Study of effective parameters in the Fischer Tropsch synthesis using monolithic CNT supported cobalt catalysts

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Fuel xxx (2014) xxx–xxx

JFUE 8050 No. of Pages 9, Model 5G

3 May 2014

Contents lists available at ScienceDirect

Fuel

journal homepage: www.elsevier .com/locate / fuel

Study of effective parameters in the Fischer Tropsch synthesisusing monolithic CNT supported cobalt catalysts

http://dx.doi.org/10.1016/j.fuel.2014.04.0560016-2361/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +98 2632804213; fax: +98 2632827542.E-mail address: [email protected] (S. Farzad).

Please cite this article in press as: Farzad S et al. Study of effective parameters in the Fischer Tropsch synthesis using monolithic CNT supportedcatalysts. Fuel (2014), http://dx.doi.org/10.1016/j.fuel.2014.04.056

Somayeh Farzad a,⇑, Alimorad Rashidi b, Ali Haghtalab c, Mohsen Ali Mandegari d

a Department of Chemical Engineering, Environmental University, P.O. Box 31746-118, Karaj, Iranb Nanotechnology Research Center, Research Institute of Petroleum Industry (RIPI), P.O. Box 14665-1998, Tehran, Iranc Department of Chemical Engineering, Tarbiat Modares University, P.O. Box 14115-143, Tehran, Irand Department of Chemical Engineering, Science and Technology, University of Mazandaran, Behshahr, Iran

h i g h l i g h t s

�Monoliths prepared from CNT supported cobalt catalyst have been evaluated for FTS.� Pressure affects the product selectivity more than T, GHSV and cobalt loading.� The CO conversion is highly affected by T in comparison with P, GHSV and Co%.� The effect of parameters interaction on conversion and selectivity is demonstrated.� Pressure augmentation increases CO conversion and heavier hydrocarbon production.

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a r t i c l e i n f o

Article history:Received 28 January 2014Received in revised form 3 April 2014Accepted 17 April 2014Available online xxxx

Keywords:Monolithic catalystFischer Tropsch synthesisCarbon nanotubesTwo factorial design method

4849505152

a b s t r a c t

In this work monolithic catalysts have been prepared using carbon nanotubes as the support material. Inorder to screen the effective factors on CO conversion, Cþ5 selectivity and C1 selectivity, fractional factorialdesign method have been used. Considering temperature, pressure, Gas Hourly Space Velocity (GHSV)and cobalt loading as the four effective parameters, different runs have been designed for experiments.Quadratic models for CO conversion and product selectivity have been developed based on the experi-mental results. Furthermore, the analysis of variance (ANOVA) was implemented as a significant toolin order to evaluate the accuracy of the model. The ANOVA results of the developed model showed thatthey are significant with a 95% confidence limit. The main effective parameters have been declared for COconversion, heavier hydrocarbon and methane selectivity. It is clear that the importance of parametersvary from one response to another. The interaction of parameters has been studied as well. The resultsdemonstrate that temperature is the most effective parameters on CO conversion while its interactionwith pressure is more important than other parameter interactions. Pressure affects Cþ5 and C1 selectivitymore than other parameters whereas interaction of pressure with cobalt loading are the most effectiveinteractions for these two responses.

� 2014 Elsevier Ltd. All rights reserved.

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1. Introduction

The Fischer Tropsch synthesis (FTS) is polymerization-like reac-tion which converts syngas (H2 and CO) to liquid fuels. So manystudies have been carried out considering different aspects of FTSsuch as, reactor type, active metal of catalyst, catalyst supportand structure. Fixed-bed reactors are constrained to catalyst parti-cle size in order to stay below an acceptable pressure drop and astructured catalyst such as monolith may provide the solution of

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such a problem [1]. Since the introduction of monolithic catalystsin the mid-1970s, they have become the standard catalyst in mostenvironmental applications. Evaluating the performance of mono-lithic catalysts for multiphase reaction systems has been the focusof several research groups [2]. The results show that monolithicreactors have some advantages over packed reactors such as lowpressure drop, higher specific surface area, higher productivity,better mass transfer performance, evenly distributed flow and bet-ter heat transfer [3–5]. Generally there are two types of monolithiccatalysts; Washcoated Monolithic Catalyst (WMC) and Bulk Mono-lithic Catalyst (BMC) which are prepared from the bulk catalyst [6].Performance of monolithic catalyst for FTS has been examined by

cobalt

http://dx.doi.org/10.1016/j.fuel.2014.04.056

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different research groups considering the WMC type. Generallycatalyst is coated on the microchannels walls, but in order toincrease the catalyst hold-up, structured supports have been pro-posed [7].

Washcoated monolithic catalysts may be prepared throughcoating the active metal on the monolithic structure or coatingthe prepared catalyst on the surface of monolith [8]. There are afew works which investigated the BMC type catalysts [9]. Of

Fig. 1. The schematic dia

Please cite this article in press as: Farzad S et al. Study of effective parametercatalysts. Fuel (2014), http://dx.doi.org/10.1016/j.fuel.2014.04.056

course, monoliths have some disadvantages such as no intercon-nectivity between the channels and a poor radial conductivity[10], limited control of temperature and lower loading of activemetal in comparison with conventional packed bed reactors [9]which caused limited application of these structured catalysts.On the other hand, different catalyst supports have been investi-gated and the use of catalysts supported on novel carbonnanostructured materials like Carbon Nanotubes (CNTs) and

gram of FTS set-up.

s in the Fischer Tropsch synthesis using monolithic CNT supported cobalt


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Table 1The minimum and maximum value of studied parameters.

Factors Minimum Maximum

T: Temperature (�C) 180 240P: Pressure (bar) 15 35G: GHSV 900 1400W: Cobalt % 15 35

Table 2The experimental design and FTS results.

Run A B C D COconversion

Cþ5selectivity

C1

selectivity

1 180 15 900 15 60.11 73.38 26.352 180 15 1400 35 72.3 77.7 21.173 180 15 900 35 64.10 79.9 19.804 240 35 900 15 69.8 86.2 8.805 180 35 900 35 68.43 82.4 17.506 240 15 1400 15 76.65 61.5 36.607 180 35 1400 35 72.78 73.3 26.348 240 15 900 15 67.5 69.8 35.159 240 15 900 35 74.15 77.9 19.7810 180 15 1400 15 71.3 63.5 36.4011 240 35 900 35 85.9 78.6 20.9012 240 15 1400 35 76.63 78.30 21.3013 180 35 900 15 67.4 90.5 8.7014 180 35 1400 15 71.6 73.3 24.9015 240 35 1400 35 84.37 71.2 28.5016 240 35 1400 15 80.97 83.9 13.20

Fig. 2. The temperature programmed reduction (TPR) profile of the monolithiccatalyst built from the CNT.

Actual CO Conversion

Pred

icte

d C

O C

onve

rsio

n

60.00

66.50

73.00

79.50

86.00

60.11 66.56 73.00 79.45 85.90

Fig. 3. The predicted value of CO conversion via its actual value.

S. Farzad et al. / Fuel xxx (2014) xxx–xxx 3


3 May 2014

carbon spheres has been explored [11]. Carbon nanotubes whichhave been focus of the researches since their discovery in 1991by Tian and He [12] have characteristics favoring the FTS such astheir specific surface area, good electronic conductivity and highsurface porosity [13]. In this work we prepared monolithic struc-ture from carbon nanotubes supported cobalt catalyst. Some

Table 3The statistical significance of three studied models.

Model Sum of square

CO Conversion Model 697.46Residual 28.24

Cþ5 selectivity Model 1035.59Residual 38.18

C1 selectivity Model 1064.25Residual 43.79


researchers investigated the effects of FTS condition such as tem-perature, pressure and Gas Hourly Space Velocity (GHSV) on theproduct distribution and catalyst activity [14–17]. The effects ofactive metal loading have been investigated as well. However tothe best of our knowledge, there are a few reports in the literatureon investigating the interaction of reaction conditions and theireffects on catalyst activity [14]. The purpose of this research wasto establish a relationship for CO conversion, Cþ5 selectivity andmethane selectivity with the parameters influencing the mainresponses of FTS. The central idea was conducting the least numberof experiments, and development of a quadratic model to predictthe conversion and product distribution. Applying the two factorialdesign, not only were the main effects (important parameters)screened but also the interaction between parameters and theirimportance order of magnitude were determined.

2. Experimental

2.1. Monolithic catalyst preparation

Carbon nanotubes were synthesized by the catalytic ChemicalVapor Deposition (CVD) of methane over Co/Mo/Al2O3 nanocata-lyst. To produce CNTs, 300 mg nanocatalyst powder fed into thefixed bed reactor was heated up to 900 �C under a pure hydrogenatmosphere with a flow rate of 200 mL/min. Following reductionof the nanoparticles, the catalyst support was exposed to a gasmixture of methane and hydrogen with the ratio of 4:1 and flowrate of 250 mL/min. During the CVD process methane decomposesinto hydrogen and carbon atoms and carbon nanotubes grow overthe catalyst particles. At the end of the reaction time, the gas flow

DOF Mean square F-value P-value

10 69.75 12.35 0.00635 65.5 – –10 103.56 13.56 0.0055 64.7 – –10 106.42 12.15 0.00655 8.76 – –



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was ceased and the reactor was cooled down to room temperatureunder a hydrogen atmosphere. The CNT supported cobalt catalystswith different cobalt loading were prepared through wetnessimpregnation method using aqueous solutions of cobalt nitrate(Co(NO3)2, 6H2O 99.5%) and ruthenium nitrosyl nitrate(Ru(NO)(NO3)3) diluted in nitric acid. The ruthenium nitrosylnitrate solution is used to load 0.2% Ru as promoter for the FTS.Afterward, the catalyst has been calcined at 350 �C for three hourswith a heating rate of 5 �C/min under argon flow due to the char-acteristics of CNT.

(a)

(c)

(e)

Pressure (bar)

180.00 195.00 210.00 225.00 240.00Temperature (oC)

CO

Con

vers

ion%

60

66.5

73

79.5

86

B-

B+

GHSV

180.00 195.00 210.00 225.00 240.00Temperature (oC)

CO

Con

vers

ion%

60

66.5

73

79.5

86

C-

C+

Cobalt%

15.00 20.00 25.00 30.00 35.00

Pressure (bar)

CO

Con

vers

ion%

60

66.5

73

79.5

86

D-

D+

Fig. 4. The interaction of para


Following the catalyst synthesis, it was efficiently mixed with5% furfuryl alcohol used as a binder. The percentage of binderand also solvent affects the strength of the produced monolith sig-nificantly. Then the catalyst containing sufficient percentage ofbinder and solvent was filled in the mold and the filled mold waspressed by a hydraulic press under controlled pressure.

The prepared monoliths were dried at 50 �C overnight in orderto lose the water content uniformly. Dried monoliths werecalcined with the proceeding procedure under argon flow. The cat-alysts temperature was raised to 80 �C during 30 min and kept at

(b)

(d)

(f)

Cobalt%

180.00 195.00 210.00 225.00 240.00Temperature (oC)

CO

Con

vers

ion%

60

66.5

73

79.5

86

D-D+

GHSV

15.00 20.00 25.00 30.00 35.00Pressure (bar)

CO

Con

vers

ion%

60

66.5

73

79.5

86

C-

C+

Cobalt%

900.00 1025.00 1150.00 1275.00 1400.00

GHSV

CO

Con

vers

ion%

60

66.5

73

79.5

86

D-

D+

meters on CO conversion.



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Actual C5+Selectivity

Pred

icte

d C

5+ S

elec

tivity

60.00

68.00

76.00

84.00

92.00

60.80 68.43 76.05 83.68 91.30

Fig. 5. The predicted value of Cþ selectivity via its actual value.



3 May 2014

this temperature for one hour. Then temperature was raised to120 �C during one hour and kept at this value for one hour.Afterward temperature increased to 250 �C during 45 min andstayed at this value for one hour. The last step was to heat themonoliths up to 700 �C and keep them at this temperature fortwo hours. Then the monolithic catalysts were cooled to roomtemperature.

2.2. Reaction setup and catalyst evaluation

The catalysts were evaluated in terms of their Fischer TropschSynthesis activity (g of HC produced/g Cat./h), CO conversion (per-centage of the converted CO to hydrocarbon products), Cþ5 and CH4

selectivity in a fixed-bed reactor. Selectivity is calculated as theratio of desired product (Cþ5 or CH4) to the total produced hydro-carbon. The schematic diagram of the setup is shown in Fig. 1.As can be seen the mixed gases enter to the top of the fixed bedreactor which is mounted in a programmable vertical furnace.The mixed gases pass the preheating section of the reactor andthen enter the reaction zone which has already been filled withthe catalyst. The main body of the reactor was made of stainlessstill with an inner diameter of 22 mm and a height of 450 mm.Prior to the activity tests, the catalyst reduction procedure wasconducted introducing pure hydrogen at an appropriate flow ratewhich was determined by TPR test. The reactor temperature isincreased from room temperature to 400 �C at a rate of 1 �C/min.At this temperature, after 12 h the activation procedure is com-pleted and the reactor temperature is decreased to reaction tem-perature under hydrogen flow. Following the activation step, thesynthesis gas with the determined flow rate and H2/Co ratio of 2is introduced to the reactor and the setup pressure is increasedto the desired value. The outlet of the reactor pass through twohot and cold traps and the uncondensed gas stream is purged toatmospheric pressure through a pressure regulator. Two Agilent7890-A chromatographs are utilized to analyze the type and com-position of outlet gas stream and condensate products from hotand cold traps.

2.3. Fractional factorial design

The two-level factorial design, the most popular method amongfactorial designs and widely used method in the industrial experi-ments, is used in order to study the general results and present amodel for operating parameters [15]. Two fractional factorialdesign method is one of the two fractional designs with 2k–p num-bers of experiments while its factors vary in two levels which arethe maximum and minimum values of that factor. This experimen-tal design method makes it possible to study many factors with rel-atively few run numbers and is commonly used to find outimportant effects, especially when runs are expensive and timeconsuming. The unknown response function is approximated bya polynomial of corresponding degree for which the regressioncoefficients are estimated based on the experimental results. Aquadratic (polynomial equation) mathematical model is consid-ered in the first phase of a research project. Defining the two orderregression model is the first phase of a study with the objective ofobtaining an interpolation model or function, the knowledge ofwhich facilitates estimating response values at different points ofthe studied factorial space. Later the polynomial degree would beincreased in the case of necessity. In definition of a quadraticmodel (Eq. (1)), the coefficient b0 and all other coefficients bi, andbij, are calculated.

y ¼ b0 þXk

i¼1

bixi þXk

iPj�1

bijxixj ð1Þ


where y is the response value; bi and bij are regression coefficientsof double factor interactions which show the effective value of eachparameter. Accuracy and confidence of the obtained estimation forthe regression coefficients depend on the used design of experi-ments. The selected effective parameters for the Fischer Tropschsynthesis with their minimum and maximum values are presentedin Table 1. Temperature, pressure and Gas Hourly Space velocity(GHSV) are the considered operative parameters and cobalt loadingof the catalyst is another effective parameter which is studiedherein. The experimental plan of the tests according to the two-level factorial design and their results are listed in Table 2. It shouldbe mentioned that in order to reduce the random error, the exper-iments were carried out following the firs column of Table 2 whichis ordered randomly [15]. The studied parameters were varied inthe two levels of maximum and minimum to determine not onlythe main effects, but also the interactions between the parameterson the FTS.

3. Result and discussion

Fig. 2 demonstrates the results of temperature programmedreduction (TPR) spectra of the monolithic catalyst with 15% cobaltloading. Obviously these catalysts exhibit three partly overlappedpeaks. The first peak usually corresponds to the reduction ofCo3O4 into CoO while the second peak is assigned to the reductionof CoO into elemental cobalt (Co�). The third peak can be assigned tothe gasification of carbon [16]. The third peak also presents thereduction of cobalt-support interacted species and is extended tohigher temperatures.

Applying the two level fractional factorial design, sixteenexperiments were specified. The obtained results were processedby ‘‘Design expert (DX)’’ software (State-Ease, version 7.1.3, USA)and a quadratic polynomial was obtained for each of the impor-tant responses (CO conversion, Cþ5 selectivity and C1 selectivity)in the range beyond the selected domain of experiments. Themodels for behavior of mentioned responses are presented asbelow:

CO conversion ¼ 72:86þ 4:41T þ 2:52P þ 2:97Gþ 1:97W

þ 1:02TP � 0:58TGþ 1:02TW � 0:91PG

þ 0:091PW þ 0:52GW ð2Þ

5



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3 May 2014

Cþ5 selectivity ¼ 76:24� 1:19T þ 4:62P � 2:47Gþ 1:47W

þ 0:81TP þ 1:64TGþ 0:48TW � 1:09PG

� 5:36PW þ 0:48GW ð3Þ

C1 selectivity ¼ 22:52þ 0:25T � 4:54P þ 2:9G� 0:86W

� 0:88TP � 1:28TGþ 0:2TW þ 1:1PG

þ 5:69PW � 0:74GW ð4Þ
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(a)

(c)

(e)

Pressure (bar)

180.00 195.00 210.00 225.00 240.00

Temperature (oC)

C5+

Sel

ectiv

ity

60

67.75

75.5

83.25

91

B-

B+

Cobalt%

180.00 195.00 210.00 225.00 240.00

Temperature (oC)

C5+

Sel

ectiv

ity

60

67.75

75.5

83.25

91

D-D+

Cobalt%

15.00 20.00 25.00 30.00 35.00Pressure (bar)

C5+

Sel

ectiv

ity

60

67.75

75.5

83.25

91

D-

D+

Fig. 6. The interaction of para


In the presented models, all variables are indicated in terms ofvalues listed in the first column of Table 1. The value of coefficientsin the above models showed that the interactions are important inFTS, so the results are presented and discussed in terms of theseinteractions. The statistical significance of Eq. (2) is shown in Table3 where the mean square, sum of squares and model degree offreedom (DOF) terms are respectively defined as the estimationof the model variance, total of the sum of squares for the modelterms and the number of model terms. DOF depicts the total num-ber of model terms including the intercept, minus one. Considering

(b)

(d)

(f)

GHSV

180.00 195.00 210.00 225.00 240.00

Temperature (oC)

C5+

Sel

ectiv

ity

60

67.75

75.5

83.25

91

C-

C+

GHSV

15.00 20.00 25.00 30.00 35.00

Pressure (bar)

C5+

Sel

ectiv

ity

60

67.75

75.5

83.25

91

C-

C+

Cobalt%

900.00 1025.00 1150.00 1275.00 1400.00GHSV

C5+

Sel

ectiv

ity

60

67.75

75.5

83.25

91

D-D+

meters on Cþ5 selectivity.



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Actual CH4 Selectivity

Pred

icte

d C

H4

Sele

ctiv

ity

7.00

14.50

22.00

29.50

37.00

7.96 15.12 22.28 29.44 36.60

Fig. 7. The predicted value of C1 selectivity via its actual value.



3 May 2014

the model F value and a low probability value, the introduced mod-els are highly significant. The P-value is a quantitative measure-ment in order to report the results of a hypothesis test because itis the probability of the test statistics being at least as extremeas the one observed that shows the null hypothesis is true. The sig-nificance of each parameter can be characterized by the P-value.The analysis of variance (F-test) demonstrates that the secondorder model (quadratic polynomial) well meets the experimentaldata. It is believed that the larger the sum of square for each factor,the more effect it has on the FTS CO conversion, Cþ5 and C1 selectiv-ity. The predicted values of CO conversion versus the actual valuesare demonstrated via Fig. 3. It is clear that the high value of R-square (R2 = 0.9611) proves that the equation well representedthe reaction under the given experimental domain.

As specified in Eq. (2), factor T which stands for temperaturehad the main effect on CO conversion due to its higher coefficientand the binary letters (TP) are the main interaction between twofactors. It should be mentioned that a positive sign represents thatincreasing the factor causes an increase on the response, while anegative coefficient sign shows that increase of the factor leadsto a decrease of the response. As explained above, increasing theGHSV increases the CO conversion. According to the representedmodel, temperature, pressure and cobalt loading have positiverelationship with the CO conversion which means that theirincrease causes an increase of conversion, but temperature is moreeffective than other parameters. Farias et al. [17] demonstratedthat gas velocity increase in presence of monolithic catalystincreases the CO conversion. As reported by Farias et al. [18] andTian and He [12], it was found that higher temperatures promoteCO dissociation and provide more surface C atoms leading to therelease of hydrocarbons. Since the plots make it easy to interprettwo parameter interactions, the main interactions of variables onthe CO conversion are demonstrated in Fig. 4.

Two crossed (non-parallel) lines appear indicating that theeffect of one factor depends on the level of the other one. Eachcurve has two lines: one plotted for the maximum value of factorsand the other one for its minimum value. The interaction betweenreaction pressure and temperature has the strongest effects onthe CO conversion because the highest coefficient of Eq. (2) is rel-evant to T and P interaction. It is clear from the figure that anincrease of reaction temperature at minimum GHSV increasesthe conversion.

The statistical significance of Eq. (3) (Cþ5 selectivity model) andEq. (4) (C1 selectivity model) is shown in Table 3 for all parametersand their interactions. The predicted values of Cþ5 selectivity via itsactual values are depicted in Fig. 5 and the main interactions ofvariables on it are presented in Fig. 6. The effects of temperatureand pressure on hydrocarbon chain are complicated. According toFarias report [17] pressure augmentation at lower temperatureincreases lower hydrocarbon production, but its reduction athigher temperature leads to higher production of C21–C25. Simulta-neous increment of temperature and pressure leads to productionof hydrocarbons with more than 35 carbon [17]. The Cþ5 selectivitymodel indicates that pressure and at the lower extent GHSV havethe most significant effects and the binary letter interaction ofpressure and cobalt loading are the main interactions betweentwo factors. According to the model, increasing the pressure causesan increase in Cþ5 selectivity while increasing the temperaturereduces its value. It is quite agreed with the previous researchesin which higher pressure is preferred for the chain growth becauseincreasing the pressure improves the collision probabilities of reac-tants on catalyst surface. The same effects of temperature werereported by Fernandes et al. [17]. Results clearly demonstrate thatin presence of monolithic catalysts the selectivity to Cþ5 decreaseswith increasing GHSV as has been shown by other researches[13]. Cobalt loading shows the positive relationship with the


heavier hydrocarbon selectivity according to the model. It has beenreported that there is a positive relationship between Cþ5 selectivityand cobalt particle size. It is believed that for the larger Co parti-cles, the CO is adsorbed more easily [14].

As can be seen from Fig. 6b increase of temperature at mini-mum value of GHSV reduces the Cþ5 selectivity, but at maximumvalue of GHSV causes increase of heavier hydrocarbon selectivity.Selectivity of the reaction depends on the temperature becausethe activation energy of the termination step is higher than thatof the growing step [19]. It can be ascribed to higher mobility ofthe reactants at higher temperatures which causes production ofheavier hydrocarbons. Fig. 6d shows that at both maximum andminimum values of GHSV, pressure augmentation increases theCþ5 selectivity. Increasing temperature at maximum and minimumvalues of pressure scales down the Cþ5 selectivity.

The developed model for the C1 selectivity shows that similar tothe Cþ5 selectivity, the most effective parameter is the pressure andits increase leads to reduction of methane selectivity. Methanereduction can be ascribed to presence of CH2 monomers at higherpressure which causes chain growth [20]. The next effectiveparameter on methane selectivity is the GHSV while increasingits value increases the production of lower molecular weighthydrocarbons. The temperature increase would also enhance thevalue of C1 selectivity which is in good agreement with the litera-ture [7]. Fig. 7 predicts the values of methane selectivity via itsactual values and Fig. 8 demonstrates the main interaction ofvariables.

Atashi declared that temperature effect on selectivity is notmanifest and self sufficient, but its effectiveness relate to the cata-lyst type and other process condition. He implied that increasingthe temperature would lower the heavier product selectivity, butit is not an influential factor for light hydrocarbons [14] which isin good agreement with our investigation.

The interaction of pressure with cobalt loading and GHSV withtemperature showed the most significant effects on the methaneselectivity: temperature raise at minimum and maximum valuesof GHSV causes enhancement and reduction of the C1 selectivityrespectively (b) while pressure increase at both values of GHSVreduces the value of C1 selectivity (d).

It worth to be noted that the two non-parallel lines of theseplots show the interaction of the two factors during the selectedrange of parameters to carry out the experiments.



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(a) (b)

(c) (d)

(e) (f)

Pressure (bar)

180.00 195.00 210.00 225.00 240.00

Temperature (oC)

CH

4 Se

lect

ivity

8

15.25

22.5

29.75

37

B-

B+

GHSV

180.00 195.00 210.00 225.00 240.00

Temperature (oC)

CH

4 Se

lect

ivity

8

15.25

22.5

29.75

37

C-

C+

Cobalt%

180.00 195.00 210.00 225.00 240.00

Temperature (oC)

CH

4 Se

lect

ivity

8

15.25

22.5

29.75

37

D-D+

GHSV

15.00 20.00 25.00 30.00 35.00

Pressure (bar)

CH

4 Se

lect

ivity

8

15.25

22.5

29.75

37

C-

C+

Cobalt%

15.00 20.00 25.00 30.00 35.00

Pressure (bar)

CH

4 Se

lect

ivity

8

15.25

22.5

29.75

37

D-

D+

Cobalt%

900.00 1025.00 1150.00 1275.00 1400.00

GHSV

CH

4 Se

lect

ivity

8

15.25

22.5

29.75

37

D-D+

Fig. 8. The interaction of parameters on C1 selectivity.



3 May 2014

4. Conclusion

In this work the Fischer Tropsch synthesis have been performedover bulk monolith prepared from carbon nanotubes supportedcobalt catalyst. Screening the effects of temperature, pressure,GHSV and cobalt loading on CO conversion, Cþ5 and C1 selectivitywas accomplished by using a two level factorial design methodthrough 16 experiments. The Fischer Tropsch synthesis was inves-tigated in the range of 180–240 �C, 15–35 bar, gas space velocity of


900–1400 h�1 and cobalt loading of 15–35% by weight. Quadraticmodels were developed to predict the conversion and product dis-tribution based on the results. The results showed that tempera-ture, GHSV and pressure are the main effects, in orderconsidering CO conversion. Pressure augmentation leads toincrease of CO conversion and heavier hydrocarbon productionwhile temperature increment causes CO conversion and methaneproduction increase. The effect of GHSV on conversion and produc-tivity was controversial subject while in presence of monolithic



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3 May 2014

catalysts its enhancement leads to decrement of heavier hydrocar-bons production. The obtained results are in good agreement withthe reported data in the literature.

Moreover, among the effects of all interactions between thestudied effective factors on the conversion, the interactionbetween pressure and temperature had the most significant effect.The models of Cþ5 and C1 selectivity show pressure as the mosteffective parameter while the interaction of pressure-cobalt load-ing and temperature-GHSV are the greatest interactions amongall. The interaction of operational variables and their effect onthe CO conversion and product selectivity has been discussed inthis paper thoroughly.

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Study of effective parameters in the Fischer Tropsch synthesis using monolithic CNT supported cobalt catalysts