Simultaneous Desulfurization and Denitrogenation of Liquid Fuel by

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SIMULTANEOUS DESULFURIZATION AND DENITROGENATION OF LIQUID FUEL BY NICKEL MODIFIED GRANULAR ACTIVATED CARBON Sandeep Kumar Thaligari, Shelaka Gupta, Vimal Chandra Srivastava, and Basheshwer Prasad Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b00579 • Publication Date (Web): 31 May 2016 Downloaded from http://pubs.acs.org on June 4, 2016

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SIMULTANEOUS DESULFURIZATION AND DENITROGENATION OF LIQUID FUEL BY NICKEL MODIFIED GRANULAR ACTIVATED CARBON Sandeep Kumar Thaligari1, Shelaka Gupta1,2, Vimal Chandra Srivastava1,*, Basheshwer Prasad1 1

Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667 Uttarakhand, India 2 Department of Chemical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016 Delhi, India *Corresponding author. Tel.: +91-1332-285889. E-mails: [email protected] (VCS), [email protected] (TSK), [email protected] (SK), [email protected] (BP) ABSTRACT Simultaneous adsorption of sulfur compound (dibenzothiophene, DBT) and nitrogenous compound (quinoline, QN) was studied onto nickel loaded granular activated carbon (Ni-GAC). Taguchi’s L27 methodology was used to investigate the effect of various operating parametersand optimize them. Parameters studied during simultaneous adsorption included initial DBT/QN concentration (Co,i), Ni-GAC dose, temperature and contact time. Ni-GAC dose was found to be the most significant factor while the interaction between Co,i’s was also significant. Optimized parameters of Taguchi experiment were further used for determining binary isotherm behavior. Adsorption capacity of Ni-GAC for QN adsorption was found to be greater than DBT. Among various multi-component empirical models, extended-Freundlich model best followed the binary isotherm experimental data at 303 K. DBT and QN adsorption data was also analyzed and represented by models based on idealand real-adsorbed solution theory. Keywords: Desulfurization; denitrogenation; adsorption; multi-component adsorption; isotherm modeling; adsorbed solution theory models.

1. INTRODUCTION

1 ACS Paragon Plus Environment

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Majority of energy produced in the world is still derived from the fossil fuels and half of it comes from petroleum. Due to more and more stringent environmental regulations, desulfurization and denitrogenation of liquid fuels is becoming a challenging task.1 Though, highly beneficial to the environment, desulfurization is a big economical and operational challenge.2 Transportation fuels containing sulfur and nitrogenous compounds produce oxides of sulfur and nitrogen which are detrimental to the environment and also SOx poisons the catalyst which is used in the catalytic convertors.1 The most conventional method used by the industries for sulfur removal is hydrodesulphurization (HDS) which is a very expensive process due to very high operation conditions and requirement of hydrogen gas.3,4 HDS is ineffective in removing refractory dibenzothiophene (DBT) and its alkyl derivatives.5 Owing to this, various other methods such as

oxidative

desulfurization,

adsorptive

desulfurization,

reactive

desulfurization,

biodesulfurization, etc. are being researched.6-14 Nitrogen compounds present in the liquid fuels hamper the desulfurization process as they are more attracted than the sulfur compounds for the catalytic/adsorption sites. Moreover, nitrogen present in oil makes the oil more viscous which is a serious concern for transportation of the oil. Nitrogen compounds produce NH3 during hydrocarbon reforming which acts as a poison to the catalyst. Hence, the removal of the nitrogen compounds like quinoline (QN) present in the fossil fuel is getting high attention.15 Hydro denitrogenation (HDN) occurs in parallel with HDS process. Various other methods are used for the denitrogenation of fuels such as solvent extraction using ionic liquids.16,17 Desulfurization by extraction with membrane and adsorption is cheaper as compared to HDS because these methods have the advantage of non requirement of the H2 gas, and easy operability at ambient pressure and temperature, thus reducing the cost of the


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process.18,19 Studies on adsorption of nitrogenous compounds by activated carbon, copper zeolites, zirconia, activated alumina, etc. has been reported earlier.20–22 Few studies are reported on use of Ni impregnated GAC (Ni-GAC) for flue gas desulfurization,23-25 however, there is no substantial literature on use of Ni-GAC for desulfurization of liquid fuels or for simultaneous desulfurization and denitrogenation. Guo et al.23 used various Ni catalysts supported on untreated and acid (H2SO4 and HNO3) treated activated carbons (AC) for adsorption of SO2 from flue gas. Guo et al.24 reported effect of calcination temperature on chemical state of Ni loaded on AC and flue gas desulfurization. Gong et al.25 used HNO3 treated Ni-loaded activated carbons (prepared by ultrasonic-assisted impregnation method) for flue gas desulfurization. Taguchi’s orthogonal array, a statistical technique, is used for effectively studying the effect of various parameters and their interaction in minimum number of experimental runs at some specified conditions in time- and cost-effective manner.26 Various investigators have used this technique for optimization of parameters in the field of chemical and environmental engineering.27-32 In the current study, Taguchi’s experimental design methodology has been applied for optimizing simultaneous DBT and QN adsorption using Ni-GAC as an adsorbent. Binary adsorption isothermal data at 303 K was experimentally collected obtained and further represented by various empirical models, and adsorbed solution theory (AST) based thermodynamic models namely ideal-AST (IAST) and real-AST (RAST).

2. EPERIMENTAL SECTION 2.1. Chemicals and Model Oil. GAC (G S chemical testing lab and allied industries, Delhi); Ni(NO3)2.6H2O (Himedia Laboratories Pvt. Ltd, Mumbai); DBT (Spectrochem Pvt. Ltd. Mumbai); and QN and iso-octane (S.D. fine Chemicals, Mumbai) were purchased from


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various companies. Model fuel used was a mixture of DBT or/and QN dissolved in isooctane. 2.2. Preparation of Ni-GAC and its Characterization. First, as received GAC was washed a number of times with distilled water so as to completely remove the associated ash. Sample was made moisture free in an air-oven at 110°C. Dried GAC was stirred with 30% HNO3 solution. It was water-washed again till the pH of the used-up water was neutral. Adsorbent was again dried, and further stirred with the aqueous solution of the nickel nitrate hexahydrate to get Ni loading of 1% (which was found to be the best in the preliminary study). The volume of the solution used was 10% excess with respect to the pore volume of the GAC. After Ni loading, the prepared sample was calcined at 400°C for 3 h. Then the adsorbent was cooled in the dessicator and later used for desulfurization and denitrogenation. In order to confirm the nickel loading, 1 g of the sampled was soaked in 10 ml of 65% nitric acid for 24 h for getting Ni dissolved from the Ni-GAC. Filtered solution was analyzed with help of atomic absorption spectrophotometer to assess the concentration of the Ni.33 Ni impregnation range was found to be 0.90 times of that desired. Morphology of the Ni-GAC was analyzed using QUANTA, Model 200 FEG, USA scanning electron microscope (SEM). X-ray diffraction (XRD) analysis was performed with the XRD instrument (Bruker AXS, Diffractometer D8, Germany). Micromeritics ASAP 2020 apparatus was used for textural characterization. 2.3. Experimentation. For the experimental study, initially model fuel was prepared by taking 31.25 and 71.42 mmol/L of DBT and QN individually. Two solutions were mixed in appropriate proportion to get mixtures having different concentrations of DBT and QN. For each experiment, 10 ml of the desired concentration solutions inside the 25 ml test tubes were stirred at 700 rpm. For the Taguchi’s experiments the temperature was varied i.e. at 15°C, 30°C and 45°C. After sufficient residence time, residual sulfur and nitrogen


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concentrations was determined using a gas chromatograph (GC) and appropriate analysis procedure.32

3. RESULTS AND DISCUSSIONS 3.1. Adsorbent Characterization. Blank GAC and different percentage Ni loaded GAC were tested for their efficacy in DBT removal. Blank-GAC, 1%Ni-GAC, 2%Ni-GAC, 3%Ni-GAC and 4%Ni-GAC gave 43%, 62.2%, 53.2% and 49.6% DBT removal efficiencies (Figure S1 in supporting document). It may be seen that 1%Ni-GAC gave maximum DBT removal and that the removal efficiency decreased with an increase in Ni loading. This was due to decrease in Brunauer–Emmett–Teller (BET) surface area of the Ni-GACs. Further characterization and experiments were conducted with 1%Ni-GAC only. Adsorbent is referred as Ni-GAC only. Figure 1 compares the morphology of virgin- and Ni-GAC using SEM. Blank GAC seems to have good porosity which decreased a little after Ni impregnation. Ni-GAC seems to posses more homogeneous surface. Broad peaks at 2θ=24° and 44° in the XRD spectra (Figure S2) represent amorphous carbon in Ni-GAC before adsorption. The XRD patterns of Ni-GAC after simultaneous DBT and QN adsorption (here-to-after referred as Ni-GAC-SN) also indicates its amorphous state.34 Figure 2 shows the liquid nitrogen adsorption-desorption isotherms and variation of cumulative pore volume of Ni-GAC before and after adsorption. All the samples exhibit type IV isotherm with H1 hysteresis loop indicating mesoporous nature of the Ni-GAC. GAC (without loading of Ni) was found to have a surface area of 591 m2/g which decreased to 553.2 m2/g for Ni-GAC after Ni loading. This was due to the loading of Ni species onto GAC in which loaded Ni may have blocked some of the micropores. The BET surface area of Ni-GAC-SN further decreased to 302.4 m2/g. Single point total pore volume for blank-Ni-GAC was 0.312 cm³/g and after adsorption for Ni-GAC-


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SN, it was 0.164 cm³/g. Barrett–Joyner–Halenda (BJH) adsorption and desorption area (17 Å < pore size < 3000 Å) was 48.3 m2/g and 37.6 m²/g, respectively. EDX of blank GAC, NiGAC, Ni-GAC-S and Ni-GAC-N was conducted and the results are shown in Figure S3. Virgin GAC possessed 86.83% C and 13.17% O; whereas Ni-GAC contained 88.22% C, 9.92% O and 1.87% Ni. EDX analysis of DBT loaded Ni-GAC (here-to-after referred as NiGAC-S) showed 88.28% C, 9.61% O, 1.52% Ni and 0.59% S. Similarly, QN loaded Ni-GAC (here-to-after referred as Ni-GAC-N) was found to 88.34% C, 9.63% O, 1.46% Ni and 0.57% N. The increase in C content after adsorption may be due to the loading of DBT and QN. TGA and DTA curves of the Ni-GAC and Ni-GAC-SN are shown in Figure S4(a) and S4(b), respectively. Mass loss up to 400°C because of moisture and volatile removal was 18% for blank GAC and 17.69% for Ni-GAC-SN. Ni-GAC and Ni-GAC-SN did not show any phase change peak up to 400°C.35 Blank Ni-GAC showed 80.5% mass loss in the temperature range of 400-585°C whereas Ni-GAC-SN showed 77.45% mass loss in the temperature range of 400-625°C. These peaks represent oxidative degradation of the NiGACs. DTA analysis shows two peaks for blank Ni-GAC. These peaks combine together during degradation of Ni-GAC-SN and that the amount of energy released is higher for NiGAC-SN (20 MJ/kg) is about three times of blank Ni-GAC (7.03 MJ/kg). This study shows that the adsorbent is thermally stable up to 400°C. FTIR is a technique of identifying the functional groups responsible for the adsorption. FTIR of blank Ni-GAC (Figure 3) shows a broad band at 3430.81 cm-1 due to free and hydrogen bonded OH groups. At 1570 cm-1, the spectrum shows a broad band due to CO group from aldehydes and ketones. The band around 1461 cm-1 may be due to carbonyl group which may be conjugated hydrocarbon bonded. Peak at 1200 cm-1 may be due to –CO and SiO-Si stretching or due to CC group vibration in lactones and also due to deformation of –OH


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group. The peak at ≈750 cm-1 signifies SiH bond. Shift in the peaks after adsorption is due to the functional groups usage during DBT and QN adsorption. Adsorption of organo sulfur compounds and basic organo nitrogen compounds such as DBT and QN, respectively, on to Ni-GAC is dominated by Bronsted base (through lone pair of electrons on S and N atom in DBT and QN, respectively) and acid (Ni2+ on GAC) interaction.36 Therefore, Ni loading on GAC enhances the adsorptive desulfurization and denitrogenation as compared to blank GAC. 3.2. Optimization of Simultaneous Desulfurization and Denitrogenation Parameters by Taguchi’s orthogonal array (OA) Method. Five operating parameters that affect the simultaneous adsorption of QN and DBT from iso-octane onto Ni-GAC were identified based on the preliminary and previous studies.20–22,37,38 Levels of these parameters used in the study are given in Table 1. Two parameter interaction between initial concentration of DBT/QN i.e. (Co,DBT × Co,QN) was also studied as the initial concentration of one DBT/QN (Co,i) affects the adsorption of other QN/DBT during simultaneous adsorption. L27 OA (Table 2) with 27 experiments has total degree of freedom (DOF) greater than the DOF for the experiments.27 Therefore, experiments were conducted in triplicate as per the conditions given for each run in Table 2. The individual (qDBT, qQN) and the total (qtot) adsorption capacities were calculated using the following relationship: q tot = q DBT + q QN =  ( C o ,DBT − C e ,DBT ) + ( C o ,QN − C e ,QN )  m

(3)

where, Co,i and Ce,i are the initial and the equilibrium DBT/QN concentration (mmol/l) and m is the Ni-GAC dose (g/l). Table 2 shows the average value of qDBT, qQN and qtot for each experiment. 3.2.1. Effect of operating parameters. The average value of qtot, qDBT and qQN for each factor at three levels are given in Table 3 in which higher is the difference between two levels, greater is its significance and affect on adsorption capacities. Operating parameters are 7 ACS Paragon Plus Environment

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found to significantly affect the adsorption process. The effect of Co,DBT w.r.t Co,QN and vice versa are found to significantly affect the adsorption capacities. From Table 3, it can be seen that the Ni-GAC dose (m) has the greatest affect at level 1 whereas Co,QN has highest significance at levels 2 and 3. Overall, Co.i has a stronger influence on qtot than other parameters. qtot increased with an increase in Co,i due to the decrease in resistance of the DBT/QN uptake because of increased driving force. From Table 3, it can be observed that m has greatest effect at level 1 and Co,i at level 3 on qs. It was also found that the difference between levels 1 and 2 is highest for Co,DBT, and hence, Co,DBT has stronger influence on qDBT. Similarly, from Table 3, it can be seen that at level 1 again m has highest effect on qQN while at levels 2 and 3, Co,QN holds the largest effect. Again in case of qQN, the difference between levels 2 and 3 is largest for Co.QN which indicates that it has strongest effect. The effects of parameters on adsorption capacities are given in the Figure 4. An increase in Co,i, T, and t from 1 to 3 results in an increase in qtot values. As adsorption is an exothermic process, it is expected that the q values decrease with an increase in temperature. However, an increase in temperature shows different pattern for qDBT and qQN. It is observed that when temperature increases from 15°C to 30°C, qDBT increases but in this temperature range qQN decreases, on the other hand when adsorption of DBT decreases for T ≥ 30°C, qDBT increases. This is because both DBT and QN compete for the same adsorption site. So at first, when sulfur is adsorbed more, adsorption of QN is less. On the other hand when adsorption of DBT decreases, QN adsorption increases. But qtot shows an increase with increase in temperature which suggests that the overall simultaneous DBT and QN adsorption is endothermic and is diffusion controlled.40 Overall, significant competition seems to exist between the two adsorbates for the same adsorption sites.


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Figure 4 shows that the DBT adsorption increased with contact time until equilibrium was achieved between DBT and Ni-GAC. Same is observed for QN but in that case, equilibrium is achieved much earlier. Overall, qtot value increased continuously with time in the studied range. Initially, numbers of vacant sites are greater which get occupied with time and also repulsive forces between the adsorbates decrease the rate of adsorption with time. Further the DBT and QN molecule have to travel deeper into the pores which results in higher resistance. As a result, adsorption slows down in the later stages. As far as m is concerned, it can be seen that as m increases, first from level 1 to level 2 and then from 2 to 3, the value of qtot decreases. Although, with an increase in m, q values decrease, however, percent removal increases because of the availability of more sites for adsorption.40 Figure 5 shows that the interactive plot between Co,DBT and Co,QN. Higher is the difference in the slope of lines in Figure 5, higher is the interaction between the Co,i values whereas zero interaction is shown by parallel lines.41 It is clear from the graph the Co,i values significantly affect the average value of qtot. ANOVA results are given in Table 4. The percentage contribution of each factor for qDBT, qQN and qtot is shown in the bar graph in Table 4. It can also be seen that the interaction between A and B also contributes significantly to the raw data for simultaneous as well as individual DBT/QN removal onto Ni-GAC. 3.2.2. Selection of optimum levels and maximized response estimation. During this study, greater value of qtot was considered as better. After examining the average value of qtot (Figure 4), optimum level of parameters were obtained. Parameter m at level 1 and parameters Co,i, T and t at level 3 show higher values of qtot which simultaneously maximize the DBT and QN adsorption by Ni-GAC. The maximum possible level of qtot was calculated by following relationship:27,28

q tot,predicted = T + (A3 − T) + (B3 − T) + (C3 − T) + (D1 − T) + (E3 − T) 9 ACS Paragon Plus Environment

(4)

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The predicted maximum value of qtot, qDBT and qQN for Ni-GAC was 275.4 mmol/g, 103.5 mmol/g and 171.9 mmol/g, respectively. Finally optimized parameters were verified by three confirmation experiments and the results were within 95% confidence interval. 3.3. Binary Isotherm Study. It was performed by quantifying the individual qe and Ce values during simultaneous adsorption of DBT and QN at 30°C. For each Co of sulfur in terms of DBT: 50, 100, 250, 500 and 750 mg/l; Co of QN was varied in the range of 50-750 mg/l. Table 5 shows that qe,QN increased as Co,QN increased for each concentration of DBT. But qe,QN decreased as Co,DBT increased. This was true for DBT adsorption in presence of QN. At Co,QN=53.57 mmol/l (750 mg/l) along with Co,DBT=23.4375 mmol/l (750 mg/l), qe,QN was 0.5469 mmol/g while at Co,QN=53.57 mmol/l with no DBT present, qe,QN. was 0.9897 mmol/g. Overall QN adsorption onto Ni-GAC was higher than that of DBT. QN has the higher value of the negative electrostatic potential (-0.060 a.u.), dipole moment (1.844 D) and ionization potential (9.241 eV) than DBT which has respective values of +0.243 a.u., 1.362 D and 8.598 eV. QN is a basic compound and higher affinity of Ni-GAC for QN may be due to the acid– base interaction.32,37,38 3.3.1. Empirical Models. Various multi-component isotherms like non-modified, modified and extended-Langmuir models, modified R-P model, extended-Freundlich model, etc. were used for representing the binary DBT and QN adsorption data using sum of square of error (SSE) which was calculated by the following equation:42,43 n

SSE = ∑ ((qe,meas − qe,calc ) N + (qe,meas − qe,calc ) S ) i2

(5)

i =1

Values of various parameters of multi-component isotherms along with SSE values are given in Table 6. Non-modified models showed poor fit as their SSE values are high. Extended-Langmuir with SSE value 0.1764 was better fit as compared to modified-Langmuir with SSE value 0.5476. In extended-Langmuir model, Ki value reflects the affinity between


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DBT/QN and Ni-GAC in a binary mixture which is 0.2736 for QN and 0.2017 for DBT while overall (DBT+QN) uptake, qmax is 0.9004 mmmol/g. Best fit of the binary adsorption data is given by Extended-Freundlich model with lowest SSE value 0.1631. Figure 6 presents the parity plots for experimental and predicted qe values of DBT and QN. It can be seen in the plots that except for non-modified Langmuir and non-modified R-P model, the experimental points lie around the diagonal lines for rest of the models. Overall Extended-Freundlich model best followed the binary adsorption data. It may be because of the heterogeneous nature of the Ni-GAC and chemisorptive nature of the adsorption as indicated by fitting of the pseudo-send order kinetic model and best-representation of isotherm data by R-P model. 3.3.2. Ideal adsorbed solution theory (IAST) and real adsorbed solution theory (RAST) models. In this part, adsorption data of DBT and QN onto Ni-GAC were analyzed with IAST and RAST model. Details of these models and procedure are given elsewhere.31 Non-ideality in the binary system was accounted by incorporating activity coefficient (a function of spreading pressure) into the basic equation of IAST. Wilson activity coefficient model was used for estimating activity coefficient. The results were analyzed by comparing the statistical values which are calculated between predicated adsorbed phase concentration and experimental values of the binary system of DBT and QN. Figure 7 shows the plot of predicted adsorbed phase and experimental values of DBT and QN. From Figure 7, it is clear that the RAST (SSE=0.32) model which assumes non-ideal behavior in the system provides better fitting than IAST model (SSE=2.23).44

4. CONCLUSIONS Nickel loaded granular activated carbon (Ni-GAC) was synthesized and characterized by various techniques. It was further used for the simultaneous DBT and QN adsorption. Various characterization tests like liquid nitrogen adsorption, EDX and FTIR


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helped in understanding the mechanism of adsorption. Taguchi’s method helped in optimizing the operating parameters for binary DBT and QN adsorption. Ni-GAC dose was found to be the most significant factor while the interaction between Co,i’s was also found to be significant. For the binary system containing both DBT and QN, equilibrium adsorption data was obtained for Co,DBT=1.56-23.43 mmol/l, Co,QN=3.57-53.57 mmol/l, T=303 K, t=9 h, m=20 g/l. DBT and QN were found to compete for the adsorption sites and their interaction was antagonistic in nature. QN adsorbed more on NI-GAC because of its basic nature. than that for DBT. Among the empirical models, extended-Freundlich isotherm best-fitted the binary experimental isotherm data. RAST model provided better representation of the simultaneous DBT and QN adsorption onto Ni-GAC than the IAST model.

ASSOCIATED CONTENT Supporting information Variation of sulfur removal with Ni loading on GAC; XRD pattern of Ni-GAC before and after sulfur and nitrogen adsorption; Fe-SEM images, EDX analysis, differential thermal gravimetric and differential thermal analysis of Ni-GACs before and after adsorption. Supporting information is available at http://pubs.acs.org.

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Gong, M.; Wang, X.; Guo, J.; Liu, Y.; Yin, H. Asian J. Chem. 2014, 26 (23), 8191– 8196.

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Wu, Y.; Xiao, J.; Wu, L.; Chen, M.; Xi, H.; Li, Z.; Wang, H. J. Phys. Chem. C, 2014, 118, 22533–22543.


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Page 16 of 29

Table 1. Multi-component adsorption study parameters for the adsorption of DBT and QN onto Ni-GAC using Taguchi’s OA. Parameters

Units

Levels 0

1

2

A:

Initial concentration of DBT

Co, DBT

mmol/l

0

7.81

15.62

B:

Initial concentration of QN

Co, QN

mmol/l

0

17.86

35.71

C:

Temperature

T

(°C)

15

30

45

D:

Ni-GAC dose

m

(g/l)

5

15

25

E:

Contact time

t

(min)

60

360

660


Page 17 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Table 2. Taguchi’s L27 (313) orthogonal array for multi-component adsorption of DBT and QN system onto Ni-GAC. Exp. No.

A

B

AxB

AxB

C

D

E

qDBT

qQN

qtot

1

0

0

0

0

15

5

1

0.00

0.00

0.00

2

0

0

0

0

30

15

6

0.00

0.00

0.00

3

0

0

0

0

45

25

11

0.00

0.00

0.00

4

0

17.85

1

1

15

5

1

0.00

144.57

144.57

5

0

17.85

1

1

30

15

6

0.00

57.93

57.93

6

0

17.85

1

1

45

25

11

0.00

47.85

47.85

7

0

35.71

2

2

15

5

1

0.00

182.00

182.00

8

0

35.71

2

2

30

15

6

0.00

65.07

65.07

9

0

35.71

2

2

45

25

11

0.00

85.37

85.37

10

7.81

0

1

2

15

15

11

8.01

0.00

8.01

11

7.81

0

1

2

30

25

1

4.76

0.00

4.76

12

7.81

0

1

2

45

5

6

63.19

0.00

63.19

13

7.81

17.85

2

0

15

15

11

22.67

59.68

82.35

14

7.81

17.85

2

0

30

25

1

13.15

30.44

43.59

15

7.81

17.85

2

0

45

5

6

56.23

145.11

201.34

16

7.81

35.71

0

1

15

15

11

17.04

64.43

81.47

17

7.81

35.71

0

1

30

25

1

8.02

58.21

66.24

18

7.81

35.71

0

1

45

5

6

40.14

138.93

179.07

19

15.62

0

2

1

15

25

6

19.03

0.00

19.03

20

15.62

0

2

1

30

5

11

150.74

0.00

150.74

21

15.62

0

2

1

45

15

1

34.29

0.00

34.29

22

15.62

17.85

0

2

15

25

6

13.36

28.17

41.53

23

15.62

17.85

0

2

30

5

11

87.59

57.21

144.81

24

15.62

17.85

0

2

45

15

1

31.31

32.65

63.97

25

15.62

35.71

1

0

15

25

6

13.99

52.20

66.19

26

15.62

35.71

1

0

30

5

11

47.06

144.57

191.63

27

15.62

35.71

1

0

45

15

1

17.45

57.29

74.73

Total

648.04


1451.68 2099.72

Energy & Fuels

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Page 18 of 29

Table 3. Average and main effect of qtot, qDBT and qQN values. Parameter

Raw data, Average value

Main effect (Raw data)

L1

L2

L3

L2-L1

L3-L2

A

64.75

81.11

87.44

16.36

6.33

B

31.11

91.99

110.20

60.88

18.20

C

69.46

80.53

83.31

11.07

2.78

D

139.71

51.98

41.62

-87.73

-10.36

E

68.24

77.04

88.02

8.80

10.98

AxB

68.72

80.00

84.58

11.28

4.58

A

0.00

25.91

46.09

25.91

20.18

B

31.11

24.93

15.97

-6.19

-8.96

C

10.46

34.59

26.96

24.14

-7.63

D

49.44

14.53

8.04

-34.91

-6.49

E

12.11

22.88

37.01

10.77

14.13

AxB

20.45

23.54

28.02

3.09

4.48

A

64.75

55.20

41.34

-9.56

-13.86

B

0.00

67.07

94.23

67.07

27.16

C

59.01

45.94

56.36

-13.07

10.42

D

90.27

37.45

33.58

-52.82

-3.87

E

56.13

54.16

51.01

-1.97

-3.14

AxB

48.27

56.46

56.56

8.19

0.10

qtot

qS

qN


Page 19 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Table 4. ANOVA of qtot for multicomponent adsorption of DBT and QN onto Ni-GAC. Sum of squares

DOF

Mean square

%

F-value

contribution qtot A

2466.05

2

1233.03

2.42

1.91

B

30876.64

2

15438.32

30.26

23.97

C

966.30

2

483.15

0.95

0.75

D

52273.20

2

26136.60

51.23

40.58

E

1768.88

2

884.44

1.73

1.37

A×B

5948.26

4

1487.07

5.83

2.31

Residual

7728.18

12

644.02

7.57

Model

94299.34

14

45662.60

92.43

Cor. total

102027.52

26

46306.62

100.00

70.90

QQN A

2493.98

2

1246.99

3.22

1.31

B

42345.66

2

21172.83

54.59

22.20

C

859.01

2

429.51

1.11

0.45

D

18052.50

2

9026.25

51.23

9.46

E

119.86

2

59.93

0.15

0.06

A×B

2249.31

4

562.33

2.90

0.59

Residual

11447.08

12

953.92

14.76

Model

66120.32

14

32497.83

85.24

Cor. total

77567.40

26

33451.76

100.00

34.07

QDBT A

9609.64

2

4804.82

31.88

17.48

B

1043.75

2

521.87

3.46

1.90

C

2739.39

2

1369.70

9.09

4.98

D

8925.57

2

4462.79

29.61

16.24

E

2807.52

2

1403.76

9.31

5.11

A×B

1723.51

4

430.88

5.72

1.57

Residual

3297.89

12

274.82

10.94

Model

26849.39

14

12993.82

89.06

Cor. total

30147.28

26

13268.64

100.00


47.28

Energy & Fuels

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Page 20 of 29

Table 5. Comparison of individual and total adsorption uptakes and yields found at different DBT concentrations with increasing concentration of QN onto Ni-GAC. Ce, QN

Ce, DBT

qe, QN

qe, DBT

AdQN%

AdDBT%

Adtot%

0.15

0.10

0.17

0.07

95.64

93.23

94.91

0.72

0.30

0.32

0.06

89.83

80.48

88.15

5.63

0.48

0.61

0.05

68.42

68.82

68.45

19.48

0.74

0.81

0.04

45.45

52.33

45.74

34.39

0.93

0.95

0.03

35.8

40.25

35.93

1.03

0.83

0.12

0.11

71.04

73.41

72.15

2.47

1.10

0.23

0.10

65.33

64.67

65.13

7.03

1.33

0.54

0.08

60.61

57.38

60.13

21.40

1.52

0.71

0.08

40.06

51.22

40.96

37.46

2.05

0.80

0.05

30.06

34.25

30.29

1.18

2.73

0.11

0.25

66.78

65

65.56

3.18

3.46

0.19

0.21

55.45

55.67

55.56

8.71

3.87

0.45

0.19

51.21

50.45

50.98

22.50

4.03

0.66

0.18

36.98

48.38

39.03

39.71

5.27

0.69

0.12

25.86

32.46

26.70

1.41

6.20

0.10

0.47

60.26

60.28

60.28

3.21

8.55

0.19

0.35

55.03

45.22

48.30

8.84

10.09

0.45

0.27

50.48

35.4

43.44

22.6857

11.4781

0.6514

0.20

36.48

26.54

33.45

40.8910

12.0484

0.6340

0.17

23.67

22.89

23.49

1.4317

12.3468

0.1069

0.55

59.91

47.32

48.98

3.6257

13.3992

0.1758

0.50

49.24

42.83

44.33

10.5910

15.5976

0.3633

0.39

40.69

33.45

36.58

24.1071

17.7398

0.5803

0.28

32.5

24.31

29.25

42.6321

19.8468

0.5469

0.17

20.42

15.32

18.87


Page 21 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Table 6. Multi-component isotherm parameter values for the simultaneous adsorption of DBT and QN by Ni-GAC. Non- modified Langmuir model SSE

2.2608 Modified Langmuir

Extended Langmuir Model

Model Adsorbate

η L,i

Ki

qmax

N

2.4320

0.2736

0.9004

S

3.4124

0.2017

SSE

0.5476

0.1764 Extended Freundlich Model

Adsorbate

X

Y

Z

N

0

0.3119

0.4717

S

0

0.7197

0.5373

SSE

0.1631 Modified R-P Model

Adsorbate

ηRP,i

N

0.001

S

8.3008

SSE

0.542712378


Energy & Fuels

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Blank-GAC

Page 22 of 29

Ni-GAC

Figure 1. Scanning electron micrographs of blank- and Ni-GAC.


Page 23 of 29

Quantity Adsorbed (cm3/g STP)

200 190 180 170 1(A) 1(D) 2(A) 2(D)

160 150 140 0

0.2 0.4 0.6 0.8 Relative Pressure (P/Po)

1

(a)

0.012 Cumulative Pore Volume (cm3/g.Å)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Ni-GAC Ni-GAC after adsorption

0.01 0.008 0.006 0.004 0.002 0 0

20 40 60 Pore Diameter (Å)

80

(b) Figure 2. (a) Liquid nitrogen adsorption (A)-desorption (D) isotherms of the Ni-GACs. 1(A) and 1(D) denote adsorption and desorption curves for Ni-GAC; 2(A) and 2(D) denote adsorption and desorption curves for Ni-GAC after adsorption. (b) Variation of cumulative pore volume of the Ni-GACs.


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 3. FTIR of Ni-GAC before and after adsorption.


Page 24 of 29

Page 25 of 29

80

q (mmol/g)

70

q-tot (mmol/g)

60

q-N (mmol/g)

q-S (mmol/g)

50 40 30 20 10 11

6

1

25

15

5

45

30

15

35.71

17.86

0.00

15.63

7.81

0 0.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Time (h) Adsorbent dose (g/l) Figure 4. Effect of process parameters on qtot for multicomponent adsorption of DBT and QN onto Ni-GAC. Co, DBT

o Co, QN (mmol/l) Temp. ( C)


Energy & Fuels

40

25 B1 (q-S)

35

B1 (q-N)

B2 (q-S)

30

B2 (q-N)

qQN (mmol/g)

20

qDBT (mmol/g)

B3 (q-S)

15 10 5

B3 (q-N)

25 20 15 10 5 0

0 0

1

2

3

0

4

1

2

3

4

Interaction between A and B

Interaction between A and B (a)

(b) 20 18 16 14

qtot (mg/g)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 29

12 10

B1 (qtot)

8 B2 (qtot)

6 4

B3 (qtot)

2 0 0

1

2

3

4

Interaction between A and B (c) Figure 5. The interaction between A and B parameters at 3 levels on (a) qDBT (b) qQN and (c) qtot for multicomponent adsorption of DBT and QN onto Ni-GAC.


Page 27 of 29

qe,calc (DBT) (mmol/g)

2

1.5

1

Non-Modified Langmuir Modified Langmuir Extended Langmuir Extended Freundlich R-P Modified R-P 10% Error 20% Error 30% Error

0.5

0 0

0.5

1 1.5 qe,exp (DBT) (mmol/g)

2

(a) 1.5

Non-Modified Langmuir Modified Langmuir Extended Langmuir Extended Freundlich R-P Modified R-P 10% Error 20% Error 30% Error

1.2 qe,calc (QN) (mmol/g)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

0.9

0.6

0.3

0 0

0.3

0.6

0.9

1.2

1.5

qe,exp (QN) (mmol/g)

(b) Figure 6. Comparison of actual and theoretical equilibrium adsorption values of DBT and QN in a binary mixture of DBT and QN.


IAST 0.1

0.2

0.3

0.4

0.5

Page 28 of 29

qQN Calculated

1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.0

1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

0.6

IAST 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

qDBT Experimental

qQN Experimental

(a)

(b)

0.8

qQN Calculated

0.7

qDBT Calculated

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

qDBT Calculated

Energy & Fuels

0.6 0.5 0.4 0.3 0.2

RAST

0.1 0.0

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

RAST 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

qQN Experimental

qDBT Experimental (c)

(d)

Figure 7. Experimental and calculated qe values for DBT and QN compounds in the binary mixture by IAST and RAST, (a) DBT-IAST, (b) QN-IAST, (c) DBT-RAST, (d) QN-RAST.


Page 29 of 29

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Energy & Fuels

TOC 608x331mm (96 x 96 DPI)

ACS Paragon Plus Environment

Simultaneous Desulfurization and Denitrogenation of Liquid Fuel by

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