Study of H2S Removal at High Temperature with 20%Fe2O3/Al2O3 Sorbent
Applying Factorial Experimental Design Method
Ching-Ying Huang and Yau-Pin Chyou
Institute of Nuclear Energy Research, Longtan, Taoyuan County, Taiwan, R.O.C
Abstract
In gasification-based energy systems, e.g., an IGCC (Integrated Gasification Combined-Cycle)
system, the coal gasification process will generate large amounts of high-temperature syngas but will
also release H2S, COS, CS2, and other harmful gaseous pollutants. Many developed countries in the
world were devoted to research and development of high-temperature, dry desulfurization technology,
which can improve the thermal efficiency as well as reduce the cost of power generation and
environmental protection.
In this study, iron nitrate is impregnated on porous alumina and calcinated at 973K for 6 hours to
prepare the desulfurization sorbent. The sorbent is used as a high-temperature desulfurization agent for
the removal of hydrogen sulfide in a fixed-bed reactor. To improve the sulfur capacity of desulfurization
sorbent, this paper applies the 25 factorial experiment designs that not only apply the Two-way ANOVA
and regression analysis to analyze the experimental data but also verify the data completely by
experiment step design before studying the important operational factors. Finally, the impact factors of
the desulfurization reaction, the main effects and the interaction effects, can be identified. The 25 sets of
experimental data via conversion reduced the number of interaction impacts. The analysis obtained
indicates that the main effect is the reaction temperature (positive) and CO2 concentration (negative).
The interaction effect is the CO2 concentration & reaction temperature.
Keyword: gasification, IGCC (Integrated Gasification Combined-Cycle), desulfurization sorbent,
factorial experimental design
1. Introduction
Over the last decade, coal has met nearly half of the rise in global energy demand, and its demand is
growing faster. Whether or not coal demand will continue to rise as strongly as it is currently will
depend on the effectiveness of policy measures that favor lower-emissions energy sources, the
deployment of more efficient, coal-burning technologies, and, especially important in the long term,
carbon capture and storage (CCS) [1]. The Integrated Gasification Combined-Cycle (IGCC) is more
economical, with relatively lower penalties from CO2 capture, than its counterparts in natural gas
combined-cycle (NGCC) and pulverized coal (PC) power plants.
Gasification is a process that converts carbonaceous solid fuel, such as coal, biomass, petcoke, and
so on, into gaseous fuel called syngas. The main syngas components are hydrogen (H2), carbon
monoxide (CO), carbon dioxide (CO2), water vapor (H2O), and various impurities. The concentrations of
various components depend on the specific gasification process and the source employed. During
gasification, sulfur species will form hydrogen sulfide (H2S) and Carbonyl sulfide (COS). H2S is the
main sulfur species in syngas and accounts for approximately 93%~96% of the sulfur in this phase [2].
The sulfur species in the syngas need to be removed in order to meet the turbine protection specifications,
power plant environmental requirements, and other requirements for different end-use applications (such
1
as SOFC and PEM fuel cell) [3]. The hot gas desulfurization technology applied to integrated
gasification combined-cycle (IGCC) raises thermal efficiency and eliminates the need for sour water
treatment, compared to traditional low-temperature wet processes.
At present, many researchers have paid attention to research and development of solid
desulfurization sorbent and high-temperature process. Candidate sorbents for hot gas desulfurization
should meet several requirements, which include higher sulfur capacity, regeneration efficiency,
mechanical stability, chemical stability, etc., as mentioned in the literature [4]. Iron oxide sorbent has
been used favorably because of its high sulfur capacity and reactivity as well as its ease of regeneration
[5-6]. Besides, iron oxides are cheap and abundant in comparison with others. The mechanism of hot gas
desulfurization technology is as follows [7]:
MeO  H 2 S  MS  H 2O
3
MeS  O2  MeO  SO2
2
(1)
(2)
Evaluation of sorbents for hot gas desulfurization includes sulfur capacity, desulfurization
efficiency, re-generability and durability. In this study, sulfur capacity is used as the evaluation basis.
Referring to COP Z-SorbIII application, 500 ppmv was defined as the breakthrough point. The actual
sulfur capacity (MH2S) is expressed in the formulas (3) as follows [8]:

PFgas y H2S
M H2S   MWH2S 

Rg T


  ts


(3)
where MH2S is the actual sulfur captured by the sorbent, MWH2S is the molecular weight of H2S, P is the
absolute pressure, Fgas is the volumetric gas flow rate under the process condition, ygas is the inlet H2S
mole fraction, Rg is the universal gas constant, T is the absolute temperature and ts is the actual
sulfidation time until breakthrough (typically 500 ppm).
2. Experimental
2.1 Sorbent preparation and characterization
Iron(III)-nitrate (Alfa-Aesar, Fe(NO3)3．9H2O, 99.5%) was used to prepare the sorbent. Alumina
(Alfa-Aesar) was used as a support in this study. Desulfurization sorbent was prepared by the
impregnation method. Ten grams of alumina were ground up and sieved through 300 and 600 μm (30~50
meshes). Then, it was dried at 373K to deplete moisture before synthesis. The sorbent of
20wt%Fe2O3/Al2O3 was synthesized by mixing 12.86 g Fe(NO3)3．9H2O with suitable amounts of
DI-Water, and then the solution was poured onto the alumina support. The impregnated alumina support
was left to stand overnight at room temperature and dried in an oven for 24 hours to remove additional
moisture. The precursor was calcinated at 973K for 6 hours to synthesize the required sorbent.
2.2 Evaluation test of the sorbent
The chemical performance evaluation of the desulfurization sorbent was performed by a fixed-bed
reactor and a gas chromatograph (Varian 450 GC) equipped with a pulse flame photometric detector
(PFPD) and fitted with a GS-Q capillary column. The fixed-bed reactor system was shown in the Figure
1. The factor and set value of experimental design are shown in the Table 1. Table 2 is the 25 factorial
design configuration. According to the operation conditions of the Table 2, thirty-two group
experiments were performed to find out the significant factors of desulfurization reactions. In this table,
the plus and the minus represent high level and low level, respectively.
2
Figure 1. Desulfurrization fixeed-bed reacctor system
m
Taable 1. The symbols annd levels of factorial expperimental ddesign
Factor
Design parameter
H
High
level (+
+)
Loow level (-)
A
Tem
mperature
700oC
300oC
B
CO concentration
40%
10%
C
H2 conncentration
30%
10%
W
Weigh hourlly space vellocity
80000 (mL/hr-gg)
188000 (mL/hrr-g)
D
（W
WHSV）
E
CO2 cooncentrationn
10%
0%
Table 2. 25 faactorial desiign configurration
Run
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
A
－
＋
－
＋
－
＋
－
＋
－
＋
－
＋
－
＋
－
＋
B
－
－
＋
＋
－
－
＋
＋
－
－
＋
＋
－
－
＋
＋
C
－
－
－
－
＋
＋
＋
＋
－
－
－
－
＋
＋
＋
＋
D
－
－
－
－
－
－
－
－
＋
＋
＋
＋
＋
＋
＋
＋
E
－
－
－
－
－
－
－
－
－
－
－
－
－
－
－
－
3
Run
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
A
－
＋
－
＋
－
＋
－
＋
－
＋
－
＋
－
＋
－
＋
B
－
－
＋
＋
－
－
＋
＋
－
－
＋
＋
－
－
＋
＋
C
－
－
－
－
＋
＋
＋
＋
－
－
－
－
＋
＋
＋
＋
D
－
－
－
－
－
－
－
－
＋
＋
＋
＋
＋
＋
＋
＋
E
＋
＋
＋
＋
＋
＋
＋
＋
＋
＋
＋
＋
＋
＋
＋
＋
3. Results and Discussion
3.1 The chemical analysis results of 20 wt% Fe2O3/Al2O3 sorbent
Chemical analysis on the desulfurization sorbents was performed for different operating conditions
according to the experimental design configuration, shown in Table 2. The breakthrough time was
obtained by the breakthrough curve, which was used to calculate sulfur capacity of the sorbent by
formula (3). The sulfur capacities of run 1 to run 32 are shown in Table 3, in which the values from 0.93
to 7.71 g-S/100 g-sorbents are presented. The 32 sets of experimental results are observed by changing a
single factor to find its effect on the high temperature desulfurization reaction. It is found that higher
temperature (compared Run-1 with Run-2), higher CO concentration (compared run-2 with run-4),
lower CO2 concentration (compared Run-2 with Run-18), and lower H2 concentration (compared Run-2
with Run-6) were conducive to the desulfurization reaction. The experimental results could be explained
by the water-gas shift reaction, i.e., Reaction (4).
According to Le Chatelier’s Principle, as the concentration of CO increases, the reaction shifts
towards the right side of Reaction (4). Water formed from the desulfurization reaction, i.e., Reaction (5),
will be consumed. Additionally, as the concentration of H2 increases, the reaction shifts towards the left
side of Reaction (4). Thus, the water from desulfurization can’t be consumed. Therefore, increasing CO
concentration or decreasing H2/CO2 concentration enhances the desulfurization reaction. Lower water
content is conducive to the sulfidation reaction [9].
CO  H 2 O  CO 2  H 2
(4)
Fe 2 O3  2 H 2 S  H 2  2 FeS  3H 2 O
(5)
Table 3. The breakthrough time and sulfur capacity of the 32 set experiments
Run
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Breakthrough time
(min)
9.46
40.13
8.50
41.55
7.70
29.79
11.41
28.78
3.15
17.83
2.47
18.42
3.15
14.22
4.26
12.79
Sulfur capacity
(g-S/100 g-sorbents)
1.75
7.44
1.58
7.71
1.43
5.52
2.12
5.34
1.31
7.44
1.03
7.69
1.31
5.93
1.78
5.34
Run
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Breakthrough time
(min)
7.98
19.2
10.46
28.53
6.34
13.53
11.04
18.94
2.95
7.25
3.02
11.05
2.22
5.79
3.08
7.99
Sulfur capacity
(g-S/100 g-sorbents)
1.48
3.56
1.94
5.29
1.18
2.51
2.05
3.51
1.23
3.03
1.26
4.61
0.93
2.42
1.29
3.33
3.2 The results of experimental design and analysis
3.2.1 Analysis results without conversion
The sulfur capacity was inputted into the XLISP statistics, to run the Yates Algorithm [10], from
which the estimates of effect were obtained, as shown in the Table 4. Figure 2 is the normal quantile
plots, which is a way to see if a data set is plausible and if it is a sample from a normally distributed
population or procedure. From Figure 2, a number of dramatic changes points were found. A possible
4
reason is that the difference of each factor unit scale or the difference between high and low level is too
big.
As per the guideline of analysis, each experiment is performed once; hence, significant effects are
difficult to identify unless they are very large. The standard method of judging the magnitude of an effect
is the normal probability plot. This plot is however not invariant of the labels used for specifying the high
(+) and low (-) levels of each factor. An invariant way of obtaining such a plot is via the Loh function to
obtain Figure 3, which also superimposes a pair of boundary lines on the plot. Effects that fall outside the
boundaries are considered significant [11]. The model running results show that an effect is possibly
significant if its absolute value is greater than 0.463. Five possible significant effects were obtained,
which are temperature, H2, temperature&H2, CO2, and temperature&CO2.
It is supposed that only the three main effects and the two interactions are significant. If the
hypothesis is true, residuals should match an independent and identically distributed, normal distribution
(IIDN). The adequacy of this model can be checked with a plot of the residuals as follows: the residuals,
which are the differences between the respective predicted values (obtained from the model) and the
observed values, can be obtained. The estimated values are first obtained by using the function y-hat
with a list of the estimated effects (including the grand mean), while non-significant effects are replaced
by zeros. From quantile-plot residuals (as Figure 4), the residuals are shown to fall on a straight line,
which can be determined as the normal distribution. It shows that the observed value is from the
theoretical value. Figure 5 shows the relationship between fit-values and residuals. From Figure 5, the
maximum and minimum residuals are shown as horizontal lines, symmetrical about the mean. This
shows that the data set has constant variance, meaning the residuals are identical. The mathematical
relationship between the sulfur capacity and each significant factor is obtained by running the model.
The mathematical relation formula is as follows.
E
 AE 
 A
C
 AC 
y      X1    X 3  
 X1 X 3    X 5  
 X1 X 5
2
2
 2 
2
 2 
 3.26  1.78 X 1  0.39 X 3  0.42 X 1 X 3  0.78 X 5  0.72 X 1 X 5
Table 4. The effect of various factors on the sulfur capacity
Run
Effect
Run
Effect
1
η
3.261
17
E
-1.569
2
A
3.562
18
AE
-1.450
3
B
0.463
19
BE
0.405
4
AB
0.159
20
ABE
0.279
21
CE
0.125
5
C
-0.772
22
ACE
0.304
6
AC
-0.836
7
BC
-0.021
23
BCE
-0.061
8
ABC
-0.315
24
ABCE
0.047
9
D
-0.28
25
DE
-0.147
10
AD
0.144
26
ADE
-0.086
11
BD
-0.121
27
BDE
-0.026
12
ABD
0.038
28
ABDE
0.050
13
CD
0.114
29
CDE
-0.006
14
ACD
0.057
30
ACDE
0.070
15
BCD
-0.033
31
BCDE
0.030
16 ABCD
-0.009
32 ABCDE
0.026
5
(6)
F
Figure 2. Noormal quan
ntile plot
Figure 3. Loh effect plot
F
Figure 4. R
Residuals distrribution
Figure 5. Y
Y-hat v.s Resiiduals
3.2.2 Anallysis resultss with conveersion
The experimentaal results w
were tried thhrough appropriate connversion prrocessing too seek for tthe
optimal laambda valuee (conversion factor). Figure
F
6 show
ws that wheen the lambdda value is aapproximateely
-1 to 0.25,, the residuaal sum of squuares (RSS)) is at its minnimum. Thaat minimized residual suum of squarres
(RSS) is called
c
the m
maximum likkelihood esttimator (ML
LE). Finallyy, lambda vaalue 0.25 w
was chosen ffor
convertingg sulfur capacity. The rreason to chhoose lambdda value 0.225 is that thee results of independennce
and normaal distributioon are betterr, and the innteraction efffects are reduced.
In thee XLISP staatistics, the transformedd sulfur cappacity of sorrbents was inputted to run the Yattes
Algorithm
m, which ggave the esttimates of eeffect, as shhown in Taable 5. The transformation of sulffur
capacities is via Loh ffunction to ggain Figure 7. Effects that
t fall outsside of the bboundaries are
a considerred
significantt. The resullts show thaat an effect is possiblyy significantt if its absollute value is greater thhan
0.0632. N
Number of possibly signnificant effeects is threee. Main signnificant effeects are tempperature, CO
O2
concentrattion, and thee interactionn of temperaature and CO
O2 concentrration.
From
m quantile-pllot residualss (as Figuree 8), the resiiduals are shhown to falll on a straigght line, whiich
can be dettermined ass the normall distributioon. It showss that the obbserved valuue is from tthe theoreticcal
value. Figgure 9 shows the relatioonship betw
ween fit-valuues and residduals. From
m Figure 9, tthe maximuum
and minim
mum residuaals are show
wn as horizontal lines, syymmetrical about the m
mean. This sshows that tthe
data set haas constant variance, meaning
m
the residuals aare identicall. The symm
metry of Figgure9 is bettter
than that in Figure 5. The mathem
matical relattionship bettween sulfurr capacity annd each signnificant facttor
is obtainedd by runningg the modell. The matheematical relation formuula is as folloows.
 AE 
 A
E
y      X1    X 5  
 X1 X 5
 2 
2
2
 1.2882  0.1191X1  0.006645X 5  0.05223X 1 X 5
6
((7)
Run
n
1
2
3
4
5
6
7
8
9
100
111
122
133
144
155
166
Figure 6. L
Lambda ploot
Figure 7. Loh effect plot
(aafter λ=0.255 transform
mation)
Figu
ure 8. Resid
duals distrib
bution
(aftter λ=0.25 ttransformaation)
F
Figure 9. Y--hat v.s Ressiduals
(aafter λ=0.255 transform
mation)
Table 5. The sign
nificant facttor in Modeel (after λ=00.25 transfformation)
Effect
Effect
R
Run
η
1.288
1
17
E
-0.129
A
0.382
1
18
A
AE
-0.105
BE
B
0.056
0.047
1
19
AB
AB
0.003
BE
0.020
2
20
CE
C
-0.055
-0.015
2
21
AC
AC
-0.063
CE
0.021
2
22
BC
BC
0.018
CE
-0.008
2
23
ABCE
ABC
-0.038
0.009
2
24
D
-0.046
D
DE
-0.016
2
25
AD
AD
0.032
DE
-0.004
2
26
BD
BD
-0.015
DE
-0.003
2
27
ABD
ABDE
0.011
0.007
2
28
CD
CD
0.013
DE
-0.006
2
29
ACD
ACDE
4.727E-4
0.012
3
30
BCD
BCDE
-0.002
8.365E-44
3
31
ABCD
ABC
D
-0.002
CDE
0.001
3
32
7
4. Conclusions
In this study, the 25 factorial experiment designs method was utilized to find the significant impact
factors for 20 wt%Fe2O3 sorbent capture of H2S at high temperature. The major conclusions from this
study can be summarized as follows:
(1)The 32 sets of experimental results are observed by changing a single factor to find its effect on high
temperature desulfurization reaction. It is found that higher temperature, higher CO concentration and
lower H2/CO2 concentration were conducive to the desulfurization reaction. The experimental results
could be explained by water-gas shift reaction.
(2) It is originally thought that the concentration of CO had a significant effect, but no impact was found
by XLISP analysis, presumably because additional CO was consumed by the water-gas shift reaction.
However, in the 16-32 set of experiments, H2 and CO2 exist in the gas compositions and cause the
reverse of the water-shift reaction, resulting in a CO effect that is not readily apparent.
(3)The results of the non-transformation model show that the main effects are temperature, CO2
concentration, and H2 concentration. The interaction effects are temperature & CO2 concentration and
temperature & H2 concentration.
(4) From the results of using λ=0.25 to transform data, it was found that it decreased the number of
interaction effects. The obtained main effects are temperature (positive effect) and CO2 concentration
(negative effect), while the interaction effect is temperature & CO2 concentration.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
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