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In this study, nickel nanoparticles (NiNPs) were synthesized and utilized for removing dispersed oil from oilfield-produced water in Sudan. The synthesis process involved using two concentration of hydrazine as a reducing agent and sodium hydroxide as solvent. Physiochemical characterizations, such as X-ray diffraction (XRD) and transmission electron microscopy (TEM), confirmed the successful preparation of NiNPs. The TEM analysis revealed an average particle size ranging from 70 to 90 nm, with a change in morphology from star-shaped to monodispersed spherical particles. The crystal structure analysis confirmed the face-centered-cubic (FCC) configuration of the NiNPs, validating their structural properties. Significantly, the NiNPs demonstrated an impressive capability to remove oil form produced water, achieving a remarkable efficiency of 98% in eliminating dispersed oil from produced water. The oil removal process followed Freundlich isotherms, as evidenced by the high value of the linear regression coefficient. Additionally, the kinetics of the oil removal process conformed well to the pseudo-second-order model, indicating a rapid reaction. This study successfully demonstrated the efficient removal of dispersed oil from produced water using nickel nanoparticles, which interacted physically with the oil particles. These findings highlight the potential of NiNPs as an effective adsorbent for treating oilfield-produced water and mitigating environmental contamination.

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  • Nickel has been synthesized by two different concentrations of hydrazine.

  • This is the first time testing metallic nickel nanoparticles for this purpose in Sudan.

dispersed oil, nickel nanoparticles, Sudan oilfields
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Produced water is a byproduct of gas and oil production from both onshore and offshore wells (Spoonamore 2011; Alkhazraji & Alatabe 2021). Produced water is a mixture of oil, organic compounds, salts, heavy metals, radioactive elements, and dissolved oxygen. However, oil is dispersed in produced water during oil and gas operations. Produced water composed of different mixtures, BTEX (benzene, toluene, ethylbenzene, and xylene), classified as low molecular weight (382.59 g/mol). Another central combination of high molecular weight, less-soluble PAHs (polyaromatic hydrocarbons), and phenols (≈760 g/mol) were found. Experimental data showed that most oils in produced water are polar; however, dispersed oil is oil suspended in the aqueous phase (Igunnu & Chen 2014).

The amount of dispersed oil depends upon the density and viscosity of the oil, the water–oil tension interface, and the droplet history (Bretz et al. 1994). The ratio of oil to produced water varies widely from zero to more than 50% (2% oil and 98% water). The volume of produced water is directly proportional to the production of gas and oil (Henderson et al. 1999). Many techniques have been applied to separate and remove dispersed oil from produced water, such as biological, adsorption, membrane filtration, ionic surfactant, hydrocyclones, chemical oxidation, and electrochemical methods (Igunnu & Chen 2014). Nevertheless, there exist various limitations associated with these techniques, including high energy consumption during pressure plumb operations, the need for chemical usage in some cases, the potential requirement for pre- and post-treatment processes, and the overall costs being dependent on the volume of produced water (Pichtel 2016).

Nanoparticles offer several advantages as a potential technique for the treatment of produced water.

Enhanced contaminant removal: Nanoparticles have a high surface area-to-volume ratio, which allows for efficient adsorption and absorption of contaminants present in produced water and nanoparticles can be engineered and functionalized to target specific contaminants or classes of pollutants. They can be modified with different coatings, surface charges, or functional groups to enhance their affinity for particular pollutants (Yap et al. 2021).

The low-cost adsorbent powder is a valuable material for removing tiny oil droplets from produced water due to its large surface area (Ko et al. 2014). The magnetite and surface-coated magnetic nanoparticles attract researchers for their applications in the separation and removal of oil from produced water (Igunnu & Chen 2014; Ko et al. 2014, 2017; Hosseini et al. 2018; Adewunmi et al. 2021).

Over recent decades, metallic nickel (Ni) and nickel oxide (NiO) nanoparticles have had various potential applications due to their remarkable properties, high ferromagnetic properties, high chemical stability, and coercive force. Therefore, they used adsorbents to purify water to remove heavy metals, anions, and dyes. They were also used as antimicrobial agents (Ravindhranath & Ramamoorty 2017; Jaji et al. 2020; Khoso et al. 2021). The magnetic nickel-ferrite nanoparticles (NFNs) are synthesized by co-precipitation and then used as adsorbents to remove heavy metals from wastewater (Khoso et al. 2021). The essential advantages and role of NiNPs as adsorbents for removing dispersed oil from produced water in oilfields (Jaji et al. 2020), top-down and bottom-up protocols prepare nickel nanoparticles (NiNPs). Therefore, NiNPs are fabricated by the bottom-up protocol by many methods such as sol-gel, spinning, chemical vapor deposition, pyrolysis, precipitation, and green methods. Trends in NiNP applications in fields such as biomedical, catalysis, supercapacitors, and dye-sensitized solar cells were explored.

Researchers recently synthesized a photocatalyst reactor for oil removal from produced water using zinc nanoparticles as a catalyst in batch and continuous systems (Alkhazraji & Alatabe 2021).

In Sudan oilfields, there is a problem with a tremendous amount of water accompanied by dispersed oil, contaminating the environment when discharged. Authors tried to find methods to remove dispersed oil from produced water. These methods include chemical and biological. To overcome this problem, the current study researchers synthesized NiNPs and then used them to treat spoiled oil in oilfield-produced water. To the best of our knowledge, this is the first time testing metallic nanoparticles for this purpose in Sudan.

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Sampling

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Produced water samples were collected from Al-Fulah, an oil field in West Kordofan state in Sudan. In recent years, there has been a significant rise in the volume of produced water in Sudan's oil fields. This increase can be attributed to the growth in oil production and the aging of existing fields. Presently, approximately 1.2 million barrels per day of water are being produced in the national company (GNPOC) oil field in Sudan (Ahmed Khadam et al. 2009).

A colorimetric method was applied to determine oil concentration using DR6000 Spectrophotometer, HACH, USA. The calibration curve was constructed using a standard solution of 1000 mg L−1 of dispersed oil and n-hexane as an extraction solvent (Federation 1999). Quality control and assurance have been applied during the experiment.

Preparation of NiNPs

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Nickel chloride hexahydrate (NiCl2·6H2O), absolute ethanol 99%, sodium hydroxide (NaOH), and hydrazine monohydrate (N2H4·H2O) were involved in the synthesis of NiNPs.

NiNPs were prepared by adopting Zhiyu methods; a mixing of nickel chloride (3 g) was dissolved in ethanol. While sodium hydroxide (NaOH) was dissolved directly in absolute ethanol, two appropriate amounts of hydrazine monohydrate (N2H4·H2O) (5.4 and 8.2 g) were added. Meanwhile, the ratio between (N2H4/Ni2+ was 10 and 15). Mixture Ni-1 and mixture Ni-2, respectively, were indexed. The temperature was controlled to 60 °C with continuous stirring for 1 h and constant pH.

Nickel has been reduced from ions to atoms and directly accumulated to the nanoscale; the reaction can be addressed as follows (Wu & Chen 2003; Li et al. 2006):
(1)
Figure 1 illustrates the color transformation of the initial mixture, progressing from turquoise (Figure 1(a)) to gray after a 2-min interval (Figure 1(b)), and finally achieving a black color (Figure 1(c)). Following the duration of 1 h, the resulting precipitate was collected using a 0.45-μm filter after washed. It was then subjected to a series of washes using absolute ethanol, deionized water, and acetone 99%. Subsequently, the black precipitate was dried at room temperature and stored for further use in a sealed container for subsequent analysis. The reaction can be represented by Equation (1).
Figure 1
Preparation of the nickel nanoparticles process. Mixture after 2 min (b) and mixture after 1 h (c).
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Preparation of the nickel nanoparticles process. Mixture after 2 min (b) and mixture after 1 h (c).

Figure 1
Preparation of the nickel nanoparticles process. Mixture after 2 min (b) and mixture after 1 h (c).
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Preparation of the nickel nanoparticles process. Mixture after 2 min (b) and mixture after 1 h (c).

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Characterization procedure

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The characterization of NiNPs was carried out using sophisticated techniques such as X-ray diffraction (XRD) and transmission electron microscope (TEM). The XRD analysis was performed by a PAN analytical 3 kW X'pert Powder XRD Multifunctional diffractometer with Cu Kα radiation source (λ = 0.154 nm). The crystalline phase and the average crystal size of metallic NiNPs were found by applying the Scherrer equation:
(2)

The particle size of NiNPs was determined by a TEM model H700H.

Batch adsorption experiment

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Batch experiments (Ni-1 and Ni-2) were carried out to determine the adsorption isotherms of dispersed oil onto the adsorbents (NiNPs) as follows: Initial concentration (Ci) of dispersed oil in produced water sample was measured, shaken gently at a constant rate, and placed in the water bath at 60 °C. A series of different weights of the NiNPs ranging from 0.01, 0.02, 0.04, and 0.05 g were added to 100 ml of produced water. After equilibrium, the concentration of each sample was measured, and the logarithm of the adsorption capacity (qe) and removal efficiency (%) at equilibrium were determined using Equations (3) and (4), respectively:
(3)
(4)

During experiments, qe can be calculated in different forms, and it represents the adsorption capacity (mg g−1), whereas Ci and Ce represent the initial and equilibrium concentrations (mg L−1) of the adsorbate, and V and W stand for solution volume (L) and mass (g) of the adsorbent, respectively.

The main benefits of isotherms are considered equilibrium and interaction mechanisms between the adsorbent and the adsorbate at static temperature. Therefore, this equilibrium can be described by a set of models from one to five parameters. Langmuir and Freundlich cover two parameters (Liu et al. 2019; Jaji et al. 2020).

Adsorption and kinetics models

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Langmuir isotherm considers some basic assumptions; the adsorption process between the adsorbent and the adsorbate is homogeneous, monolayer adsorption occurs onto the external surface, and the surface reaches a saturation point where the maximum adsorption of the surface will be achieved (Al-Ghouti & Da'ana 2020). The Langmuir equation is represented as follows:
(5)
where is the maximum adsorption capacity (mg g−1), and KL (L mg−1) is Langmuir's isotherm constant, which shows the binding affinity between dispersed oil and NiNPs. Ce and qe are the liquid phase concentrations. The linear form of the Langmuir equation is:
(6)

In the linear equation, 1/qe vs. 1/Ce were plotted as a mathematics function, therefore, KL and qmax were calculated directly from the value of slope and intercept, respectively.

While the separation factor (RL) is calculated by using Equation (7):
(7)
where RL is the dimensionless Langmuir constant which indicates the adsorption possibility either favorable (0 < RL > 1), unfavorable (RL > 1), linear (RL = 1), or irreversible (RL = 0) (Ayub et al. 2020).
Freundlich isotherm includes the following assumptions: Heterogeneous distribution of active site and energies and present multilayer surface between adsorbents and adsorbate. Freundlich's isotherm equation is:
(8)
The linear form of Freundlich's isotherm is:
(9)

Freundlich constant and Kf and n values can be obtained from the slope and intercept of plotting Log qe vs. Log Ce in the linear form of the equation.

Therefore, Kf is Freundlich's constant used to measure the adsorption capacity, and 1/n is the adsorption intensity. The value of 1/n demonstrates the adsorption process is either favorable (0.1 < 1/n < 0.5) or unfavorable (1/n > 2) (Ayawei et al. 2017).

In addition, the pseudo-first-order and pseudo-second-order have been applied to determine the time of oil adsorption on NiNPs and enhance experimental data to obtain the best results. To describe the mechanism of the kinetics of adsorption of dispersed oil on NiNPs, two models have been applied to Equations (10) and (11) in Table 1 (Li et al. 2012).

Table 1

Kinetics models

Kinetic modelsEquationPlotConstants and parametersEq. noRef
Pseudo-first-order    (10) Dehmani & Abouarnadasse (2020)  
Pseudo-second-order    (11) Dehmani & Abouarnadasse (2020)  
Kinetic modelsEquationPlotConstants and parametersEq. noRef
Pseudo-first-order    (10) Dehmani & Abouarnadasse (2020)  
Pseudo-second-order    (11) Dehmani & Abouarnadasse (2020)  
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Characterization results

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The XRD results showed that NiNPs formed by the reduction of Ni2+ with hydrazine were crystalline, which is identical to recorded results found in the literature (Wu & Chen 2003; Khoso et al. 2021). XRD data in Figure 2 showed that Ni-1 displayed three main distinct diffraction peaks recorded at 44.57°, 51.9°, and 76.4°. Therefore, their corresponding miller indices are Ni (111), Ni (200), and Ni (220), which demonstrate face-centered cubic (FCC), while XRD data of Ni-2 displays three distinct diffraction peaks at 44.52°, 51.70°, and 76.39°. The highest pick was recorded at 44.42° at 2θ with miller indices Ni (111), Ni (200), and Ni (220). This result indicates that NiNPs have been reduced from ions to nanoscale successfully.
Figure 2
XRD patterns of Ni-1 and Ni-2.
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XRD patterns of Ni-1 and Ni-2.

Figure 2
XRD patterns of Ni-1 and Ni-2.
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XRD patterns of Ni-1 and Ni-2.

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Moreover, the average particle size determined by the Debye-Scherrer equation was found 12 nm for both ratios.

The TEM monograph image and size distribution of NiNPs are shown in Figure 3(a) and 3(b) for both samples. The main particle of Ni-1 indicates a fine star structure with a mean diameter of 85 nm which is attributed to plans FCC NiNPs Ni (111) plan. Nevertheless, in low concentration Ni-2, the morphology changed to monodispersed spherical particles with an average size close to 70 nm. As the ratio of increases, the mean diameter decreases, and hence, the morphologies of NiNPs changed from star-like to spherical shape. It can be explained by the influence of the reduction rate on nucleation. Moreover, when most nuclei form, they almost simultaneously grow at the same rate, and the resultant nanoparticles will be monodispersed. At lower hydrazine concentrations, the formation of larger particles with star-like shapes was attributed to the slow reduction rate of nickel chloride, and only a few nuclei formed at the beginning of the reduction reaction. While, with the increase in hydrazine concentration, the reduction rate favored the generation of much more nuclei and the formation of smaller NiNPs (Wu & Chen 2003; Wu et al. 2010; Zhu et al. 2019; Figure 4; Table 2).
Table 2

Physical properties of produced water samples

ParametersMaximum valueMinimum value
pH 8 at T = 26.5 °C 7.4 at T = 26.5 °C 
Oil and grease (mg L−1) 300 200 
TDS (mg L−1) 1,217 950 
Conductivity (μS/cm) 4,200 3,800 
Total oil (IR, mg L−1) 380 59 
ParametersMaximum valueMinimum value
pH 8 at T = 26.5 °C 7.4 at T = 26.5 °C 
Oil and grease (mg L−1) 300 200 
TDS (mg L−1) 1,217 950 
Conductivity (μS/cm) 4,200 3,800 
Total oil (IR, mg L−1) 380 59 
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Figure 3
Ni-1 and Ni-2 TEM images (a) 85 nm and (b) 70 nm.
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Ni-1 and Ni-2 TEM images (a) 85 nm and (b) 70 nm.

Figure 3
Ni-1 and Ni-2 TEM images (a) 85 nm and (b) 70 nm.
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Ni-1 and Ni-2 TEM images (a) 85 nm and (b) 70 nm.

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Figure 4
Removal of dispersed oil from produced water percentage.
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Removal of dispersed oil from produced water percentage.

Figure 4
Removal of dispersed oil from produced water percentage.
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Removal of dispersed oil from produced water percentage.

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Adsorption and kinetics evaluation

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Langmuir–Freundlich isotherm includes the knowledge of the adsorption of heterogeneous surfaces. It describes the adsorption energy distribution onto the adsorbent's heterogeneous surface. The adsorption isotherm provides valuable information about adsorption capacity, binding affinity, and the surface properties of the NiNPs so that the binding mechanism of adsorbate can be understood. Two theoretical models, Langmuir and Freundlich's isotherms, were used to examine the adsorption behavior of NiNPs for dispersed oil uptake into NiNP surfaces, as shown in Figures 5 and 6. Langmuir's isotherm describes the monolayer adsorption of dispersed oil adsorbate onto the NiNP surface, having a finite number of adsorption sites. Freundlich's isotherm supports that adsorption occurs on the heterogeneous surface of the adsorbent. At low adsorbate concentration, this model becomes the Freundlich isotherm model, while at high adsorbate concentration, it becomes the Langmuir isotherm (Ayawei et al. 2017). Table 3 shows adsorption information.
Table 3

Freundlich and Langmuir constants for adsorption of dispersed oil using nickel nanoparticles

W (g)Ci (mg L−1)Ce (mg L−1)qe (mg g−1)1/Celn Ce1/qeln qeRemoval (%)
0.01 550 157 3932 0.0064 5.0547 2.5 × 10−4 8.28 71.5 
0.02 550 100 2250 0.0100 4.6052 4.4 × 10−4 7.72 81.8 
0.03 550 71 1598 0.0142 4.2569 6.3 × 10−4 7.38 87.2 
0.04 550 54 1240 0.0185 3.9890 8.1 × 10−4 7.12 90.2 
0.05 550 40 1020 0.0250 3.6889 9.8 × 10−4 6.92 92.7 
W (g)Ci (mg L−1)Ce (mg L−1)qe (mg g−1)1/Celn Ce1/qeln qeRemoval (%)
0.01 550 157 3932 0.0064 5.0547 2.5 × 10−4 8.28 71.5 
0.02 550 100 2250 0.0100 4.6052 4.4 × 10−4 7.72 81.8 
0.03 550 71 1598 0.0142 4.2569 6.3 × 10−4 7.38 87.2 
0.04 550 54 1240 0.0185 3.9890 8.1 × 10−4 7.12 90.2 
0.05 550 40 1020 0.0250 3.6889 9.8 × 10−4 6.92 92.7 
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Figure 5
Langmuir's isotherm plots for the adsorption of dispersed oil onto NiNPs.
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Langmuir's isotherm plots for the adsorption of dispersed oil onto NiNPs.

Figure 5
Langmuir's isotherm plots for the adsorption of dispersed oil onto NiNPs.
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Langmuir's isotherm plots for the adsorption of dispersed oil onto NiNPs.

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Figure 6
Freundlich's isotherm plots for the adsorption of dispersed oil onto NiNPs.
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Freundlich's isotherm plots for the adsorption of dispersed oil onto NiNPs.

Figure 6
Freundlich's isotherm plots for the adsorption of dispersed oil onto NiNPs.
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Freundlich's isotherm plots for the adsorption of dispersed oil onto NiNPs.

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The removal fits well with Freundlich's isotherm since the value of the linear regression coefficient (R2 = 0.98314) is higher than Langmuir's isotherm (R2 = 0.9775). The results illustrate that dispersed oil adsorbate forms a physical adsorption monomolecular layer into the internal surface of NiNPs.

The maximum adsorption capacity of NiNPs for dispersed oil was 22,841 mg g−1. The value of the separation factor (RL) for NiNPs is less than one (0.670), which favors the adsorption phenomenon. The data in Table 4 demonstrate Langmuir isotherm parameters information.

Table 4

Langmuir's isotherm parameters for the removal of dispersed oil by NiNPs

InterceptSlopeqmax (mg g−1)KL (L mg−1)RLR2
0.000044 0.03905 22,841 0.001121 0.670 0.9775 
InterceptSlopeqmax (mg g−1)KL (L mg−1)RLR2
0.000044 0.03905 22,841 0.001121 0.670 0.9775 
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Kinetics models pseudo first and second orders were applied to perform the concentration change per time. The ability of NiNPs to remove dispersed oil from produced water was governed by (Figure 7). The value shifted when powder doses increased to 3,932, 2,250, 1,598, 1,240, and 1,020 mg g−1, respectively. The rise of NiNP doses does not restrict the adsorption process . The pseudo-first and second kinetics order values were 14.04 and 833.3 mg g−1, respectively, indicating that the dispersed oil fit well with the pseudo-second-order reaction. Correlation factor R2 is used to fit the best kinetics model in Table 5. So R2 of the second order is 0.998, proving that the experimental results of adsorption of dispersed oil on the adsorbent are well described by the second-order kinetic. The K2 value is very lower than 1.93 × 10−3, illustrating that the adsorption was rapid. Consequently, Dehmani and his co-workers studied the ability of nickel oxide nanoparticles to remove phenol at different temperatures. Thus, the adsorption amount increased when the temperature was raised. According to calculated correlation coefficients, the adsorption kinetics of phenol on nickel oxide nanoparticles fit very well to pseudo-second-order (Pirmoradi et al. 2017; Dehmani & Abouarnadasse 2020; Figures 8 and 9).
Table 5

Kinetic parameters for removal of dispersed oil using nickel nanoparticles

InterceptSlopeqe (mg g−1)K-valueR2
Pseudo-first-order 2.64202 0.07329 14.04 0.0014658 0.238 
Pseudo-second-order 7.45E-04 0.0012 833.33 1.93 × 10−3 0.998 
InterceptSlopeqe (mg g−1)K-valueR2
Pseudo-first-order 2.64202 0.07329 14.04 0.0014658 0.238 
Pseudo-second-order 7.45E-04 0.0012 833.33 1.93 × 10−3 0.998 
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Figure 7
The effect of NiNP doses on dispersed oil removal.
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The effect of NiNP doses on dispersed oil removal.

Figure 7
The effect of NiNP doses on dispersed oil removal.
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The effect of NiNP doses on dispersed oil removal.

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Figure 8
Pseudo-second model.
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Pseudo-second model.

Figure 8
Pseudo-second model.
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Pseudo-second model.

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Figure 9
Pseudo-first model.
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Pseudo-first model.

Figure 9
Pseudo-first model.
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Pseudo-first model.

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Oil dispersed removal from produced water quickly by magnetic nanoparticles as recorded in literature because of external magnetic field force. Therefore, it successfully separates oil droplets from attached water (Ko et al. 2014). Figure 4 shows the removal percentage vs. NiNP doses; it can be noticed that there are two adsorption processes. In the first step, the interaction between dispersed oil and the side surface of NiNPs before saturated, while in the second step, dispersed oil molecules saturated the pore size of the NiNPs, and adsorption was reduced due to weak concentration (Gerçel & Gerçel 2007; Bhatnagar et al. 2010).

Positively magnetic nanoparticles crafted by the amine functional group showed high removal of oil from emulsion solution found at 99.5, 97.9, and 96.4%, respectively, by electrostatic forces. Positively magnetic nanoparticles have destroyed the stabilization energy barrier between oil drops, and it separated from the water easily (Ko et al. 2017).

Meanwhile, nickel is essential to transition elements, its magnetic properties higher than the other elements rather than the iron family. The saturated magnetization (Ms) of NiNPs was estimated to be between 50 and 60 emu g−1 (Hwang et al. 1997; Simonsen et al. 2018). Different weights of NiNPs showed a high ability to remove dispersed oil from produced water. The removal percentage was 98% (40 mg L−1). Figure 4 represents the relationship between NiNP doses and removal percentage.

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A new nickel-nanoparticle adsorbent was synthesized through a thermal decomposition process. Hydrazine and sodium hydroxide were employed as reducing agents and a solvent, respectively. When applied to treat oilfield-produced water, the findings indicated that the resulting water was effectively purified and suitable for agricultural reuse. The evaluation of fit quality and adsorption performance often involves the use of linear regression analysis due to its broad applicability in different adsorption data. Furthermore, several researchers have extensively employed nonlinear regression analysis to bridge the gap between predicted and experimental data. Consequently, it is crucial to determine and clarify the utility of both linear and nonlinear regression analysis in various adsorption systems.

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All relevant data are included in the paper or its Supplementary Information.

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The authors declare there is no conflict.

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