 1840 1860 1880 1900 1920 
Temperature, K 
 
 
 
%
 S
eq
R2 = 0.492
Stir rate vs. desulfurization rate.18
Figure 10
 
0.21 
0.18 
0.15 
0.12 
0.09 
0.06 
0.03 
0.00 
1.0 1.1 1.2 1.3 1.4 1.5 
Initial Stir Rate (m3/s)
 
 
 
 
 
 
D
es
ul
fu
riz
at
io
n 
R
at
e
 
R² = 0.7006
Arrival B3 vs. total sulfur removed (RSIOT heat excluded).18
Figure 11
0.06 
 
0.05 
 
0.04 
 
0.03 
 
0.02 
 
0.01 
 
0.00 
1.25 1.35 1.45 1.55 1.65 1.75 
Arrival B3 
 
 
 
 
 
 T
ot
al
 S
ul
fu
r 
R
em
ov
ed 
R² = 0.6172
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OCT 2018 I IRON &
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It was seen that the SI1* heat contained a consider-
able amount of furnace slag carryover. This was seen 
through other studies completed on these heats. EAF 
slag carryover is inevitable; however, it is desired to 
minimize the amount of carryover present at the LMF. 
When excessive EAF slag carryover occurs, it increases 
amount of oxygen content considerably, which reduc-
es the ability of the heat to desulfurize. It was also 
seen that both SI1* and SI4 had higher arrival sulfur 
of the heats investigated. Both heats ended with a 
similar sulfur content; however, SI* had 25% longer 
processing time, which allowed more time for sulfur 
to reduce out of the heat. SI2 arrived at the LMF only 
25° higher than its final exit temperature, which is 
75° colder than other heats studied. This incoming 
temperature is not ideal for sulfur removal. Another 
barrier for SI2 was the incoming slag wasconsider-
ably higher than desired with a slag basicity, B3 of 1.7 
(ideal range 1.2–1.4). SI3 was an ideal heat for desul-
furization and sulfur removal. It had a lower starting 
sulfur wt.%, therefore resulting in the amount of total 
sulfur removal that was not as high as the other heats. 
However, the rate of removal was very high. This heat 
also had a calcium-aluminate addition at tap that 
heats SI4 and SI1* did not have. This addition allowed 
for the arrival slag to be lower in non-desirable oxide 
components such as MnO and FeO by dilution. The 
comparison of the discussed processing variables is 
shown in Table 4.
Since the heats studied were not able to determine 
whether Si affects the ability of the heat to remove sul-
fur, a data set of more than 2,600 heats was compiled. 
This data set contained a range of grades, including 
RSI and Si-bearing, and different arrival sulfurs due 
to various scrap mixes used throughout the data set. 
The data showed that for Si-bearing heats, the average 
final sulfur was 0.0032, and for RSI heats the average 
final sulfur was 0.0047. The standard deviation was 
0.0014 and 0.0016 for the aforementioned Si-bearing 
and RSI heats. This validates the presence of Si does 
help aid in removing sulfur from an aluminum-killed 
steel.
Conclusions 
It has been seen that silicon does promote the removal 
of sulfur, in particular FeSi, where there is a residual 
calcium wt.% present. However, it was also shown 
that Si affects the sulfur equilibrium at the slag/steel 
interface. It was suggested that Si is a variable for 
sulfur equilibrium and the prediction of the final 
sulfur. More developmental work is needed to vali-
date an equation that can be used to predict %Seq for 
Sulfur content in the steel throughout processing time for RSI (a) and Si-bearing (b).18
Figure 12
0.050
0.045
0.040
0.035
0.030
0.025
0.020
0.015
0.010
0.005
0
0 10 20 30 40 50 60 70 80
Time (min)
%
 S
ul
fu
r
0.050
0.045
0.040
0.035
0.030
0.025
0.020
0.015
0.010
0.005
0
0 10 20 30 40 50 60 70 80
Time (min)
%
 S
ul
fu
rRSI1
RSI2
RSI3
RSIOT
RSI4
SI1*
SI2
SI3
SI4
(a) (b)
Table 4
Si-Bearing Heat Data Related to Desulfurization Rates18
Heat
Desulfurization 
rate, k' Arrival temp (K) Sulfur removed Arrival B3 Arrival Al% Arrival MnO% Arrival FeO%
SI3 0.175 1,876 0.026 1.58 0.027 2.47 2.25
SI2 0.14 1,842 0.029 1.70 0.045 2.1 2.54
SI4 0.067 1,879 0.045 1.31 0.097 2.85 2.85
SI1* 0.05 1,892 0.045 1.26 0.051 6.35 3.98
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78 Technical Article
Si-bearing heats along with being able to predict final 
sulfur values. It was also shown that higher tempera-
tures, higher initial aluminum wt.% in the steel, lower 
B3 slags and a high-velocity stir promote desulfuriza-
tion. This matched current industrial knowledge and 
practices for sulfur removal.
Acknowledgments
The author would like to thank the LMF team at 
Nucor Steel Tuscaloosa Inc. for their time and efforts 
put into getting the trials completed. She would also 
like to thank her dissertation committee, in particular 
Ron O’Malley and Laurentiu Nastac.
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Did You Know?
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The agreement is conditional on EVRAZ executing these planned investments by 2024.
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