siB

amhranhran

nd cf H240

Behbe

ractOVAotheteent of the quadratic model. The results indicated that up to 100% H S conversion

id gasecoverend corlfur ism natular Cl

Fuel Processing Technology 109 (2013) 163171

Contents lists available at SciVerse ScienceDirect

Fuel Processing

e lscy could not exceed 97% [3,4]. A typical tail gas of the Claus unit maycontain 6000 ppm SO2 and 12,000 ppm H2S [4]. Thus, it's necessary toembed a tail gas treatment unit (TGT) at the end of the Claus unit, toremove the residual hydrogen sulde [5]. The typical TGTs are wetprocesses. These processes use liquid to reduce the residual H2S inthe tail gas, but the drawback of these methods is that they arequite expensive [6]. Currently, dry catalytic processes based on selec-tive catalytic oxidation of H2S to elemental sulfur are being developed[7]. Comprimo's Super-Claus process and Mobil's direct-oxidationprocess are two examples of dry processes that use catalyst (-alumina

and determined the optimal conditions for maximizing butylgalactosideconcentration in an enzymatic synthesis. Manohar et al. [20] usedPlackettBurman, BoxBehnken and central composite designs andresponse surface method in lipase catalyzed esterication reactions.Guo and coworkers [13] optimized the culture condition for Hydrogenproduction by Ethanoligenens harbinense B49 by RSM. Can [15] employeda 23 full-factorial central composite design and response surface meth-odology to optimize the removal efciency of Ni(II) from cone biomassof Pinus sylvestris. Some authors [12] presented a quadratic model forPb(II) removal from aqueous solution by Pistacia vera using responsesupported iron oxide/chromium oxide anrespectively) to catalyze the oxidation oMany of researchers have investigated the operthe maximum efciency of H2S removal from g

Corresponding author.E-mail address: TOWFIGHI@modares.ac.ir (J.T. Darya

0378-3820/$ see front matter 2012 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.fuproc.2012.10.013aus process to produceics limitations, the used, the maximum efcien-

technique.RSM applications have been performed for optimization of different

process. Ismail et al. [19] developed an empirical model by using RSM

elemental sulfur. According to thermodynamcatalysts and the number of reactors in plants1. Introduction

Hydrogen sulde, existing in the aceries or natural gas plants, must be reatmosphere due to the high toxicity adays conversion of H2S to elemental suod to eliminate the sulfur pollutant frois conventionally treated by the pop 2012 Elsevier B.V. All rights reserved.

s generated by oil ren-d before releasing to therosive effect [1]. Nowa-the most effective meth-ural gas [2]. This process

Optimization is a method used in diverse processes with the aim ofobtaining the highest yields in the shortest period of time with thelowest cost [12].

Response surfacemethodology (RMS) is a famous and up-to-date ap-proach for modeling and optimizing experimental observations [1318].Optimizing the surface response and determining the relationship be-tween input variables and response of tests are considered in RSM2

was obtained at the optimum conditions.BoxBehnken experimental design adjusted-R of 0.9447, absoluplied the satisfactory adjustmResponse surface modeling of H2S convercatalysts based on SiC nanoparticles using

Momene Moradi a, Jafar Towghi Daryan a,, Ali Moha Department of Chemical Engineering, Tarbiat Modares University, P.O. Box 14115-143, Teb Gas Research Division, Research Institute of Petroleum Industry, P.O. Box 14665-1998, Te

a b s t r a c ta r t i c l e i n f o

Article history:Received 13 March 2012Received in revised form 26 August 2012Accepted 26 October 2012Available online 30 November 2012

Keywords:H2S catalytic oxidationSiC nanoparticles

Catalysts of sodium silicate aused in catalytic oxidation ospace velocity (from 2000 toH2S were studied by a Boxoped to describe relationshiptors and their quadratic intethe analysis of variance (ANconversion compared with

2

j ourna l homepage: www.d TiO2-based catalystf H2S to sulfur [3,6,8].ating conditions to obtainaseous streams [911].

n).

rights reserved.on by catalytic oxidation reaction overoxBehnken experimental designadalizadeh b

, Iran, Iran

admiumoxide supported on silicon carbide nanopowderswere synthesized andS to elemental sulfur. The effect of temperature (from 150 to 250 C); gas hour00 h1) and sodium:cadmium weight ratio (from 1 to 5), on the conversion ofnken experimental design method. A quadratic regression equation was devel-tween the operating conditions and the response. The signicance of main fac-ions on the conversion of H2S in catalytic oxidation were examined by means of). The results showed that temperature had the most signicant effect on H2Sr two variables. F value of 29.46, coefcient of determination (R2) of 0.9779,average deviation (AAD) of 0.29% and, coefcient of variation (CV) of 0.59%, im-

Technology

ev ie r .com/ locate / fuprocsurface modeling and BoxBehnken experimental design. elik [21]synthesized phenylacetaldehyde by oxidation of 2-phenylethanolthrough biotransformation. The effects of biotransformation time, initialsubstrate concentration, agitation speed and fed-batch number on thephenylacetaldehyde production was investigated and optimized bythe response surface methodology. Keyvanloo et al. [22] investigatedthe effects of temperature, steam-to-naphtha ratio, residence time andthe interactions between them in naphtha steam cracking process.

ratio and GHSV on conversion of H2S from gas stream in a catalytic ox-idation reaction. Also the optimum operating conditions are deter-

perature to 600 C with a slope of 5 C/min in order to transform thesalt precursors into their corresponding oxide and silicate. The prepared

164 M. Moradi et al. / Fuel Processing Technology 109 (2013) 163171calcinated nanocatalysts were pelletized and meshed between 20 and30 m, then loaded in the reactor to be tested according to determinedexperiments by DoE (Design of Experiments).

2.2. Characterization techniques

Different characterization methods involving XRD, ASAP, TEM andSEM were used to identify prepared catalysts. The structural characteri-zation studies of the solids were carried out by X-ray diffraction.

XRD measurements were carried out with a Philips diffractometer,(CoK Radiation, =1.78896, step size=0.02/s at 40 kV and20 mA). The specic surface area (SBET) was calculated by theBrunauerEmmettTeller (BET) method. The surface area and pore sizemeasurements were performed on a micrometrics ASAP 2010 instru-ment using N2 adsorption at 77 K. Before nitrogen physical adsorption,the samples were degassed at 300 C for 5 h. Scanning electron micros-copy (SEM) images were obtained with a Philips, XL30 device. Gold wasused as the conductive material for sample coating. TEM images wereobtained with a Philips, CM 200 device. Gold was used as the conductivematerial for sample coating. The presence and location of the differentspecies in the samples were conrmed by EDS with an EDAM3 X-raymined. A quadratic model is proposed based on the BoxBehnkendesign (BBD) including the effect of the process variables over the H2Scatalytic oxidation conversion.

2. Materials and methods

2.1. Catalyst preparation

Silicon carbide nanopowders (>99% purity) with 90 m2/g surfacearea and particle size of 50 nm, used as a catalyst support, were suppliedfrom Neutrino Company. Catalysts with 10 wt.% of sodiumcadmiumwere provided (different mass ratio of sodium/cadmium in each cata-lyst). The catalysts of Na/Cd (0:1, 1:1, 3:1, 5:1, and1:0)were synthesized.Catalysts were prepared by impregnation method. Sodium methylate(CH3ONa) was used to load sodium silicate while cadmium acetate((CH3COO)2Cd2H2O) was used to load cadmium oxide particles onsilicon carbide nanopowders support. To prepare the catalysts, the pre-nominate salts were dissolved in distilled water and the solution waspoured on SiC nanopowders. The wet solid was dried by rotary evapora-tor at 70 C to penetrate the salts in pores and then was dried in oven at120 C for 2 h. The mixture was then calcinated in air from room tem-They provided a model and nd the optimized operating condition forthe study. Habibi and coworkers [9] studied onH2S adsorption from syn-thesized natural gas. They selected the sorbent morphology, GHSV andH2S feed concentration to be used as effective parameters on adsorptionof hydrogen sulde by central composite design and a mathematicalmodel was offered to obtain the H2S concentration in the range of oper-ating conditions of variables [9]. Catalytic oxidation of isopropylmercap-tan on tungsten oxide nanoparticle catalysts supported on multiwallcarbon nanotubes (MWNTs) has been investigated by Farahzadi et al.[23]. They optimized the operating conditions, such as temperature, cat-alyst loading, gas hour space velocity and oxygen-to-IPMmolar ratio, bymeans of RSMmethod. The quadratic and cubic interactions were inves-tigated by use of central composite design (CCD). Temperature and nanotungsten oxide loading revealed the most effects on mercaptan contentof outlet stream.

This study is devoted to use response surface methodology to iden-tify the inuence of parameters such as temperature, sodiumcadmiumanalyzer (diameter of the probe: ~3 nm).2.3. H2S catalytic oxidation set-up

Catalytic tests were carried out isothermally under atmosphericpressure. The reaction between hydrogen sulde and oxygen was car-ried out in a tubular reactor. The apparatus used for the H2S catalyticoxidation has been described in previous publications [9,24,25].

The reactantmixture containedH2S (8000 ppmv), O2 (16,000 ppmv),steam (20 vol.%) and balance helium, which are typically the industrialworking concentrations [4,26]. The ow rates of gas streams were con-trolled by a mass ow controller (Bronkhorst mass ow meters linkedto a JUMO (dTRON 304) electronic control units).Water vapor was intro-duced to the reactant streams using a steam evaporator.

The gaseous feed stream was 130150 cm3/min and the amount ofcatalyst was assigned by desired space velocity. The tests were donefor 360 min.

The efuent gas from the reactor was passed through a trapcontaining a concentrated NaOH solution and vented out to a hood. Forsampling, the efuent gas was conducted to a 20%-KOH solution andthen analyzed by a potential metric titration instrument (METTLER DL40GP (memotitrator), with an accuracy of 1% of 1 mL) equippedwith an AgAg2S electrode (DM 141-SC). By using this method the con-centration of H2S in outlet and feed streams were achieved.

2.4. BoxBehnken experimental design and optimization by RSM

The optimum conditions for maximizing the conversion of H2S andinvestigation of the effect of quadratic interactions as well as maineffects in H2S catalytic oxidation were determined by means of a threefactor, BoxBehnken design combined with response surface modelingand quadratic programming. This experimental methodology usesregression design to model the response as a mathematical function offactors with unbiased and minimum variance. Thus the graphical out-look of the mathematical model describes the shape of the responsesurface, being investigated [27]. For this study, the effects of three pro-cess variables of A: reaction temperature (C), B: space velocity (h1)and C: sodiumcadmium weight ratio, on the conversion of H2S wasinvestigated.

The parameters should be normalized before analyzing the regres-sion. The natural variables are coded as +1, 1 and 0 for high, lowand central point, respectively. So the units of the parameters are notimportant. The actual variables (Xi) are coded by linear transformationas follow:

xi Xi

XhighXlow 2

XhighXlow2

1

where xi is the dimensionless coded value of ith factor, Xi is the uncodedvalue of the ith independent variable (natural factor), Xhigh and Xlow arethe uncoded factor value at high and low level, respectively. The threeexamined levels and experimental ranges of each independent variableare given in Table 1.

The behavior of the system is explained by the following quadraticpolynomial equation as a function of independent variables involvingtheir quadratic interactions and squared terms.

y 0 X3

i1ixi

X3

i1iixi

2 X3

i1

X3

i

with cadmium and sodium oxides, the BET surface area has decreasedin the modied SiC catalyst.

3.2. Statistical analysis of experiments

The actual design of experiments, the conversion and concentra-tion of outlet H2S are tabulated, as given in Table 3. The conversionof H2S to elemental sulfur was calculated through Eq. (4):

Conversion % H2S in H2S outH2S in

100 4

where [H2S]in and [H2S]out are the concentration of H2S in inlet andout let streams in ppmv, respectively.

The results were analyzed using the analysis of variance (ANOVA),a regression model, coefcient of determination (R2), adjustedR-square, coefcient of variation (CV), absolute average deviation(AAD), statisticaldiagnostic and response plots.

The analysis of variance (ANOVA) test is a robust and usual statisti-cal method in the different elds. The ANOVA provides a statistical pro-cedure that determines whether the means of several groups are equalor not. The Fisher's variance ratio, F-value, is used to test the signicance

cadmium oxide supported on SiC.

165M. Moradi et al. / Fuel Processing Technology 109 (2013) 163171In statistics, BoxBehnken designs (BBD) are typical experimentaldesign for response surface methodology. BBD is a class of rotatable ornearly rotatable second-order designs based on three-level incom-plete factorial designs. Each design can be thought of as a combina-tion of a two-level (full or fractional) factorial design with anincomplete block design. In each block, a certain number of factorsare put through all combinations for the factorial design, while theother factors are kept at the central values. The BoxBehnken designis a good design for this methodology because:

1. It permits estimation of the parameters of the quadratic model.2. There are no runs where all factors are at either the +1 or 1

levels.3. They are all spherical designs and require factors to be run at only

three levels.4. Use of blocks.

The number of experiments (N) required for the development ofBBD is dened as follow:

N 2k k1 Co 3

where k is number of factors and Co is the number of central points.Based on Eq. (3), with 3 main factors and 4 times replication in

center point to reduce the magnitude of error, (k=3 and Co=4),the runs will be limited to 16. Four tests for catalysts with only sodi-um silicate supported on SiC, cadmium oxide supported on SiC, pureSiC and the one without catalyst have been done. By applying thismethod 20 runs should be performed.

3. Results and discussion

3.1. Nano catalysts characterization

The XRD patterns of catalysts andmetal phase located on silicon car-bide nanoparticles before the H2S oxidation is exhibited in Fig. 1. As isevident from these patterns (Fig. 1a), the crystalline phases detectedin the fresh support could be indexed to the -SiC structure and havepeaks at 2=85.88, 71.0, 48.5 and 41.6. Exposing the catalysts toair stream, for calcination, as shown in (Fig. 1b and d) new peaksappeared corresponding to sodium silicate and cadmium oxide, respec-tively. Catalysts contain sodium silicate (Fig. 1b) have peaks at 2=54.58, 51.92, 48.4, 39.1, 35.3, 31.3, and 20.86. The XRD of catalystscontain cadmium oxide is shown in Fig. 1d, and has peaks at 2=78.3,65.2, 44.4, and 38.5. Catalyst with Na:Ca=1:1 is displayed in Fig. 1c.In catalysts of sodiumoxide and Na:Cd=1:1 there is a peak at the angleof 25.2 that is attributed to silica. Considering that, Na2O is one of the

Table 1Factors and levels for the BBD design.

Factors Level

1 0 +1

A Reaction temperature 150 200 250B Space velocity 2000 3000 4000C Na/Cd 1 3 5fewmaterials that reactwith SiC, so during calcination, reaction of sodi-um methylate with silicon carbide produces SiO2 and sodium silicate[28].

SEM and TEM images of SiC nanoparticles are presented in Fig. 2aand b. The SEM image shows the uniform surface morphology of SiCnanoparticles (Fig. 2a). The SiC nano particles are exactly clear in Fig. 2b.The diameter of SiC nanoparticles are in the range of 4090 nm (Fig. 2b).

The BET results are shown in Table 2. Surface areas of catalysts withtotal loading of 10 wt.% of sodium and cadmium, decreased from90 m2/g belongs to SiC nanopowders. Because SiC pores are lled upFig. 1. XRD patterns of the nanocatalysts before H2S oxidation (a) silicon carbide nano-powder, (b) sodium silicate supported on SiC, (c) catalyst with Na:Cd=1:1, and (d)of the model, individual variables and their interactions [22,29]. Meansquare (MSS) is the sum of squares divided by the degrees of freedom,for each source. The F-value is dened as:MSS of variableMSS of residual, and shows the rel-ative contribution of the sample variance to the residual variance. If theratio deviatesmore andmore from1, the samples are not from the samepopulation, with more condence.

The analysis of variance (ANOVA) based on data from experimentsare shown in Table 4.

We can compare the F-value of calculations with F-value obtainedfrom F-distribution table with and e degrees of freedom and the

3.3. Statistical evaluation

f the silicon carbide nanopowder.

166 M. Moradi et al. / Fuel Processing Technology 109 (2013) 163171level of (signicance level) to discern the signicance and adequacyof the model [17]. An effect is statistically signicant, if the calculatedF-value for the effect is greater than the F-value extracted from thetable in desirable probability level.

Based on calculated p-value, all three factors, temperature, GHSVand Na/Cd and their interaction effects and also the temperaturesquared term were found to be signicant (Table 4).

The regression equation obtained after variance analysis gives thelevel of H2S conversion. It includes a linear relationship between allthe main effects and response, a quadratic relationship with temper-ature and, the product of temperature and GHSV, temperature andNa:Cd, and GHSV and Na:Cd. The nal quadratic polynomial equa-tions in terms of coded and natural variables are presented as follows:

ConversionH2S 98:77 2:63A0:95B 0:90C 0:79AB0:98AC0:74BC1:46A20:53B2 0:39C2 5

ConversionH2S 69:52656 0:26763T2:04687 103GHSV

5

Fig. 2. (a) SEM and (b) TEM o0:70078 Na : Cd 1:575 10 T GHSV9:75103T Na : Cd 3:70313 104GHSV Na : Cd 5:825 104T25:28125 107GHSV20:097656 Na : Cd 2:

6

As seen in Table 4 the Fisher's F-value and a very low probabilityvalue of the regression model are found to be 29.46 and 0.0003, respec-tively. This implies that the terms in the model have a signicant effecton the response. The tabular F-value with model=9 and error=6 asdegrees of freedom of model and error, respectively, at the signicancelevel of 0.05 (F0.05,(9,6)=4.0990) is much lower than the calculatedF-value F0:05; 9;6 MSSmodelMSSerror 29:46

, represents that most of the varia-

tion in the response can be explained by the regression equation.

Table 2The BET results of catalysts.

Samples Surface area (m2/g)

SiC nanopowder 90Catalyst with 10 wt.% Na 86.4Na:Cd=5:1 86.5Na:Cd=3:1 86.8Na:Cd=1:1 87.0Catalyst with 10 wt.% Cd 87.1The coefcient of determination, R2, indicates the overall predic-tive capability of the model. It shows how well a regression approxi-mates experimental data and can be dened as:

R2 SSModelSSTotal

1 SSerrorSSTotal

: 7

The R2 value of themodel has been determined as 0.9779. Therefore,it can be assumed that 97.79% of the total variations in the responsecould be explained by the model. However, this large amount of R2

does not necessarily imply that the regression model is a suitable one.Adjusted R-square is dened to correct the R2. In this case,adjusted-R2 value is 0.9447. As it can be seen adj-R2 is very close toR2, emphasizes the high signicance of the model. Another way to de-scribe the variation of a model is calculating the coefcient of variation(CV).Table 3The design matrix and experimental data of the out let H2S concentration and H2S con-version from the BBD design.

Run Independentvariables

Response

A B C Out let H2S concentration (ppm) H2S conversion (%)

1 200 3000 3 110 98.622 200 2000 5 12 99.853 150 2000 3 380 95.254 250 2000 3 20 99.755 150 3000 5 201 97.456 250 3000 1 10 99.877 200 4000 1 325 95.948 200 2000 1 15 99.819 200 3000 3 95 98.8110 200 4000 5 85 98.9411 150 3000 1 522 93.4712 250 4000 3 8 99.9013 200 3000 3 102 98.7214 150 4000 3 620 92.2515 250 3000 5 1 99.9916 200 3000 3 86 98.9217 200 3000 Na 1 99.9918 200 3000 Cd 92 98.9519 200 3000 SiC 2006 74.2020 200 3000 2335 70.81

The low value of the coefcient of variation (0.59%) in this model probability of residuals plot (Fig. 4) is straight that implies a satisfac-tory normal distribution and the independence of the residuals.

The optimal level of key factors and their interaction effects on H2S

Table 4Analysis of variance (ANOVA) of the response surface model for the prediction of conversion of H2S in catalytic oxidation.

Source Statistics coefcient Sum of square Mean square F-value F-value from table (p=0.005) P-value (prob>F) Remark

Model 87.78 9 9.75 29.46 4.099 0.0003 SignicantAT 2.63 55.39 1 55.39 167.31 5.987 b0.0001 SignicantB-GHSV 0.95 7.29 1 7.29 22.03 5.987 0.0033 SignicantCNa:Cd 0.90 6.41 1 6.41 19.37 5.987 0.0046 SignicantAB 0.79 2.48 1 2.48 7.49 5.987 0.0339 SignicantAC 0.98 3.80 1 3.80 11.49 5.987 0.0147 SignicantBC 0.74 2.19 1 2.19 6.63 5.987 0.0421 SignicantA2 1.46 8.48 1 8.48 25.62 5.987 0.0023 SignicantB2 0.53 1.12 1 1.12 3.37 5.987 0.1161 C2 0.39 0.61 1 0.61 1.84 5.987 0.2234 Residual 1.99 6 0.33Total 89.76 15

indicates the degree of freedom.P-valuesb0.05 were considered to be signicant.

167M. Moradi et al. / Fuel Processing Technology 109 (2013) 163171shows a very high degree of accuracy and condence of tests.Table 5 shows the observed and predicted response values with

residuals and percent error of responses for the runs. This is also de-scribed in Fig. 3. It shows the experimental results of H2S conversionversus model results. The point collections around the diagonal lineillustrate that deviation between the experimental and predictedvalues was less and good t of the model is obtained. Moreover calcu-lating the absolute average deviation (AAD) is a direct method forexplaining the deviations. The AAD is dened as:

AAD

Xk

i1

yi;realyi;pre

yi;real

0@

1A

k 100

0BBBBBB@

1CCCCCCA

13

where yi,pre is the predicted and yi,real is the real result, respectively,and k is the number of experimental tests. As mentioned above, thehigh values of R2 and adj-R2 indicated that the quadratic equationcan predict the response under the experimental data domain. Calcu-lating the R2 and AAD values together should be better to check theaccuracy of the model. The value of AAD in this case is 0.29%. So thisamount of R2 and AAD shows that the model equation denes thetrue behavior of the system. The result shows that this regressionmodel can be used for interpolation in the experimental domain.

The normal probability plot (NPP) determines the normal distri-bution and the homogenization of the data. In this case, the normal

Table 5

Observed responses and predicted values with residuals.

Run Temp GHSV Na:Cd

Observed(Cppmo)

Predicted(Cppmp)

Residuals(CppmoCppmp)

Error (%)

1 200 3000 3 98.62 98.77289 0.15289 0.155032 200 2000 5 99.85 99.74475 0.10525 0.1054083 150 2000 3 95.25 95.89919 0.64919 0.681574 250 2000 3 99.75 99.58719 0.162808 0.1632165 150 3000 5 97.45 96.94607 0.50393 0.5171166 250 3000 1 99.87 100.4185 0.54845 0.549167 200 4000 1 95.94 96.04477 0.10477 0.10928 200 2000 1 99.81 99.43538 0.374618 0.3753319 200 3000 3 98.81 98.77289 0.037114 0.03756110 200 4000 5 98.94 99.31664 0.37664 0.3806811 150 3000 1 93.47 93.20545 0.26455 0.28303212 250 4000 3 99.90 99.25283 0.64717 0.64781813 200 3000 3 98.72 98.77289 0.05289 0.0535714 150 4000 3 92.25 92.41483 0.16483 0.1786815 250 3000 5 99.99 100.2591 0.26907 0.269116 200 3000 3 98.92 98.77289 0.147114 0.14872conversion were further investigated by the BoxBehnken design ofRSM.

3.4. Effects of themodel components and their interactions onH2S conversion

From Fig. 5, H2S conversion was increased by increasing the reac-tion temperature and NaCd ratio and decreased by increasing spacevelocity. According to Table 4, temperature is the most effective indi-vidual factor in H2S conversion (F=167.31, pb0.0001). The averageconversion of 98.77% is observed at temperature between 150 and250 C (GHSV=3000 h1, Na:Cd=3). But in non-catalytic thermaltest (T=200 C), the maximum conversion reached 70.81%(Table 3). It implies that one catalytic step without initial thermalstep is able to signicantly enhance the conversion of H2S. Besidesthe thermal reaction at temperatures above 200 C leads to produceSO2 in efuent gas. The conversion of 74.20% is observed in SiC test.The test was done only with the support, without adding salts. Thisconversion is close to the conversion of reaction with no catalyst(70.81%), so the support is inertness in all reactions.

The conversion in the reaction of sodium silicate catalyst reachedto 99.99% and it reached to 98.95% in cadmium oxide catalyst. The rel-ative basic strength of sodium silicate is more than cadmium oxideand reducing in H2S concentration is related to the basic strength.

Fig. 6 shows interaction behavior of each two variables. As seen inthis gure, the effect of GHSV was negligible when temperature wasFig. 3. The experimental results of H2S conversion versus model results.

Eq. (6) is used to draw the curves.The dependence of the degree of enhancement of H2S conversion

Analysis of the contour plots revealed that signicant interactionsoccurred between temperature and Na:Cd ratio, as is shown by thehyperbolic nature of the contours in Fig. 7c. According to the F andp-value in Table 4 interaction between temperature and Na:Cd (F=11.49 and p=0.0147) has the greatest interaction inuence in chang-ing the concentration of H2S. In this case, there is a stationary point(like a saddle point) that is neither a maximum nor a minimumpoint. In constant Na:Cd, rising temperature increases H2S conversionto its highest level, then conversion will decrease again. This reduc-tion in conversion occurs at high temperatures which backward reac-tions can be performed. In the range of 2.53.5 of Na:Cd there is noarea that H2S conversion reaches 100%. At temperatures below225 C, increasing the amount of Na to Cd has a positive effect on in-creasing the H2S conversion. This means that sodium silicate is moreactive than cadmium oxide at low temperatures. At temperatureshigher than 225 C, rising Na:Cd ratio decreased the conversion toits minimum level then, it increased again. In the range of 12.5 and3.55 of Na:Cd, H2S conversion reaches nearly 100%, but in 3.5bNa:Cd there is wider area of full conversion.

168 M. Moradi et al. / Fuel Processing Technology 109 (2013) 163171on the mutual interaction between T and GHSV can be bestinterpreted from the response surface contour diagram (Fig. 7a),which indicates that H2S conversion is inversely related to GHSVand directly related to temperature. The residence time increases bydecrease of GHSV and, the rate of reaction accelerates by increase oftemperature so, hydrogen sulde converting can be enhanced. Attemperatures higher than 225 C and low GHSV, increasing in tem-set at the high level. A sharp decrease in H2S conversion, from 95.9%to 92.4% was found for GHSV as temperature was kept at the lowlevel. This phenomenon happens because of temperature and GHSVinteraction. Similarly, these synergistic effects on the H2S conversioncan be seen for the T and (NaCd) and GHSV and (NaCd) interac-tions in Fig. 6.

3.5. Graphical description of the model equation and determination ofoptimal operating conditions

Response surface plots and contour plots are useful for the modelequation image and perceiving the nature of response surface. Theyare presented in Fig. 7af, which depicts the interaction of each twovariables by keeping the other at its central level for H2S conversion.

Fig. 4. The normal probability plot of the raw residuals.perature reduces the H2S conversion. That occurs, because with lon-ger residence of reactants and products in reactor at hightemperatures backward Claus reaction accelerates. The contour plotdisplays elliptical lines. The maximum conversion of H2S occurs attemperature between 225 and 250 C and GHSV between 2500 and3000 h1 at Na/Cd: 3 in the area conned in the smallest ellipse.This interaction implies that maximum H2S conversion is reached toabout 100%.

Fig. 5. Main effects of reaction temperature (A), GHSV (B), and Na:Cd (C) on the con-version of H2S.As it is shown in Fig. 7e, GHSV and Na:Cd ratio have the least sig-nicant interaction effect (F=6.63, P=0.0421). Obviously, in GHSVrange of 20003000 h1 and Na:Cd ratio of 5 (maximum) the mini-mum concentration of H2S occurs. Basic strength of sodium silicateis greater than cadmium oxide.

The aim of this work was to maximize the H2S conversion in cata-lytic oxidation. The optimal values of key factors (temperature, GHSVand Na:Cd) were obtained by solving the regression in Eq. (6). As itcan be seen in Fig. 7af, there are wide ranges of optimum conditionsthat H2S conversion reaches to maximum level. Because of strong in-teractions between experimental parameters we could not pick out asingle point for maximum conversion. There are many combinationsof T, GHSV and Na:Cd that could give the maximum conversion ofH2S. In this work we have a plane of 100% conversion of H2S intemperatureGHSVNa:Cd coordinate. The point of T=250 C,GHSV=3000 h1 and Na:Cd=5 in experimental design is one ofthe points with 100% conversion. The coded values obtained bysubstituting the respective values of Xi in Eq. (5) are: x T250C

1,x GHSV3000 h1 0, and x(Na:Cd=5)=1. The predicted H2S conver-sion, in mentioned coordinate, is equal to 100, too. This point is spec-ied in Fig. 8ac. Besides, two more points between optimizationintervals have been selected to compare the result of model with ex-perimental ones. The rst test was carried out in operating conditionof T=225 C, GHSV=3000 h1 and Na:Cd=4. The conversion ofthis run was 99.95%. The second test was performed at T=200 C,GHSV=3000 h1 and Na:Cd=5 which leads to the conversion of99.78%. The prediction of model for both of these points is 100% and

Fig. 6. Interaction effects of AB, AC and BC on the conversion of H2S: where (+)

and () indicate the high and low levels of these factors, respectively.

169M. Moradi et al. / Fuel Processing Technology 109 (2013) 163171as it is obvious the results are very close to the calculated one. By en-tering the results in AAD calculations it still remains very low (0.28%).

4. Conclusion

For formulate and obtain the optimal condition of H2S conversionin a catalytic oxidation reaction, response surface methodology and

Fig. 7. Contour and response surface plots for H2S conversion as a function of tempeBoxBehnken experimental design were adopted Based on analysisof experimental data all three parameters of temperature, GHSV andsodium: cadmium weight ratio were effective in H2S conversion,meanwhile, temperature had the most signicant effect of indepen-dent variables. Main effect of each parameter was more signicantthan respective quadratic effect; it implies the direct effect of vari-ables on H2S conversion. TNa:Cd combination was the most effective

rature vs. GHSV (a, b), temperature vs. Na:Cd (c, d) and GHSV vs. Na:Cd (e, f).

170 M. Moradi et al. / Fuel Processing Technology 109 (2013) 163171interaction with a positive effect on conversion. The values of coef-cient of determination (0.9779), adjusted-R2 (0.9447), absolute aver-age deviation (0.29%), coefcient of variation (0.59%) and F value of29.46 illustrate a suitable adaption of experimental data with derivedequation. There were wide ranges of optimum conditions that H2Sconversion reaches to maximum level. There is a plane of 100% con-version of H2S in temperatureGHSVNa:Cd coordinate.

Acknowledgments

Financial support of the Research Institute of Petroleum Industry(RIPI) is appreciated.

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171M. Moradi et al. / Fuel Processing Technology 109 (2013) 163171

Response surface modeling of H2S conversion by catalytic oxidation reaction over catalysts based on SiC nanoparticles using BoxBehnken experimental design1. Introduction2. Materials and methods2.1. Catalyst preparation2.2. Characterization techniques2.3. H2S catalytic oxidation set-up2.4. BoxBehnken experimental design and optimization by RSM

3. Results and discussion3.1. Nano catalysts characterization3.2. Statistical analysis of experiments3.3. Statistical evaluation3.4. Effects of the model components and their interactions on H2S conversion3.5. Graphical description of the model equation and determination of optimal operating conditions

4. ConclusionAcknowledgmentsReferences