Migration of Emulsified Water Droplets in Petroleum Sludge during

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Migration of Emulsified Water Droplets in Petroleum Sludge during Centrifugation Qunxing Huang, Feiyan Mao, Xu Han, Yong Chi, and Jianhua Yan Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef5008837 • Publication Date (Web): 05 Jul 2014 Downloaded from http://pubs.acs.org on July 6, 2014

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Migration of Emulsified Water Droplets in Petroleum Sludge during Centrifugation Qunxing Huang*, Feiyan Mao, Xu Han, Jianhua Yan, Yong Chi

State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China

ABSTRACT: Centrifugation is an efficient and environmentally friendly method to recover valuable oil resources from petroleum sludge. In this paper, the Monte Carlo method was used to predict the settling behavior of different-sized water droplets, based on the Navier-Stokes equation. Differential scanning calorimetry (DSC) was used to deduce the size distribution of emulsified water droplets in petroleum sludge. Numerical estimation and experimental analysis both show that the water removal rate increases with rising centrifugal speed. When the centrifugal speed increased from 2000 to 10,000 rpm, the water removal rate increased from 28% to 99%. Meanwhile, the critical separation size of emulsified water droplet decreased from over 14 µm at 2000 rpm to around 3 µm at 10,000 rpm. The Monte Carlo method was successfully applied to assess the effect of petroleum viscosity and centrifugal speed on the water removal rate and the simulations are in good agreement with DSC analysis. The results of this study can provide quantitative information and guidance for optimizing the centrifugation of petroleum sludge for oil recovery.

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INTRODUCTION Increasing depletion of energy resources and fear of secondary pollution have driven interest in recovering oil from petroleum sludge, which is produced in large quantities during oil refining, storage and transportation.1,2 In China, the annual production of petroleum sludge is estimated at 3 million tons.3 Petroleum sludge contains high concentrations of petroleum hydrocarbons (PHCs) and various recalcitrant components, such as emulsified water and fine solid particles, heavy metals and other impurities.4,5 Improper disposal of petroleum sludge can cause serious threats to the surrounding environment and inflicts health risks to local residents because of the PHCs and heavy metal content.6 Therefore, in many countries it is classified as hazardous waste under current environmental regulations. On the other hand, petroleum sludge is also a potential source of usable oil and chemicals. In the past few years, many techniques have been developed to recover these valuable resources from petroleum sludge before its final disposal.7 Petroleum sludge can generally be considered a stable emulsion of aqueous droplets dispersed in an oily liquid. The emulsion is stabilized by a rigid film at the water/oil interface that prevents water droplets from coalescing with each other. This protective film consists of many surface-active components such as asphaltenes, resins, fine solids, organometallics, etc.8-10 The water droplets are very difficult to remove11 and therefore, the separation of emulsified water from petroleum sludge is of great importance for improving the quality of recovered hydrocarbon resources. Many studies have been devoted to destabilizing the emulsion and removing these


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water droplets. Among these, centrifugation has been used most widely because of its simple system and low consumption of chemicals.7 Petroleum sludge commonly undergoes a de-emulsification pre-treatment before centrifugation to reduce energy consumption and enhance efficiency. This may be accomplished by physical, thermal or chemical methods.12,

13

De-emulsification results in the coalescence of water

droplets and hence a progressive shift in size towards larger droplets. Once the droplets have grown large enough, they can be removed rapidly by centrifugation. The settling behavior of different water droplets in sludge under different operating conditions is considered to be the key to achieving high water phase removal efficiency. However, understanding and modeling the separation of water droplets of diverse sizes in the concentrated and opaque petroleum emulsions is very challenging. In previous research, the statistical Monte Carlo method has been used to study migration in many different scenarios, including solid particle segregation,14,15 separation of gases of different density16 and assessing the effect of particle shape on segregation efficiency.17 Martins et al. used the Monte Carlo method to solve the centrifugation equilibrium problem by modeling all types of particles as hard non-attractive spherocylinders.18 Avendaño et al. also employed a Monte Carlo simulation to study binary mixtures of charged hard spherocylinders and spheres.19 Very recently, Sui and Zhou used the same method to model the non-isothermal degradation of anthocyanins in a complex aqueous system at high temperature.20 However, most of these research findings are not directly applicable to high-viscosity petroleum sludge and currently there is no efficient method to predict the settling


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behavior of different water droplets quantitatively during centrifugation. The objective of this study is to model the separation of emulsified water droplets during their centrifugation and examine the effects of centrifugal speed and sludge viscosity on water removal rates. Petroleum sludge collected from a storage tank was treated in a laboratory centrifuge at different speeds. The water removal rate and droplet size distribution were measured by differential scanning calorimetry (DSC). The transfer of randomly located water droplets in the sludge was modeled by Monte Carlo method based on the Navier-Stokes settling principle. Simulated droplet size distribution after centrifugation was compared with experimental results. The model proposed in this study provides a method for optimizing centrifugation conditions, improving the quality of recovered oil and reducing energy costs.

MODELING Petroleum sludge can be considered as a high-viscosity emulsion of water droplets in a continuous oil phase. The transfer of an individual water droplet during centrifugation can be described using Stokes’ principle, by replacing gravitational acceleration, g , with centrifugal acceleration, ω 2 R . The sedimentation velocity, v , of a water droplet of diameter D can be formulated as v=

D 2 ( ρ − ρ 0 )ω 2 R , 18µ

(1)

where µ is the viscosity of the continuous oil phase, ρ and ρ0 are the densities of the water and the oil phase, respectively, ω is the centrifugal angular speed, and

R is the distance between the sample and the center of the centrifuge rotor. Because


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of the difference in density between the oil and water, the water droplets will be subject to a centrifugal force and will tend to move towards the bottom of the centrifuge tube as shown in Figure 1. If the interaction between water droplets is ignored, larger particles will travel further in a given time,21 so that water particles of different sizes will move to different positions along the radial axis. According to Eq. (1), for any time t and centrifugal speed ω , the settling distance ∆l can be calculated from ∆l = v ⋅ t =

D 2 ( ρ − ρ 0 )ω 2 R ⋅t . 18µ

(2)

From Eq. (2) it follows that if the density of water is higher than that of oil, emulsified water droplets of any size will migrate to the bottom layer given enough time. The migration distance depends on the square of particle size, so the centrifugal time required to separate a 1-µm droplet is 100 times longer than that for a 10-µm droplet. For an idealized case, if the centrifugation time and speed are fixed and the viscosity of the petroleum sludge is known, then a critical diameter, Dc , can be defined to estimate the minimum size of water droplets that will move the distance,

∆lmax , required for separation, i.e., from the surface of the sludge to the bottom of the tube. Dc =

18 µ ⋅ ∆lmax . ω 2 R ( ρ − ρ 0 )t

(3)

Due to the complex of petroleum sludge, many factors can cause deviations from Eq. (3). These factors include non-uniform viscosity and density. Clarkson et al. proposed the grade efficiency function to account for these uncertainties. According to


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the definition proposed by Clarkson et al.,22 the grade efficiency is a possibility function. Particles of the same size may experience different conditions leading to a variation in separation efficiency which is referred to as the grade efficiency curve. For any particle smaller than the critical size, the grade efficiency can be used to determine whether it can be separated or not.22 Mannweiler and Hoare introduced the Rosin-Rammler-Sperling-Bennett (RRSB) distribution function, f ( D) , to describe the grade efficiency curve for particles in a disc-stack centrifuge using D / Dc as an independent variable.23 To achieve 100% separation, particles must be two and half times larger than the critical size, i.e., D / Dc > 2.5 . In this study, the following probability function in Eq. (4) is used to calculate this grade efficiency for different water droplets n f ( D) = 1 − exp − ( AD / Dc )  ,  

(4)

where A and n are both empirical parameters determined by experiments.22 Using the definitions above, the settling behavior of the water droplets in petroleum sludge can now be modeled by the Monte Carlo method and the change in size distribution along the length of the tube is calculated statistically. During the simulation, interaction between, and coalescence of, water droplets are assumed to be negligible. The deviation caused by non-uniform density and viscosity is taken into account by using the grade efficiency factor. The calculation, carried out in Matlab, can be summarized in the following steps

Step 0. Properties of the petroleum sludge sample (density, viscosity and water/oil content) are measured. The original size distribution of the emulsified water droplets


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is analyzed by DSC. The centrifugal speed and geometry are selected and the centrifuge tube is subdivided into ten layers of equal height. The initial particle size distribution is assumed to be the same in all layers.

Step 1. The total number of droplets and their sizes are calculated from the measured water content and droplet size distribution. The current droplet size is set to the minimum size.

Step 2. For each water droplet of a given size, a random number with uniform distribution is generated to determine its original position in the sludge.

Step 3. The settling distance and the grade efficiency are calculated, according to Eqs. (2) and (4), respectively.

Step 4. A random number corresponding to the grade efficiency is generated to determine whether each droplet moves to the new position.

Step 5. The particle moves to its new position if the grade efficiency criterion is satisfied. When it moves to a new position, the number of particles in the old position is reduced by one, and the number in the new position is increased by one.

Step 6. Go to Step 2 and randomly select a new droplet. If all droplets of the given size have been considered, increase current size and go back to Step 2. Once all sizes have been considered, count the size distribution of water droplets in all layers and end calculation.

EXPERIMENTS AND METHODS Petroleum Sludge Sample. Petroleum sludge was collected from the bottom of a


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crude oil storage tank in the harbor at Zhoushan, China. The gross appearance of the sample and an optical micrograph are shown in Figure 2. All micrographs in this article were obtained using an Olympus BX53 optical microscope. The sludge was analyzed to determine those properties required for modeling. Water content was measured by the ASTM-D95-05 procedure and the total hydrocarbon content was evaluated by Soxhlet extraction, using toluene as the solvent. Analytical grade chemical reagents were purchased from Sinopharm Chemical Reagent Shanghai Co., Ltd. The viscosity of the sample at different temperatures was measured using a Brookfield DV-II+Pro viscometer. The physical properties used for the model calculation are given in Table 1.

DSC Analysis. Because petroleum emulsions are opaque and complex, classical optical measurement techniques cannot be used to obtain the droplet size distribution. Differential scanning calorimetry (DSC) is a technique for characterizing emulsions by analysis of their dispersed water freezing transition.6,24 In a previous study, we proposed a quantitative analytical method to establish the relevant properties of emulsified water in petroleum sludge, including droplet size distribution, stability of the water-in-oil (W/O) emulsion, total water content and removal ratio of solids after treatment.6 Based on the principle of thermal equilibrium, we suggested the empirical Eq. (5) to determine the water droplet diameter Di according to the measured freezing temperature Ti . Equations describing weight distribution and number distribution of droplet size were also developed.6

Di = exp(

−35 ), Ti + 10


(5)

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DSC analyses were carried out with a Netzsch apparatus type 200F3 using nitrogen as the carrier gas at a flow rate of 60 mL/min. Around 9 mg of sample was submitted to a controlled heating and cooling procedure in a hermetically sealed aluminum crucible to detect the heat absorbed or released during the phase change of emulsified water droplets. The heating and cooling rates were both set at 5 °C per minute. Samples were first heated to 50 °C, where they were held for 5 minutes, to ensure contact with the crucible and thus a uniform temperature distribution within the sample. The sample was then cooled with liquid nitrogen to -60 °C and reheated to 20 °C. The analysis for all samples has been repeated to avoid sampling uncertainty.

RESULTS AND DISCUSSION As shown in Figure 2, the petroleum sludge before centrifugation is a black viscous liquid with an appearance similar to heavy oil. Different-sized spherical water droplets are emulsified in the sludge, as seen in the micro picture. After centrifugation, some of these water droplets may still be retained in the lighter layer of oil, reducing the quality of recovered oil. The water removal efficiency depends mainly on the centrifugal speed if the viscosity of the sludge is fixed.

Effect of Centrifugal Speeds on the Water Removal Efficiency. According to Eq. (2), the settling distance is proportional to particle size. Hence, it is necessary to study the water droplets in the upper layer after centrifugation before model simulation. A series of 3-mL samples of petroleum sludge were placed in 5-mL centrifuge tubes and centrifuged at different speeds for 10 minutes, after which the upper layer was


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examined microscopically and analyzed by DSC. The micrographs of the recovered oil shown in Figure 3 clearly illustrate the diminishing size of water droplets remaining in the upper layer seen with rising centrifugal speed. The DSC thermograms of these samples (Figure 4) also show that emulsified water droplets release and absorb heat at different temperatures. For instance, a population of isolated water droplets will freeze well below the ice-water equilibrium temperature because of undercooling6 and the temperature at which this occurs varies strongly with droplet size and cooling rate; the smaller the water droplets, the lower their freezing temperature. The oil sample recovered as a lighter layer after treatment at a centrifugal speed of 2000 rpm showed a broad freezing transition between -20 °C and -37 °C, with a small peak around -44 °C. When the speed increased to 4000 rpm, there are two exothermic signal peaks located at -35 °C and -44 °C. The DSC curves of samples centrifuged at 6000 rpm and 8000 rpm show a single freezing peak at around -44 °C, whereas for samples centrifuged at 10,000 rpm, this peak becomes almost undetectable. The displacement of freezing transition signals towards lower temperatures can be understood by the shift towards smaller sizes with rising rotational speeds. For all samples, Gaussian-shaped melting peaks were observed near 0 °C during the heating period, while the heat flux of the melting transition decreased at higher speeds (Figure 4). According to the DSC principle, the total heat absorbed by frozen water during melting within an emulsion can be used to determine the water content,6 with a linear relationship between the integral of the recorded heat flux and the water content. The evolution of the water removal rate deduced from DSC


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analysis is shown in Figure 5. For petroleum sludge centrifuged at 2000 rpm, the water removal rate was very low, and more than 70% of the initial water content remained in the oil. By referring to the micrograph in Figure 3 and to Eq. (2), at this low speed only large water droplets are removed from sludge as fine water droplets do not transfer to the bottom layer within the 10-minute treatment time. Increasing the speed to 4000 rpm dramatically enhances the dewatering rate to nearly 80%, and 94% of the water was removed from petroleum sludge at a speed of 6000 rpm. However, when the speed was increased to 10,000 rpm, although large droplets can no longer be observed from the micrograph, the recovered oil still contains approximately 1% of fine water droplets. Therefore, it is essential to understand the settling behavior of different-sized water droplets, especially for small particles at high centrifugal speed.

Monte Carlo Simulation. The size distribution of water droplets in 10 uniform layers of petroleum sludge was calculated after centrifugation, using the Monte Carlo method described above, and the results were compared with data derived from DSC thermograms. The sludge sample was centrifuged at a speed of 10,000 rpm for 10 minutes and then divided into upper, middle and bottom sub-layers. Each of the sub-layers was subjected to DSC analysis to establish the size distribution of the emulsified water droplets. The water droplet size distribution of the original petroleum sludge used for the Monte Carlo simulation is given in Figure 6. Two groups of water droplets, peaking at 3.5 µm and 5 µm, can be distinguished. The DSC-derived and Monte Carlo-simulated size distribution curves are shown in Figure 7 (a), (b) and (c), for upper, middle and bottom layers, respectively. The size


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profile in the upper layer indicates that experimental and simulated results are very close and cover the same size range, from 2.8 to 7.5 µm. The simulated data for fine particles around 3 to 3.5 µm are higher than the experimental data; this trend reverses for droplets larger than 3.5 µm. These differences may be attributed to the fact that during the experiments, small water droplets coalesce to form bigger droplets. However, during numerical calculation, such coalescence was ignored. In the middle layer, for particles smaller than 3.5 µm, the differences between the values predicted by the model and the experimental values are very small. During centrifugation, the viscosity of the middle layer will increase because of the migration of heavy components (mostly asphaltenes) into this layer, resulting in increased settling resistance. However, the model does not take this viscosity variation into account, which leads to excessive predictions in the distribution for particles around 4.5 µm to 6.5 µm and lower distribution for particles larger than 6.5 µm. The emulsified water droplet size distributions in the bottom layer found by Monte Carlo simulation and experiment are shown in Figure 7 (c). The overall trend of the simulated graph is almost the same as the experimental results. There is a large population of droplets in the 5 to 10-µm range, suggesting that further multi-stage centrifugation could effectively reduce the water content in the bottom layer. Thus, the water droplet size distribution obtained from the Monte Carlo simulations can be used to optimize the treatment strategies for petroleum sludge after centrifugation. The dewatering rate and the water droplet size distribution varied with different centrifugal speeds. Figure 8 depicts the distribution of water droplet size in the upper


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layer. The simulation agrees well with the experimental data. Centrifugation at 2000 rpm produced a broad size distribution, mostly between 2.7 and 14.4 µm. A decrease in the number of large water particles was observed when increasing the centrifugal force. After being centrifuged at 4000 rpm for 10 minutes, the recovered oil still contained water droplets around 2.6 µm to 10.0 µm, with two peaks at 3.1 µm and 5.4 µm. When the petroleum sludge was centrifuged at 8000 rpm, all droplets larger than 6 µm were removed. For a W/O emulsion, the smaller the water droplets, the more difficult dewatering will be. As mentioned above, the upper layer still contains droplets smaller than 3 µm after centrifugation at 10,000 rpm (Figure 7).

Prediction of the Critical Separation Size. The size values obtained both from numerical calculation and from DSC analysis at different centrifugal speeds are plotted in Figure 9. For the petroleum sludge discussed in this work, the critical separation size decreased from over 14 µm at 2000 rpm to around 3 µm at 10,000 rpm. The trend seen in the calculated values is consistent with experimental results. The differences can be explained tentatively by interactions between water droplets and by viscosity variations during centrifugation. Since de-emulsification leads to the coalescence of water droplets, applying a de-emulsification pre-treatment to the sludge could enhance the water removal efficiency. For instance, once water droplets in petroleum sludge are larger than 7 µm, theoretically all water droplets can be removed from recovered oil within 10 minutes at a centrifugal speed of 4000 rpm. Two other centrifugal times (20 and 30 minutes) at different speeds were also tested using the model, and the results suggest that increased centrifugal time improves the


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centrifugation performance because all droplets larger than 5 µm can be removed at 4000 rpm in 30 minutes (Figure 9). Thus, the critical separation size can provide quantitative guidance for optimizing the centrifugation parameters.

Prediction of the Water Removal Rate. Once the water droplet size distribution is known, it is easy to deduce the water content in the recovered oil, so the Monte Carlo method can be used to predict the water removal rate under different operating conditions. Figure 10 illustrates the evolution of water removal efficiency as a function of both centrifugal speed and viscosity. The predictions are in good agreement with the experimentally measured values shown in Figure 6. Sludge viscosity has a great influence on water removal rate. For a given set of centrifugal condition, the dewatering rate decreased dramatically with rising viscosity. In practice, viscosity reduction by preheating the sludge is often found to be beneficial. For high-viscosity sludge, with the increase of the centrifugal force, the water removal rate increases almost linearly, whereas at a viscosity of 3 Pa·s, the increased rate of removal efficiency slowed with respect to the centrifugal speed.

CONCLUSION Centrifugation is an efficient and environmentally friendly method to recover oil from petroleum sludge. To reduce the water content retained in the recovered oil, it is essential to understand the migration of emulsified water droplets in petroleum sludge. Numerical calculation and experimental results show that the settling distance of emulsified water droplets is proportional to centrifugal speed and time. For the sludge


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samples discussed in this paper, small droplets (less than 3 µm) will be retained in the oil layer, even after centrifuging at 10,000 rpm for 10 minutes. The Monte Carlo method can be used to assess the effects of centrifugal speed and sludge viscosity on the critical separation size and water removal rate. The model simulation can be used to optimize the centrifugal separation system and improve the quality of recovered oil from petroleum sludge.

AUTHOR INFORMATION Corresponding Author *Telephone: +86-571-87952834. Fax: +86-571-87952438. E-mail: [email protected]

ACKNOWLEDGMENT Acknowledgment is gratefully extended to the National Basic Research Program of China 973 Program (Grant No. 2011CB201500), the National Science & Technology Pillar Program (No. 2012BAB09B03), the National High Technology Research and Development Program (No. 2012AA063505) and the Creative Team Project of Solid Waste Treatment of Zhejiang Province (A2009R50049) for their financial support.

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Tables Table 1. Petroleum Sludge Properties. Parameter

Value

Water by distillation/wt (%)

40.30

Oil by solvent extraction/wt (%)

58.15

Solid residues/wt (%)

1.55

Viscosity at 25°C/Pa·s

3

Density of water/kg·m

-3

Density of continuous phase/kg·m-3

1×103 0.8×103


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Figures Figure 1. Schematic of water droplet behavior in petroleum sludge during centrifugation.


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Figure 2. Gross and microscopic appearance of petroleum sludge.


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Figure 3. Micrographs of recovered oil from petroleum sludge centrifuged at different speeds for 10 minutes.


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Figure 4. DSC of oil recovered from petroleum sludge centrifuged with a cooling/heating rate of 5 °C/min.


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Figure 5. Water removal rate of petroleum sludge centrifuged at different centrifugal speeds for 10 minutes.


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Figure 6. Size distribution of water droplets contained in original petroleum sludge.


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Figure 7. Size distributions of water droplets in upper (a), middle (b) and bottom layer (c) of petroleum sludge centrifuged at 10,000 rpm for 10 minutes.

(a)

(b)


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(c)


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Figure 8. Number proportion of water droplet size in recovered oil.


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Figure 9. Calculated and experimental values of critical separation diameter.


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Figure 10. The effect of sludge viscosity on water removal rate for petroleum sludge centrifuged at different speeds for 10 minutes.


Migration of Emulsified Water Droplets in Petroleum Sludge during

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