Study and optimization of adsorption of sulfur compounds present in fuel

Abstract

The present work investigates the performance of fly ash, coal dust, bentonite, laterite and sodium zeolite as adsorbents for desulfurization of synthetic fuel by batch adsorption experiments at 50 °C and under atmospheric pressure. A series of synthetic fuel samples containing known concentrations of thiophenol, benzothiophene and di-benzothiophene, as singular and multi component mixtures in isooctane, were prepared. The performance of coal dust was found to be the best among all the adsorbents mentioned, in removal of both single and multi sulfur compounds from synthetic fuel. The adsorbents were characterized by BET surface area analysis, XRF, XRD, FTIR, SEM and NH₃-TPD. The effects of parameters such as stirrer speed, adsorbent particle size, temperature, adsorbent amount, initial sulfur concentration and contact time between adsorbate and adsorbent were investigated for desulfurization of synthetic fuel using coal dust as adsorbent. The sulfur adsorption equilibrium with this adsorbent was fitted to Langmuir, Freundlich, BET, Halsey and Temkin isotherms. Finally Response Surface Methodology (RSM) was applied for optimizing the adsorption process parameters. The four factor Box–Behnken design (BBD) was performed, aimed at developing second order polynomial models at the optimum conditions. The objective was to find out how the output, sulfur adsorption per gram of adsorbent (q_t), is related to the inputs, initial sulfur concentration, adsorbent amount, time and temperature, in order to get a clear picture for further research.

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Introduction

In the last twenty five years, the energy usage of the world has increased to an average of 404 quadrillion BTUs per annum, where more than 40% of this energy comes from petroleum.^1,2 Gasoline, jet fuel and diesel are the three major types of transportation fuels which contain significant amounts of sulfur and combustion of these fuels results in the production of sulfur-oxide gases which cause severe harm to human health and the environment.^3,4 In India, the existing two tier system for liquid fuel quality called Bharat Stage specification introduced the BS(IV) grade diesel in April 2010, which required a reduction of organic sulfur content from 350 ppm to 50 ppm.⁵ The conventional refinery-practiced desulfurization technology is hydrodesulfurization (HDS), which is an intensive process, and hence requires a high investment cost. Moreover, this process significantly reduces octane number by saturating alkenes and arenes by hydrogen used at high temperature and pressure.⁶ Therefore, there is an urgent need to find out new and alternative pathways for desulfurization which would be cost effective, efficient and can meet the environmental regulations. Adsorptive desulfurization is one of the alternatives to HDS used for production of ultra clean fuel. Adsorption is a mass transfer process where the molecules in free phase become bound to a surface by intermolecular forces.⁷ This process would be accomplished relatively at lower temperature and pressure. Several research works have been done on adsorptive desulfurization using various adsorbents. Shi et al.⁸ studied the adsorptive desulfurization of dibenzothiophene with 95% removal by using mesoporous carbon. Adekanmi et al.⁷ reported the adsorption efficiency of manganese-di-oxide and zinc-di-oxide on desulfurization of crude oil, where manganese dioxide showed good result compared to zinc dioxide with 0.816 g of sulfur adsorption per gram of adsorbent. Das Gupta et al.⁵ showed the adsorption efficiency of nickel based adsorbents (NiMCM-41) on desulfurization of diesel oil by bringing down the sulfur concentration from 450 ppm to 50 ppm. Adsorptive desulfurization of kerosene and diesel oil by using zinc impregnated montmorillonite clay as adsorbent has been reported which removed 76 and 77% sulfur from kerosene and diesel oil respectively.⁹ The adsorption efficiency of nano copper oxide on diesel fuel was reported to be 70% by Khodadadi et al.¹⁰ Kumar et al.¹¹ studied the adsorptive desulfurization of di-benzothiophene in isooctane phase by using zirconia based adsorbents with 53 mg g⁻¹ to 60 mg g⁻¹ sulfur adsorption.

In the present study, the performance of fly ash, coal dust, bentonite, laterite and sodium zeolite as adsorbents for desulfurization of synthetic fuel by batch process has been studied. Table 1 showed a comparative study of efficiency of our adsorbents and adsorbents which were found in literature. The effect of process variables such as time, temperature, initial sulfur concentration, stirrer speed, adsorbent amount and adsorbent particle size on the efficiency of adsorptive desulfurization has been studied with the best adsorbent. Finally Langmuir, Freundlich, BET, Halsey and Temkin adsorption isotherm models were fitted with adsorption equilibrium data and four factor BBD (Box–Behnken design) for optimizing the adsorption process parameters was implemented.

Table 1 Comparison of efficiency of some adsorbents from literature and present ones

Ref no.	Model fuel	Adsorbent	Adsorption efficiency
1	DBT in n-heptane	Magnetite nanoparticle loaded bentonite	95 mg g^−1, sulfur adsorbed
4	JP-8 fuel	Ag-mesoporous silica nanoparticle	32.6 mg g^−1 sulfur adsorbed
11	DBT in isooctane	Zirconia based adsorbent	55 mg g^−1 sulfur adsorbed
12	(a) Thiophene in n-hexane	Zeolites from fly ash	(a) 67.8% sulfur removal
	(b) BT in n-hexane		(b) 94.2% sulfur removal
13	DBT in n-hexane	Activated alumina	16 mg g^−1 sulfur adsorbed
Our research adsorbate	(a) Thiopheneol, BT & DBT in isooctane	Untreated coal dust	(a) 84.14 mg sulfur per g or 64% sulfur removal
	(b) Gasoline range fuel		(b) 82% sulfur removal

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Materials and method

Materials

Bentonite powder and thiophenol were purchased from Loba Chemie Pvt Ltd, Mumbai, India. Benzothiophene was procured from Himedia Lab. Pvt Ltd, Mumbai, India. Dibenzothiophene was purchased from Merck Schuchardt OHG, Germany. Fly ash was obtained from Kolaghat thermal power plant, India. Laterite soil was taken from Midnapur District, West Bengal, India. Sodium zeolite was purchased from SRL Pvt Ltd, Kolkata, India. Coal was obtained from Jharkhand Coal Suppliers, India, which was made dust of the required size in our laboratory.

Model fuel preparation

Five types of model fuels were prepared by adding sulfur compounds such as, thiophenol, benzothiophene and dibenzothiophene in isooctane at different concentrations. The compositions of model fuels are as follows; model fuel 1 [MF-1]: thiophenol (300 ppm) in iso-octane, model fuel 2 [MF-2]: benzothiophene (300 ppm) in iso-octane, model fuel 3 [MF-3]: dibenzothiophene (300 ppm) in iso-octane, model fuel 4 [MF-4]: mixture of thiophenol (300 ppm), benzothiophene (300 ppm) and di-benzothiophene (300 ppm) in iso-octane with total sulfur concentration of 900 ppm, model fuel 5 [MF-5]: mixture of thiophenol (300 ppm), benzothiophene (300 ppm) and di-benzothiophene (300 ppm) with total sulfur content of 900 ppm added with two aromatic compounds (benzene and toluene) with total aromatic content of 600 ppm.

Batch adsorption experiment

A typical adsorption experiment was conducted using 40 mL model fuel and 0.27 g of adsorbent, taken in a three necked glass flask housed in a water bath whose temperature was maintained at 50 °C with a digital temperature controller cum indicator with an accuracy of ±1 °C. The experiment was conducted for 2 h under stirring at 1000 rpm and samples were withdrawn at every 10 min intervals. The samples were filtered to remove any trace of adsorbent particle before they were analyzed in HPLC [Perkin Elmer, Series 200] with reversed phase Agilent SB C-18 column and a Perkin Elmer Series 200 UV/VIS detector set at 254 nm. The mobile phase used was 90% methanol in water.

Adsorbent characterization

Adsorbents were characterised by BET surface area analyser, (Model: AS1 MP/Chemi-LP, USA) instrument, X-ray fluorescence spectroscope (XRF), (Model Panalytical, Axios, Netherlands), X-ray diffraction (XRD), (Model Panalytical 3050/60) with beryllium filtered Cu Kα (1.5418 A), operating at 40 kV and 30 mA, scanning electron micrograph (SEM), (Model Zeol and Zeiss with Oxford EDS detector). NH₃-TPD (Model Chembet-3000) and Fourier Transform Infrared Spectroscopy (FTIR), (Model Perkin Elmer spectrum 100) in the spectral range of 400 to 4000 cm⁻¹.

The sulfur adsorption per unit mass of the adsorbent (mg g⁻¹) was calculated using eqn (1) and (2), as,

Results and discussion

XRD analysis of adsorbents

Powder X-ray diffraction patterns of coal dust, bentonite, fly ash, laterite soil and sodium zeolite are shown in Fig. 1. In coal dust the characteristic peaks of silicon-di-oxide (2θ = 12.469°) can be assigned as (220) with the standard pattern (JCPDS no. 89-1421), aluminium oxide (2θ = 31.052°, 50.237°) can be assigned as (400), (331) with the standard pattern (JCPDS nos 79-1559 & 79-1558), sulphur-tri-oxide (2θ = 24.969°) can be assigned as (121) with the standard pattern (JCPDS no. 72-1664) and calcium oxide (2θ = 26.740°) can be assigned with the standard pattern (JCPDS no. 17-0912). In bentonite the characteristic peaks of sodium oxide (2θ = 19.855°) which can be assigned as (001) with the standard pattern (JCPDS no. 74-0111), silicon-di-oxide (2θ = 26.674°, 50.231°, 62.361°) is as (321), (063), (1310) with the standard pattern (JCPDS nos 89-1813, 89-0735 & 89-1668). In fly ash the characteristic peaks of silicon-di-oxide (2θ = 24.07°, 29.31°, 31.27°, 44.454°) which can be assigned as (314), (132), (015), (201) with the standard pattern (JCPDS nos 88-1461, 89-0735 & 89-3433), aluminium oxide (2θ = 39.444°) can be assigned as (206) with the standard pattern (JCPDS no. 88-1609), ferrous oxide (2θ = 21.059°) can be assigned as (021) with the standard pattern (JCPDS no. 89-6466), ferric oxide (2θ = 48.465°) can be assigned as (334) with the standard pattern (JCPDS no. 89-5894) and magnesium oxide (2θ = 45.89°) can be assigned as (211) with the standard pattern (JCPDS no. 76-1363). In laterite soil the characteristic peaks of ferric or iron oxide (2θ = 26.674°, 50.170°) which can be assigned as (022), (3110) with the standard pattern (JCPDS nos 89-6466 & 89-5894), silicon-di-oxide (2θ = 21.359°) can be assigned as (006) with the standard pattern (JCPDS no. 89-1349). In zeolite the characteristic peaks of silicon-di-oxide (2θ = 21.554°, 23.893°, 27.002°, 29.809°, 34.054°) can be assigned as (111), (222), (402), (314), (512) with the standard pattern (JCPDS nos 89-3435, 88-1155, 89-1350, 89-5416 & 89-1349), aluminium oxide (2θ = 52.503°) can be assigned as (024) with the standard pattern (JCPDS no. 75-1862), sodium oxide (2θ = 69.081°) can be assigned with the standard pattern (JCPDS no. 15-0068).

Fig. 1 XRD peak of all adsorbent. ferrous oxide (Fe₂O₃); sulfur-tri-oxide (SO₃); aluminium oxide (Al₂O₃); sodium oxide (Na₂O); calcium oxide (CaO); ferric oxide (Fe₃O₄); magnesium oxide (MgO); * silicon-di-oxide (SiO₂).

Scanning electron micrograph analysis

Fig. 2 shows the SEM image of coal dust sample before and after adsorption.

Fig. 2 SEM images of coal before and after adsorption.

FTIR analysis of adsorbents

Fig. 3 shows the FTIR analysis of coal dust sample before and after adsorption.

Fig. 3 FTIR of fresh and used coal.

BET surface area analysis

BET surface area of all the adsorbents were determined by BET analysis and tabulated in Table 2. It has been observed that coal dust possesses largest surface area (149.4 m² g⁻¹) compared to the other adsorbents.

Table 2 BET surface area of adsorbents

Adsorbent	BET surface area (m^2 g^−1)
Coal dust	149.40
Laterite	60.00
Bentonite	33.06
Sodium zeolite	25.24
Fly ash	19.25

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X-ray fluorescence analysis

XRF of the samples tabulated in Table 3, shows different compositional analysis of all the adsorbents.

Table 3 Elemental analysis (wt%) of all adsorbents using XRF

Adsorbents	SiO2	Al2O3	SO3	CaO	MgO	Na2O	Fe2O3
Coal dust	42.43	22.169	9.571	5.099	0	0	13.491
Bentonite	53.28	18.27	0	0	3.027	7.311	12.975
Fly ash	25.85	10.68	0	47.34	7.604	0	4.074
Laterite soil	30.551	17.28	0	0	0	0	49.37
Sodium zeolite	41.58	32.96	0	0	0	23.67	0

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NH₃-temperature programmed desorption analysis

NH₃-TPD of all the adsorbent samples was done to determine their acidic nature. It is observed that coal dust possesses a significant acidic nature with acidity 8.22 mmol g⁻¹. Although fly ash shows better acidity (10.65 mmol g⁻¹), yet because of its very low surface area, its acidic nature cannot prominently take part in attracting large amount of adsorbate compared to coal dust. Table 4 and Fig. 4 show the acidity of coal dust before adsorption and after adsorption with MF-4 and it is observed that there is a drastic decrease of acidity after adsorption (8.22 to 6.22 mmol g⁻¹).

Table 4 NH₃-TPD of coal before and after adsorption

Adsorbent	Acidity (mmol g^−1)
Coal before adsorption	8.22
Coal after adsorption	6.22

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Fig. 4 NH₃-TPD curve of coal before and after adsorption.

Adsorptive desulfurization of MF-1, MF-2, MF-3, MF-4 and MF-5 using different adsorbents

Fig. 5A–E showed the adsorptive desulfurization of MF-1, 2, 3, 4 and 5 by using coal dust, bentonite, fly ash, laterite and zeolite. The highest sulfur adsorption per gram adsorbent (mg g⁻¹) were 44.4, 29.3, 18.66, 85 and 76.3 for MF-1, 2, 3, 4 and 5 respectively using coal dust, 43.92, 25.9, 17.9, 84.1 and 74 for MF-1, 2, 3, 4 and 5 respectively using bentonite, 43.86, 21.8, 2.66, 64.45 and 59.2 for MF-1, 2, 3, 4 and 5 respectively using fly ash, 43.43, 18.7, 1.93, 58.6 and 47.7 for MF-1, 2, 3, 4 and 5 respectively using laterite and 43.29, 18.8, 1.91, 59.2 and 48.8 for MF 1, 2, 3, 4 and 5 respectively using sodium zeolite.

Fig. 5 (A) Adsorptive desulfurization of MF-1 (thiophenol: 300 ppm; adsorbent particle size: 0.052 mm; temperature: 50 °C; adsorbent amount: 6.75 g L⁻¹ of model fuel, stirrer speed: 1000 rpm). (B) Adsorptive desulfurization of MF-2 (benzothiophene: 300 ppm; adsorbent particle size: 0.052 mm; temperature: 50 °C; adsorbent amount: 6.75 g L⁻¹ of model fuel, stirrer speed: 1000 rpm). (C) Adsorptive desulfurization of MF-3 (dibenzothiophene: 300 ppm; adsorbent particle size: 0.052 mm; temperature: 50 °C; adsorbent amount: 6.75 g L⁻¹ of model fuel, stirrer speed: 1000 rpm). (D) Adsorptive desulfurization of MF-4 (sulfur: 900 ppm; adsorbent particle size: 0.052 mm; temperature: 50 °C; adsorbent amount: 6.75 g L⁻¹ of model fuel, stirrer speed: 1000 rpm). (E) Adsorptive desulfurization of MF-5 (sulfur: 900 ppm; aromatic: 600 ppm; adsorbent particle size: 0.052 mm; temperature: 50 °C; adsorbent amount: 6.75 g L⁻¹ of model fuel, stirrer speed: 1000 rpm).

Table 5 showed the comparative studies of all adsorbents on MF-4 and MF-5 to find out the influence of aromatic compounds present in the model fuel in MF-5. It has been observed that, presence of aromatic compounds decreases the overall removal of sulfur compounds in MF-5 from 64 to 57% for coal dust, 63 to 55% for bentonite, 48 to 44.4% for fly ash, 44 to 35.7% for laterite and 44.4 to 36.6% for zeolite compared to MF-4. This may be due to the competition between sulfur compounds and aromatic compounds to avail the active sites of adsorbents.

Table 5 Efficiency of all adsorbents on model fuel 5 (MF-5)^a

Adsorbent	MF-4		MF-5
	qt (mg g^−1)	R (%)	qt (mg g^−1)	R (%)
a qt (mg g^−1) sulfur adsorbed per gram of adsorbent. R (%) is percentage of adsorption of sulfur compounds.
Coal	85	64	76.3	57
Bentonite	84.1	63	74	55
Fly ash	64.45	48	59.2	44.4
Laterite soil	58.6	44	47.7	35.7
Zeolite	59.2	44.4	48.8	36.6

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The adsorption result shows that coal dust is the best adsorbent and the effect of different process parameters of adsorptive desulfurization has been studied using coal dust. In this work, MF-4 was taken as a fuel sample of choice. The reason for best performance of coal dust as adsorbent, compared to other four adsorbents is its highest surface area which causes larger amount of mass transfer between adsorbate and adsorbent.

Parametric study on adsorptive desulfurization of model fuel 4

MF-4 was abruptly chosen as a sample of choice for parametric study with coal dust as adsorbent.

Effect of stirrer speed variation

Fig. 6 shows the effect of stirrer speed on adsorption efficiency (mg g⁻¹) of coal dust. It is observed that increase in stirrer speed from 400 rpm to 1000 rpm increases the adsorption efficiency from 53.87 to 84.29. This may be due to increase in the mass transfer between adsorbate and adsorbent. But when the stirrer speed was increased from 1000 rpm to 1200 rpm the change in adsorption efficiency was not significant, which may be due to the reason that resistance to mass transfer has no prominent influence on adsorption at that range of speed.¹⁴ Hence, all the subsequent parametric studies were done at 1000 rpm.

Fig. 6 Stirrer speed variation (total sulfur in model fuel: 900 ppm; adsorbent particle size: 0.052 mm; temperature: 50 °C; adsorbent amount: 6.75 g L⁻¹ of model fuel).

Effect of adsorbent particle size variation

Fig. 7 shows the adsorbent particle size effect, the size ranging from 0.125 mm to 0.052 mm on its adsorption efficiency (mg g⁻¹). It has been observed from the figure that, as particle size decreases from 0.125 to 0.052 mm, the efficiency of adsorption increases. Due to the decrease in particle size, the surface area is expected to increase, which in turn may increase the efficiency from 58.4 to 86.34.¹⁴ Moreover, when the size of adsorbent is reduced to less than 0.052 mm, the adsorption capacity is not significantly increased. This may be due to the fact that, the increase in surface area after that size was not significant; hence the increase in adsorption capacity was not prominent. The rest of the experiments were performed with 0.052 mm size as internal mass transfer resistance may be negligible at this stage.

Fig. 7 Adsorbent particle size variation (total sulfur in model fuel: 900 ppm; temperature: 50 °C; adsorbent amount: 6.75 g L⁻¹ of model fuel; stirrer speed: 1000 rpm).

Influence of initial sulfur concentration

The adsorption experiments were performed with different initial sulfur concentration of the model fuel, ranging from 300 to 1800 ppm and Fig. 8 shows the effect of initial sulfur concentration on the adsorption capacity of coal dust. As the initial concentration of sulfur increases, intake capacity of adsorbent or adsorption rate also increases at the same time. Increase in initial concentration leads to increase in driving force (concentration gradient) for mass transfer. From the figure it is observed that, sulfur removal on the adsorption increases with time, reaches its maximum and then forms a plateau with a little decreasing trend for all the concentration. The maximum removal of sulfur reaches faster for higher initial concentration of sulfur containing model fuel compared to the lower ones, and after that, no improvement in adsorption with time was observed. The maximum removal of sulfur for initial concentration of 1500 and 1800 ppm obtained are 105 mg g⁻¹ at 40 min and 120 mg g⁻¹ at 30 min respectively. This may be due to saturation of active sites of coal dust or pore blocking/active site blocking at higher sulfur concentration.^1,14

Fig. 8 Initial sulfur concentration variations (stirrer speed: 1000 rpm; adsorbent particle size: 0.052 mm; temperature: 50 °C; adsorbent amount: 6.75 g L⁻¹ of model fuel).

Effect of operating temperature

Temperature for adsorption was varied from 30 to 70 °C and the effect of temperature on adsorption (mg g⁻¹) is shown in Fig. 9. It has been observed from the figure that, the adsorption of sulfur increases with increase in temperature from 30 to 50 °C in a regular manner, after that it decreases as the temperature increases from 50 to 70 °C. This nature may be explained by the logic that, with increase in temperature, pore size of adsorbent as well as diffusion rate may be increased up to 50 °C but desorption of sulfur compounds from adsorbent surface may take place at temperature higher than 50 °C then the overall adsorption decreases.^1,9,10

Fig. 9 Adsorption temperature variation (stirrer speed: 1000 rpm; adsorbent particle size: 0.052 mm; sulfur concentration in model fuel: 900 ppm; adsorbent amount: 6.75 g L⁻¹ of model fuel).

Effect of variation of adsorbent amount

Fig. 10 shows the effect of adsorbent (coal dust) quantity on adsorbent capacity. It is observed from the figure that the adsorption capacity increases from 62 to 92 as the amount of adsorbent increases from 2 to 10 g L⁻¹. This may be due to the availability of more adsorbent which provides more active sites for adsorption.^1,9,10

Fig. 10 Adsorbent amount variation (stirrer speed: 1000 rpm; adsorbent particle size: 0.052 mm; sulfur concentration in model fuel: 900 ppm; temperature: 50 °C).

Reusability of adsorbent

Fig. 11 shows the study of reusability of all adsorbents on sulfur adsorption. It is observed from the figure that the reusability efficiency of coal dust is quite high compared to the other four adsorbents. After 3^rd cycle, the sulfur adsorption decreased only 5% for coal dust, whereas for bentonite, fly ash, laterite and zeolite the sulfur adsorption decreased by 15%, 30%, 32% and 37% respectively.

Fig. 11 Reusability of adsorbents (sulfur: 900 ppm; adsorbent particle size: 0.052 mm; temperature: 50 °C; adsorbent amount: 6.75 g L⁻¹ of model fuel, stirrer speed: 1000 rpm).

Adsorption equilibrium isotherm

From different adsorption equilibrium isotherms, Langmuir, Freundlich, BET, Halsey and Temkin have been chosen to fit the experimental data and the best fitted one was determined. The experimental condition was constant for all experiments (stirrer speed: 1000 rpm; temperature: 50 °C; time: 2 h; adsorbent particle size: 0.052 mm; adsorbent amount: 6.75 g L⁻¹ of adsorbate; sulfur conc: 900 ppm).

Langmuir isotherm

The Langmuir¹⁵ is one of the most extensively used isotherm models in all adsorption processes. It assumes the formation of monolayer adsorbate on the outer surface of the adsorbent and all sorption sites have equal affinity towards the adsorbate.¹⁶ The theoretical equation of this isotherm is written in linear form as,

Fig. 12 shows the straight line plot of C_e/q_e vs. C_e. From slope and intercept of the plot Q_m and K are calculated.

Fig. 12 Langmuir isotherm.

Freundlich isotherm

Freundlich isotherm is based on the equilibrium sorption on heterogeneous surfaces involving large adsorbate concentration range.¹⁷ The main assumption of this isotherm is that the adsorption sites are distributed exponentially with respect to heat of adsorption.¹⁶ This isotherm is expressed by the following equation.

q_e = K_FC_e^1/n

This can be linearised as,

Which gives a linear plot with a slope of 1/n and an intercept of log(K_F). This plot with the present experimental data is shown in Fig. 13.

Fig. 13 Freundlich isotherm.

BET isotherm

The BET model is an useful model for multi layer adsorption.¹⁸ Here, the assumption is that the solid surface influences the adsorption for very first layer of adsorbed material.¹⁹ The mathematical form of this isotherm is as follows,

The plot of C_e/q_e (C_s − C_e) vs. C_e/C_s is shown in Fig. 14 and from the slope and intercept the values of C_BET and q_s are calculated.

Fig. 14 BET isotherm.

Halsey isotherm

The Halsey model²⁰ is applicable for multilayer adsorption. This model involves the heteroporous nature of the adsorbent which makes its difference from Freundlich isotherm.²¹ The linear form of this isotherm is as follows

A plot of ln q_s vs. ln C_e with experimental data is shown in Fig. 15 and the values of K_Ha and n_Ha from intercept and slope were determined.

Fig. 15 Halsey isotherm.

Temkin isotherm

This isotherm contains a factor that comes from adsorbent–adsorbate interaction and the heat of sorption of all molecules is assumed to decrease linearly.²² Its derivation is characterized by a uniform distribution of binding energies.¹⁸ The general equation of this isotherm is as follows,

Fig. 16 shows a plot of q_e vs. ln C_e and the values of B and A_T were obtained from slope and intercept of the straight line.

Fig. 16 Temkin isotherm.

The values of all adsorption equilibrium isotherm constants for different models are showed in Table 6. It has been observed that the Langmuir adsorption isotherm is the best fitted isotherm model with highest R² value. This reveals that the adsorption is physically in nature.

Table 6 Values of adsorption equilibrium isotherm constants

Isotherm model	Parameters	Value at 50 °C	R^2
(1) Langmuir	K	0.00183	0.991
	Qm	200
(2) Freundlich	KF	1.945	0.985
	1/n	1.67
(3) BET	qs	16.95	0.932
	CBET	9.83
(4) Halsey	KHa	0.321	0.957
	nHa	−1.66
(5) Temkin	AT	0.0198	0.959
	B	38.94

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Thermodynamic analysis

The temperature effect on desulfurization of model fuel by adsorption using coal dust is explained further by thermodynamic parameters. Thermodynamic parameters i.e. change in free energy (ΔG), change in enthalpy (ΔH) and change in entropy (ΔS) were investigated by the following equations,^23,24

ΔG = −RT ln K_D (8)

ΔG = ΔH − TΔS (9)

and,

Fig. 17 shows the plot of ln K_D against 1/T and the values of ΔH and ΔS were found from slope and intercept of this linear plot. This is van't Hoff plot.

Fig. 17 van't Hoff plot.

From the figure, ΔH and ΔS values for adsorption obtained are 4.67 kJ mol⁻¹ and 0.0163 kJ mol⁻¹ respectively. The low value of ΔH confirms that this adsorption is physical adsorption.²⁵

(−ΔG) values at 10, 20, 30, 40 and 50 °C are 0.05, 0.1, 0.26, 0.43 and 0.59 kJ mol⁻¹ respectively and the negative values prove the spontaneity of the system.²⁶ The ΔS value is positive which indicates that the sulfur was randomly adsorbed on the surface of adsorbent, while positive value of ΔH confirms that the adsorption process was endothermic.^1,27

Mechanism of adsorption

The coal dust used in the present study contains some degree of surface chemical heterogeneity which has been originated from the presence of heteroatoms in the sample. The acidity obtained from NH₃-TPD analysis of all adsorbents shows that the acidity of coal dust is reasonably high (8.22 mmol g⁻¹), which is the second highest acidity among all the adsorbents. Although fly ash is showing higher acidity than coal dust, but owing to very low surface area (19 m² g⁻¹), fly ash cannot compete with coal dust having high surface area (149.4 m² g⁻¹). The high acidic nature of coal dust may be originated from surface acidic oxygen functional groups which attracts basic thiophenic ring²⁸ and causes a reduction in acidity as revealed from Table 3 and Fig. 4. The thiophenic ring contains two lone pairs of electrons and one of those pairs is utilized to retain the aromaticity of the thiophenic ring. The other one is free and increases the interaction between adsorbent acid site and the sulphur compounds.

Comparing the FTIR spectra of coal dust sample before and after adsorption (Fig. 3), it is observed that there is no change in functional group of the coal dust sample after adsorption and also no peak shift is located. Hence, no evidence of formation of any new bond with organic sulfur compound with coal dust sample is obtained and so it can be said that the sulfur compound was attached on coal surface with weak electrostatic or van der Waals force and it was a physical adsorption. No change in surface morphology in SEM pictures of coal dust sample after adsorption compared to the sample before adsorption proves the same.^29,30

XRD analysis of coal dust samples before and after adsorption also proves that the nature of adsorption is physical. All these proofs confirm that the governing isotherm model Langmuir isotherm.

Optimization of the process parameters of sulfur adsorption by using Box–Behnken model (BBD)

Response surface methodology (RSM) is a powerful statistical-based technique that can be used to predict the response of a system to any new condition and determine the optimum conditions.³¹ Box–Behnken Design (BBD) in RSM is an important design tool used for optimization of process parameters and consists of an equal number of factors of all possible combinations.³² It provides comprehensive conclusions and detailed information for smaller number of experiments and interactive effects of operating parameters on all responses.³³ The choice of BBD amongst other RSM design was due to its feasibility, simplicity, efficiency and its application where extreme treatment combinations are to be avoided. In addition, BBD also offers the fewer experimental runs for three factors which can be used to explore a quadratic response surfaces as well as, creating a second order polynomial model.³⁴ The experimental design for the optimization study was performed employing a four-factor BBD with input variables; initial sulfur concentration, adsorbent amount, time and temperature.

A second-order polynomial equation was fitted in the experimental data to make a relationship between the input variables and the responses. The BBD analysis was developed by using design expert software 7.0.0.

This equation represents the second order polynomial equation where y is the response variable of sulfur removal. x₁, x₂, x₃…x_i are the coded parameters and β₀, β_i, β_ii and β_ij are the interception, regression co-efficients of linear effect, squared effect and interaction effect, ε represents an error. The levels of the independent variables and their limits are presented in Tables 7 and 8.

Table 7 Experimental values and levels of variables

Internal parameter	Coded value	Actual value	Range
Initial sulfur concentration (mg L^−1)	C1	R1	300–1800
Adsorbent weight or mass (g L^−1)	C2	R2	3–10
Time (min)	C3	R3	10–120
Temperature (°C)	C4	R4	40–70

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Table 8 Output response

SL no.	Output
1	Sulfur adsorbed per gram of adsorbent (mg g^−1)

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The observed and the predicted values for sulfur adsorption process are presented in Table 9 and plotted in Fig. 18. The following quadratic model was obtained to establish a relationship between four independent process parameters and the dependent response [sulfur adsorption per gram of adsorbent].

Sulfur adsorption per gram of adsorbent (mg g⁻¹) = 79.98 + 37.72 × A + 13.32 × B + 15.90 × C − 5.33 × D − 0.42 × A × B + 5.00 × A × C − 6.75 × A × D − 0.31 × B × C + 0.75 × B × D − 0.50 × C × D − 7.30 × A² − 6.15 × B² − 15.57 × C² − 16.47 × D² (13)

Table 9 Experimental design, observed and predicted response values for BBD

Run	Coded value				Real value				Sulfur adsorbed (mg g^−1)
	C1	C2	C3	C4	R1	R2	R3	R4	Observed	Predicted
1	−1	−1	0	0	300	3	65	55	11.34	15.06
2	1	−1	0	0	1800	3	65	55	90	91.34
3	−1	1	0	0	300	10	65	55	39	42.54
4	1	1	0	0	1800	10	65	55	116	117.15
5	0	0	−1	−1	1050	6.5	10	40	35	36.87
6	0	0	1	−1	1050	6.5	120	40	70	69.67
7	0	0	−1	1	1050	6.5	10	70	22	27.21
8	0	0	1	1	1050	6.5	120	70	55	58
9	−1	0	0	−1	300	6.5	65	40	18	17.07
10	1	0	0	−1	1800	6.5	65	40	100	106.01
11	−1	0	0	1	300	6.5	65	70	25	19.90
12	1	0	0	1	1800	6.5	65	70	80	81.84
13	0	−1	−1	0	1050	3	10	55	30	28.73
14	0	1	−1	0	1050	10	10	55	54.22	55.99
15	0	−1	1	0	1050	3	120	55	62	61.14
16	0	1	1	0	1050	10	120	55	85	87.18
17	−1	0	−1	0	300	6.5	10	55	10	8.49
18	1	0	−1	0	1800	6.5	10	55	80	73.93
19	−1	0	1	0	300	6.5	120	55	30	30.28
20	1	0	1	0	1800	6.5	120	55	120	115.73
21	0	−1	0	−1	1050	3	65	40	52	50.12
22	0	1	0	−1	1050	10	65	40	80	75.26
23	0	−1	0	1	1050	3	65	70	39	37.95
24	0	1	0	1	1050	10	65	70	70	66.10
25	0	0	0	0	1050	6.5	65	55	80	79.98
26	0	0	0	0	1050	6.5	65	55	79.98	79.98
27	0	0	0	0	1050	6.5	65	55	79	79.98
28	0	0	0	0	1050	6.5	65	55	81	79.98
29	0	0	0	0	1050	6.5	65	55	79.90	79.98

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Fig. 18 Predicted vs. experimental values of sulfur adsorption.

ANOVA analysis

The second order polynomial equation was fitted satisfactorily for sulfur adsorption. The significance of any process parameter is high if F-value is high and p-value (probability value) is lesser than 0.05. The significance of any process parameter can also be analyzed using the sum of square value and its higher value implies more importance of corresponding variables.^35,36 Here the quadratic model has a high F value [103.78] and p-value is less than 0.0001, which signify the reliability of the model. Similarly the lower p-value of the independent variables indicates the higher significance.^37–39 Tables 10 and 11 show that the A, B, C, D, AC, AD, A², B², C² and D² are significant parameters and as their p-values are less than 0.05. Finally a high R² value of 0.990 with adjusted R² 0.980 indicate that the proposed polynomial model is reasonably well fitted with the data.

Table 10 Analysis of variances (ANOVA) for sulfur adsorption (mg g⁻¹)^a

Source	SS	df	MS	F value	p value	Prob > F
a A: Initial sulfur Concentration; B: Adsorbent mass; C: Time; D: Temperature; SS: Sum of squares; MS: Mean square; df: degree of freedom.
Model	25 706.15	14	1836.15	103.78	<0.0001	Significant
A	17 075.09	1	17 075.09	965.06	<0.0001
B	2130.13	1	2130.13	120.39	<0.0001
C	3033.08	1	3033.08	171.43	<0.0001
D	341.33	1	341.33	19.29	0.0006
AB	0.69	1	0.69	0.039	0.8464
AC	100	1	100	5.65	0.0322
AD	182.25	1	182.25	10.30	0.0063
BC	0.37	1	0.37	0.021	0.8868
BD	2.25	1	2.25	0.13	0.7267
CD	1	1	1	0.057	0.8155
A^2	345.87	1	345.87	19.55	0.0006
B^2	245.31	1	245.31	13.86	0.0023
C^2	1571.91	1	1571.91	88.84	<0.0001
D^2	1759.46	1	1759.46	99.44	<0.0001
Residual	247.71	14	17.69
Lack of fit	245.70	10	24.57	48.96	0.0010	Significant
Pure error	2.01	4	0.50
Corrected total	25 953.85	28

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Table 11 Estimated regression coefficient and corresponding error

Factor	Coefficient estimate	Standard error
Intercept	79.98	1.88
A	37.72	1.21
B	13.32	1.21
C	15.90	1.21
D	−5.33	1.21
AB	−0.42	2.10
AC	5.00	2.10
AD	−6.75	2.10
BC	−0.31	2.10
BD	0.75	2.10
CD	−0.50	2.10
A^2	−7.30	1.65
B^2	−6.15	1.65
C^2	−15.57	1.65
D^2	−16.47	1.65

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Table 12 represents the optimization of process parameters. After minimization of all process parameters, the highest predicted adsorption is found to be of 70.76% which is close to the observed value of 68%.

Table 12 Optimal operating conditions maximizing adsorption of sulfur

Sulfur concentration (mg L^−1)	Adsorbent amount (g L^−1)	Time (min)	Temperature (°C)	Sulfur adsorption (mg g^−1)
				Observed	Predicted
1800	3	34.33	40	68	70.76

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Table 12 represents the optimal conditions when all independent variables are minimized and conversion is maximized with desirability 0.810.

3-D plot of process parameters

Central composite design can be visualized as 3-D plots that represent the variation of the response with two parameters, keeping the other parameter constant. The resulting 3-D surface response plots shown in Fig. 19 describe sulfur adsorption as a function of surface plots of (a) adsorbent mass and initial sulfur concentration, (b) time and initial sulfur concentration, (c) temperature and initial sulfur concentration, (d) time and adsorbent mass, (e) temperature and adsorbent mass and (f) temperature and time. From 3D plots it is concluded that all process parameters have great influence on the sulfur adsorption.

Fig. 19 Surface plots of sulfur adsorption as a function of (a) adsorbent mass and initial sulfur concentration, (b) time and initial sulfur concentration, (c) temperature and initial sulfur concentration, (d) time and adsorbent mass, (e) temperature and adsorbent mass, (f) temperature and time.

Conclusion

Adsorptive desulfurization of model fuels was performed by using coal dust, bentonite, fly ash, laterite and zeolite. The best result was performed by coal dust [84.22 mg sulfur adsorbed per g of adsorbent] with a high surface area of 149.40 m² g⁻¹. There was a significant effect of process parameters such as time, temperature, initial sulfur concentration, stirrer speed, adsorbent amount and adsorbent particle size on the adsorptive desulfurization process by using best adsorbent. The Langmuir isotherm was best fitted with equilibrium isotherm data. The adsorption is physical adsorption in nature which is confirmed by van't Hoff plot. Finally, the Box–Behnken design model was proposed for optimization of the process parameters and it was observed that initial sulfur concentration, adsorbent amount, time and temperature have significant influence on sulfur adsorption process.

XRD of coal dust sample before and after adsorption has been provided in ESI.†

SEM micrograph analysis of coal dust, bentonite, fly ash, laterite and sodium zeolite has been done and the images are provided as ESI.† From those images it is clear that coal dust sample is least agglomerated compared to the other four adsorbents which in turn are responsible for best results of adsorption.

FTIR spectral analysis of coal dust, bentonite, fly ash, laterite and zeolite in the wave range of 4000–450 cm⁻¹ has been done. The spectral image and discussion part are provided as ESI.†

NH₃-TPD of all the adsorbent samples was reported in ESI.†

The proximate and ultimate analysis of coal dust sample has been provided as ESI.†

The HPLC analysis of MF-4 samples, before and after adsorption is provided as ESI.† It was showed that the peak areas of all sulfur compounds significantly decreased after adsorption compared the peak areas of sulfur compounds of feed stock. In the case of coal dust, peak area of all sulfur compounds decreased more compared the other four adsorbents.

Finally, we also checked the efficiency of adsorbents on real fuels; gasoline and light gas oil obtained from Haldia Refinery, Indian Oil Corporation, India. The adsorption efficiency of all adsorbents for desulfurization of gasoline and light gas oil are tabulated in ESI† along with the properties of both the fuels before and after adsorption. It is observed that there is no significant change in fuel properties due to adsorption rather a significant decrease in sulfur level.

Nomenclature

qt	Adsorption capacity at time t (mg g^−1)
qe	Adsorption capacity at equilibrium (mg g^−1)
qs	Amount of sulfur adsorbed per gram of adsorbent at saturation condition or theoretical isotherm saturation capacity (mg g^−1)
Ce	Residual concentration of sulfur compound in model fuel at equilibrium (mg L^−1)
Ci	Initial concentration of sulfur compound in model fuel (mg L^−1)
Ct	Residual concentration of sulfur compound in model fuel at time t (mg L^−1)
V	Volume of model fuel (L)
W	Mass of adsorbent (g)
Qm	Maximum monolayer coverage capacities (mg g^−1)
K	Langmuir isotherm constant (L mg^−1)
KF	Freundlich isotherm constant related to adsorption capacity (mg g^−1)
N	Adsorption intensity
Cs	Adsorbate monolayer saturation concentration (mg L^−1)
CBET	BET adsorption isotherm relating to the energy of surface interaction (L mg^−1)
KHa, nHa	Halsey isotherm constants (mg L^−1)
AT	Temkin isotherm equilibrium binding constant (L g^−1)
R	Universal gas constant (J mol^−1 K^−1)
T	Absolute temperature (K)
bT	Temkin isotherm constant
KD	Distribution coefficient
ΔH	Enthalpy difference (kJ mol^−1)
ΔS	Entropy difference (kJ mol^−1)

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Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c6ra16367j

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