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Chemical Industry & Chemical Engineering Quarterly
Chem. Ind. Chem. Eng. Q. 22 (3) 309−318 (2016)
DRAGAN D. GOVEDARICA1
RADMILA M.
ŠEĆEROV SOKOLOVIĆ1
OLGA M. GOVEDARICA1
DUNJA S. SOKOLOVIĆ2
SNEŽANA V.
SINADINOVIĆ-FIŠER1
1
Faculty of Technology, University
of Novi Sad, Novi Sad, Serbia
2
Faculty of Technical Sciences,
University of Novi Sad, Novi Sad,
Serbia
SCIENTIFIC PAPER
UDC 66.067:544:66
DOI 10.2298/CICEQ150604045G
CI&CEQ
SEPARATION OF OIL-IN-WATER EMULSIONS
BY FLOW THROUGH FIBER BEDS: A
RESPONSE SURFACE APPROACH
Article Highlights
• Polyethylene terephthalate fiber bed was used successfully for coalescence filtration
• A novel approach for the estimation of the effluent oil concentration and the critical
velocity
• A good correlation of experimental data was obtained with response surface regression model
• Higher critical velocities were observed for high oil viscosity and high bed permeability
Abstract
Separation of oil-in-water emulsions using fiber bed coalescer was studied by
response surface methodology. The bed was formed of polyethylene terephthalate fibers. The aim was to investigate the possibility of using response
surface regression for the analysis and prediction of both the effluent oil concentration and critical velocity in broad ranges of flow rates, bed permeabilities,
and nature of dispersed oil phase. The developed response surface equations
for a constant bed permeability are a responsive statistical method with the
calculated multiple R higher than 90%. It was found that dispersed oil density,
viscosity, neutralization number, and bed permeability influence significantly
the oil removal efficiency of fiber bed coalescers. The region of highest critical
velocity has been observed for a high viscosity and a high fiber bed permeability. The developed response surface models can be used to ensure high
separation efficiency and improve coalescer performance when unexpected
simultaneous changes in the bed permeability, hydrodynamic forces and dispersed oil properties take place.
Keywords: liquid-liquid separation; oily water; bed coalescence; fiber
material; critical velocity; response surface regression.
Emulsion separation is commonly required in
the petroleum industry for treatment of wastewater
and solvents, as well as for dehydration of crude oil
and petroleum products such as diesel, gasoline, and
kerosene [1-6]. A frequently applied method for
breaking emulsions is fiber bed coalescence, which
may operate stand-alone or integrated with other
separation techniques [7,8]. Fiber bed coalescers are
common separation equipment when it comes to
polishing both of the two liquid phases involved in the
Correspondence: D.D. Govedarica, Faculty of Technology,
Department of Oil and Petrochemical Engineering, University of
Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia.
E-mail: [email protected]
Paper received: 4 June, 2015
Paper revised: 12 November, 2015
Paper accepted: 13 November, 2015
emulsion. Coalescers are frequently located downstream of the gravity settlers or decanters, to increase
the quality of the fluid or meet environmental discharge limits. The coalescers are usually used for
removal of small-size droplets smaller than 100 μm in
the course of separation of water from petroleum
products and treatment of quench water [1]. To avoid
the uncertainty of the design procedure, each particular application of the bed coalescer demands extensive pilot testing over the range of expected working conditions. Reliable mathematical models for process optimization are scarce because of the complex
phenomena involved in the fiber bed coalescence: the
diverse mechanisms of coalescence, the filter media
structure, and the emulsion flow through the bed
formed of fibers [9].
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D.D. GOVEDARICA et al.: SEPARATION OF OIL-IN-WATER EMULSIONS…
Some mathematical models take into account
the effect of hydrodynamic, attraction and repulsion
forces on the coalescence efficiency [10]. Other
models assume that the coalescence and breakage
of droplets are dominantly governed by the fiber
structure, topology of the fiber bed, and emulsion flow
through the filter media [11].
Sareen et al. [12] and Grilc et al. [13,14] have
pointed out the importance of fluid velocity to emulsion flow as it controls the coalescence mechanisms
and probability of droplet coalescence. Some researchers consider also the droplet size as a highly
significant parameter for adjusting the coalescer operating variables [15,16]. Fahim and Akbar [17] concluded that the coalescer separation efficiency is
greatly dependent on fluid velocity, influent oil concentration and bed length. However, some other
authors have confined to a relatively narrow range of
working conditions, and therefore the conclusions
were contradictory [12,18,19].
Based on the available mathematical models for
prediction of the coalescence efficiency, it can be
concluded that occurrence of each coalescence
mechanism has a certain probability [10,11,16,20-23].
The analysis by Ryan and Elimelech [10] suggested
that some filtration mechanisms may be analyzed
analogously to pseudo-first-order chemical reactions.
Since the bed coalescence efficiency is dependent on
numerous factors, it is expected that good correlation
can be achieved with appropriate statistical models.
Factorial design of experiments and response
surface methodology were used to analyze different
separation processes for oily wastewaters, such as
filtration [24], membrane processes [25-27], adsorption [28] and resin bed coalescence [29]. The adventage of using statistical methods for data correlation,
such as response surface methodology, is in the reduction of the number of pilot scale experiments.
Response surface methodology has been used
by Kundu et al. [29] to identify significant interaction
effects between experimental parameters for separation of diesel oil/water emulsion using a resin bed
coalescer. The granular bed was formed of spherical
particles, and a second-order response surface model
showed that the oil removal efficiency is a function of
bed length, flow rate, and pH.
The usage of polyethylene terephthalate polymer fibers for bed coalescence has been a subject of
numerous studies concerning the influences of superficial velocity, bed permeability and porosity, fiber surface roughness, and inflow direction on the phase
separation efficiency of oil-in-water emulsions [18,30-36]. However, there are no published results in which
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Chem. Ind. Chem. Eng. Q. 22 (3) 309−318 (2016)
response surface method was used to analyze the
operation of fiber bed coalescer.
Therefore, the aim of this work was to investigate the possibility of using response surface regression for the analysis and prediction of separation efficiency of fiber bed coalescence in a broad range of
working parameters. The goal was to develop reliable
response surface models that would ensure high
separation efficiency and improved coalescers performances when unexpected changes in the bed permeability, hydrodynamic forces and dispersed oil properties take place simultaneously.
EXPERIMENTAL
Experimental setup of the fibrous bed coalescer and
operating procedure
The experiments were performed on a laboratory-scale bed coalescer with the horizontal fluid flow
orientation, whose design was described in detail in a
previous paper [37]. Crude oil (A), naphtenic-base
vacuum distillation fractions (A1 and A4), and petroleum product with a high paraffinic content (P1) were
used as the dispersed phase for bed coalescence
experiments. The dispersed phases were mineral oils
of different properties containing natural emulsifiers
such as asphaltenes. Oil droplets were dispersed in
tap water by adding measured amounts of oil to the
supply tank. The oil-in-water model emulsions with
constant oil concentration (500 mg/L) were prepared
in a supply tank (80 L), by continuous agitating with a
stainless steel impeller (650 rpm). In order to ensure
the inlet mean droplet diameter of about 10 µm, each
oily sample was continuously stirred 45 minutes prior
to the experiment and onwards until the end of experiment. The mean inlet droplet size of the number
distribution was dependent of the nature of the dispersed oil phase and it was determined by an Elzone
280 PC particle counter and an Olympus BH.2 RFCA
microscope:
• 9-10 µm (min. 0.8 µm, max. 31 µm) for oil
A/water,
• 10-12 µm (min. 0.9 µm, max. 33 µm) for oil
A4/water,
• 10-11 µm (min. 0.7 µm, max. 26 µm) for oil
A1/water and
• 9-10 µm (min. 0.9 µm, max. 28 µm) for oil
P1/water.
Hence, the influent droplet size did not vary
significantly. The steady-state regime of bed coalescence was achieved from the very beginning of the
experiment by pre-oiling the polyethylene terephthalate fibers. A steady state was confirmed by moni-
D.D. GOVEDARICA et al.: SEPARATION OF OIL-IN-WATER EMULSIONS…
toring the pressure drop, which did not change with
time. The filter media were polyethylene terephthalate
(polyester) fibers. In a coalescence experiment, the
following parameters were kept constant: bed length
(5 cm), bed permeability, and working temperature
(20 °C). Each oily water sample was tested on five
bed permeabilities. The oil-in-water emulsion was
pumped through a membrane dosage pump at the
fluid velocities ranging from 18 to 70 m/h. The selected velocity was kept constant for 1 h. Composite
samples of oil-in-water emulsion were collected at the
sampling point downstream of the fiber bed after 45
min at 5-min intervals.
Properties of dispersed oils
Four different kinds of dispersed oils with a wide
range of physical and chemical properties were used
(Supplementary material, available from the authors
upon request). Density was determined according to
ISO 3675. Kinematic viscosity was measured using
glass capillary viscometers according to the standard
ISO 3104. Neutralization number was determined by
potentiometric titration (ISO 6619). The mean molecular weight was estimated according to standard
method ASTM d 2502-67 from kinematic viscosity
measurements. Interfacial tension and surface tension measurements were done according the du
Noüy ring method and stalagmometric method, respectively.
Properties of the bed
The bed was formed of waste polyethylene
terephthalate fibers that remained after cutting out
blocks of the filter for kitchen exhaust hood. The polyethylene terephthalate fibers were needle-punched
and non-woven. The orientation of the fibers was
random. The surface morphology and size of the
fibers were characterized by scanning electron microscopy. The used fibers were smooth and staple with
average length of 50 mm and average diameter of 60
μm. The fibers had a circular cross-section profile.
Experiments were realized in a broad range of bed
porosity (0.90-0.98) and solid surface (1.330-6.670
mm-1). Due to the compressibility of polyethylene
terephthalate fibers, it was possible to vary the bed
permeability over a wide range (0.18×10-9-5.389×10-9
m2). The bed permeability was calculated from the
measured pressure drop across the bed for tap water,
and the data complied with Darcy's law. Numerical
values of the investigated bed permeabilities are
available from the authors upon request.
Chem. Ind. Chem. Eng. Q. 22 (3) 309−318 (2016)
Effluent oil concentration
Samples were stabilized and adjusted to pH 2
by adding HCl. Each emulsion sample was extracted
with CCl4. The effluent oil concentration was determined by FTIR spectrometry using a ThermoNicolet
5700 spectrometer. To determine the oil in water content, the peaks with maxima at about 2930 (–CH2
groups), 2960 (–CH3 groups) and 3030 cm-1 (C–H aromatic groups) were considered. The effluent oil concentration was followed as a function of the fluid velocity, which was increased until the effluent oil concentration was higher than 15 mg/L.
Critical velocity (Vk)
The critical velocity in our experiments was
defined as the fluid velocity when the effluent oil concentration reaches 15 mg/L [38-40]. The value of the
critical velocity was determined from the dependence
of the effluent oil concentration on the superficial
velocity of the emulsion.
Statistical analysis
The statistical analysis was based on 92 experimental values of effluent oil concentration and 20
values of the critical velocity extracted from the raw
experimental data. The fiber bed coalescence experiments were carried out over a wide range of dispersed oil properties, bed permeability, and fluid velocity. Because the concentration of the coalescer
effluent may not be stable, each experiment was
repeated twice. Experimental data were analyzed by
response surface regression (RSR) and analysis of
variance (ANOVA). Statistical analysis was performed
using statistical package Statistica 12, StatSoft.
RESULTS AND DISCUSSION
Introduction
The experimental data correspond to a coalescer whose bed permeability, hydrodynamic forces
and dispersed oil properties varied simultaneously
during the operation. Some changes in the bed
permeability can be caused by deposition of some
particulate matter, such as corrosion products, impurities, solid waste, surfactants, etc. Failure of the bed
coalescer function can also be caused by ineffective
pre-filtration of inflow emulsion and porosity reduction
due to high holdup or fluctuations of dispersed phase.
Emulsion flow to the bed coalescer can greatly vary
depending on the operational modes of the equipment upstream the coalescer. Since such variations
of operational conditions are common in the petroleum industry, their influence on the efficiency of fiber
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D.D. GOVEDARICA et al.: SEPARATION OF OIL-IN-WATER EMULSIONS…
bed coalescence has to be examined. Therefore, the
effects of dispersed oil properties, fluid velocity, and
bed permeability on the effluent oil concentration were
followed based on a statistical approach using RSR.
Dependence of the effluent oil concentration on the
fluid velocity, bed permeability and dispersed oil
properties
n
i =1
i =1
lnC i = α 0 +  α i lnX i +  α ii lnX i2 +
n
+   α ji lnX i lnX j
fluid velocity. The coefficient α0 is the intercept,
whereas αi, αii and αji are the linear, quadratic and
interaction regression coefficients, respectively. By
applying the response surface regression analysis,
the following equation was obtained:
lnC i = −2610.85 + 1038.86lnK 0 − 0.19(lnK 0 )2 −
Experimental values for dispersed oil properties,
bed permeability, fluid velocity and effluent oil concentration are available from the authors upon
request. Because the variables are not measured on
the same scale, the natural logarithms of experimental data values were fitted using an RSR model. The
second-order response surface models are widely
used because they are very flexible statistical
methods for an approximation of the true response
surface. There is considerable practical experience
showing that these models work well in real separation problems [24-29]. Hence, the experimental data
in this work were fitted with response surface regression using the following second-order model:
n
Chem. Ind. Chem. Eng. Q. 22 (3) 309−318 (2016)
(1)
j <i
This nonlinear model includes the intercept,
linear, quadratic, and interaction terms. The response
is lnCi (effluent oil concentration), and lnXi and lnXj
are the independent variables, i.e., factors. In this
paper, the independent variables for the estimation of
the effluent oil concentration were: bed permeability,
viscosity, density, neutralization number, mean molecular weight, interfacial tension, surface tension, and
−34.69lnμ + 4.32(lnμ )2 + 380.44lnρ +
+950.3lnV + 3.96(lnV )2 + 0.22lnK 0lnμ −
−154.12lnK 0lnρ + 2.56lnK 0lnN b +
(2)
+0.63lnK 0lnV − 0.54lnμ lnV −
-141.26lnρ lnV + 2.75lnN b lnV
The calculated multiple R was 76.72%, multiple
R-squared was 58.86%, and adjusted R-squared was
51.38%. The deviations between the experimentally
determined lnCi values and values predicted using
response surface regression equation (2) are shown
in Figure 1. Equation (2) can be used to predict the
coalescer performances for a wide range of flow
rates, bed permeabilities, and nature of the dispersed
oil phase.
The relatively narrow 95% confidence interval
was a result of the simultaneous effect of hydrodynamic forces on coalescence and occurrence of
several bed coalescence mechanisms. In order to
identify significant factors and evaluate those that
have pertinent effect on the coalescence efficiency,
ANOVA analysis was performed. This statistical
method is commonly used for the hypothesis testing
on the regression model and coefficients. The calculated Fisher variance ratio (F-value) and significance probability value (p-value) of the RSR model
were 7.86 and 0.00, respectively. The high F-value
implies that most of the variation in the response can
Figure 1. Deviation between the calculated and experimental values of the effluent oil concentration for all investigated permeabilities.
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D.D. GOVEDARICA et al.: SEPARATION OF OIL-IN-WATER EMULSIONS…
be explained by Eq. (2). The low p-value (less than
0.05) indicates that the model terms are significant at
the 95% confidence level.
In order to evaluate the effect of the linear,
quadratic, and interaction terms, a Pareto chart of the
calculated Student’s t-test (t-values) for the coefficients was constructed (Figure 2). Based on sigmarestricted parameterization, the Pareto chart illustrates the most important effects of the factors. It
should be noted that the higher the t-value of the
effect, the higher the influence on the effluent oil concentration response. Hence, a factor coefficient with a
significance p-value less than or equal to 0.05 is considered as statistically significant and a reference line
is drawn [24,28,29]. Considering the accepted limit, it
can be noted that any effect that falls beyond the
reference line in Figure 2 is significant. The quadratic
effects of fluid velocity and bed permeability had the
greatest effect on the coalescence efficiency. Furthermore, the interaction terms representing the effects of
bed permeability, neutralization number, and density,
as well as the linear terms of density, viscosity and
bed permeability are significant. The quadratic effects
of fluid velocity, bed permeability and viscosity cause
the effluent oil concentration response surface to
have a curved shape.
The 3D plot depicts better the interdependence
of the effluent oil concentration, bed permeability and
fluid velocity (Figure 3). It is clear that the region of
the highest bed permeability and lower fluid velocity
corresponds to the minimal effluent concentration.
This region ensures the desired effluent concentration
and separation efficiency. On the other hand, many
research studies identified the region of low effluent
oil concentration corresponding to the lowest bed permeability [41]. How can we explain the observed trend
Chem. Ind. Chem. Eng. Q. 22 (3) 309−318 (2016)
Figure 3. Three-dimensional plot representing the interdependence of the effluent oil concentration, fluid velocity
and bed permeability.
in the lower effluent oil concentration associated with
the highest bed permeability? In our previous investigations [36,38,42] we have shown that the low
effluent oil concentration is dominantly influenced by
bed permeability and coalescence mechanisms that
occur in a fiber bed. It is plausible to suppose that the
different bed coalescence mechanisms are predominant in low and high dense packed fiber bed. The
highest bed permeability can provide the domination
of the coalescence of the droplets into the surface of
the saturated liquid i.e., a capillary-conducted fluid.
On the contrary, the lowest bed permeability contributes to a domination of the coalescence of the droplets on the fibers surface. As can be seen, the effluent
oil concentration increases with an increase in fluid
Figure 2. Pareto chart for standardized effects for effluent oil concentration for all investigated permeabilities.
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D.D. GOVEDARICA et al.: SEPARATION OF OIL-IN-WATER EMULSIONS…
velocity. The plot illustrates the region of high fluid
velocities, corresponding to the highest standard deviation of the effluent oil concentration.
The Pareto chart and 3D interdependence of the
effluent oil concentration, bed permeability and fluid
velocity indicate that the separation efficiency is significantly influenced by bed permeability. The change
of polyethylene terephthalate bed permeability essentially influences: emulsion flow velocity, droplet
deformation, coalescence of the adjacent droplets,
coalescence of the droplets on the fibers surface, and
coalescence of the droplets into the surface of the
saturated oil [9,31,42].
Estimation of effluent oil concentration over constant
bed permeability
Since the ANOVA analysis and RSR demonstrated that the effluent oil concentration is dominantly
influenced by bed permeability, further analysis was
conducted to verify the response surface models over
a fixed bed geometry. Additionally, the analysis for a
defined bed permeability is valuable for the investigation of the working parameters for coalescers with
integrated pre-filtration.
This approach enabled a more precise estimation of the effluent oil concentration over a constant bed permeability using RSR model with significantly higher multiple R, multiple R-squared and
F-value (Table 1).
For all investigated bed permeabilities, the calculated multiple R was higher than 90%. It should be
also noted that the p-values of the RSR models were
considerably lower than 0.05, indicating statistical significance of the presented models.
Chem. Ind. Chem. Eng. Q. 22 (3) 309−318 (2016)
For the highest bed permeability, the calculated
multiple R was 95.42%, multiple R-squared 91.06%,
and F-value 14.00. The R-squared indicates that
91.06% of the variability within the range of values
studied can be explained by the model. The lowest
p-value indicates that for the set of the presented bed
coalescence experiments, observed data are adequately fitted to the second-order polynomial model.
The best fit of this model can be explained by the
dominance of the coalescence of droplets into the
surface of the saturated oil. Due to the high porosity,
a significant amount of saturated liquid, i.e. capillaryconducted phase, is being formed. In such cases, the
oil droplets coalescence on the surface of the capillary-conducted phase becomes the dominant mechanism of coalescence. For a steady-state regime,
Spielman and Goren observed 30 to 40% of the pores
are occupied by the capillary-conducted phase [9].
The amount of saturated oil influences the distribution
of certain coalescence mechanisms of all finely dispersed oil droplets upstream of the fiber bed, and
coalescence of the adjacent droplets in the pore
space. Experimentally determined and calculated
effluent oil concentrations over the highest bed permeability are shown in Figure 4. The 95% confidence
intervals of the RSR model for the highest polyethylene terephthalate permeability include the bed coalescence experiments carried out in the range of lower
fluid velocities, which greatly decreased the intensity
of the hydrodynamic forces (Figure 4, Table 1).
The ANOVA and Pareto analysis for the highest
bed permeability showed that the quadratic term of
fluid velocity and the linear term of oil density had the
Table 1. RSR equations and R, R2, F-value and p-value for estimation of effluent oil concentration over constant bed permeability
Bed permeability, m
K01 = 5.389×10
-9
2
Second order RSR equation
2
lnC i = 6097.21 + 70.18lnμ − 7.24(lnμ ) − 898.27lnρ +
+9813.54lnV + 10.36lnV
2
R
R2
F-value
p-value
95.42
91.06
14.00
0.000093
92.66
85.87
5.32
0.020116
92.45
85.47
7.35
0.002467
93.57
87.55
9.67
0.000521
90.90
82.64
4.76
0.020380
− 0.29lnμlnV −
−1451.02lnρ lnV + 24.90lnN blnV
K02 = 2.426×10-9
lnC i = 3573.69 + 84.18lnμ − 10.77(lnμ )2 − 538.59lnρ +
+15723.28lnV + 5.13lnV
K03 = 1.128×10
-9
2
− 2315.63lnρ lnV + 35.74lnN blnV
lnC i = 6300.764 + 40.963lnμ − 3.368(lnμ )2 − 928.272lnρ +
+3154.995lnV + 6.675lnV
2
− 2.752lnμlnV −
−468.236lnρ lnV + 10.718lnN blnV
K04 = 0.380×10-9
lnC i = 7302.99 + 88.44lnμ − 9.85(lnμ )2 − 1082.86lnρ +
2
+12486.34lnV + 8.66lnV + 1.31lnμlnV −
−1842.37lnρ lnV + 30.94lnN blnV
K05 = 0.180×10-9
lnCi = 379.438 + 1.475lnμ − 1.261(lnμ )2 − 38.419lnρ +
2
+1672.284lnV + 8.645lnV +3.044lnμlnV −
−255.845lnρlnV + 3.844lnN blnV
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Chem. Ind. Chem. Eng. Q. 22 (3) 309−318 (2016)
Figure 4. Relationship between the calculated and experimental values of the effluent oil concentration over K01 for all investigated oils.
highest effect on the effluent oil concentration. Concerning the accepted limit of the p-value less than or
equal to 0.05, it can be stated that the quadratic term
of fluid velocity represents the influence of hydrodynamic forces on the effluent oil concentration, while the
linear term of density represents the effects of the
dispersed oil cross-sectional distribution and settling
velocity in the coalescer housing [42]. The ANOVA
and Pareto analysis revealed that the interaction term
of neutralization number, linear term of viscosity,
interaction term of density and fluid velocity, linear
term of velocity, as well as the quadratic term of viscosity, had a high effect on the removal of dispersed
oil droplets. This means that these terms are statistically significant for the estimation of the effluent oil
concentration over constant bed permeability.
Estimation of the critical velocity
In the petroleum industry, the goals of coalescer
design are the acceleration of emulsion separation
and equipment size minimization. A bed coalescer
designed to operate efficiently at high fluid velocities
would be much more effective in oily wastewater
treating facilities. In this work, the effects of nature of
dispersed oil phase and bed permeability were analyzed using response surface methodology. Experimental values of dispersed oil properties, bed permeability, and the critical velocity available from the
authors upon request.
The logarithmic experimental data were fitted
with RSR using the following second-order model:
n
n
n
i =1
i =1
j <i
V k = α 0 +  αi lnX i +  αii lnX i2 +  α ji lnX i lnX j
(3)
The response surface model includes the intercept, linear, quadratic and interaction terms. The
independent variables (factors) were ln Xi and ln Xj,
and the response was the critical velocity term ln Vk.
The effluent oil concentration was estimated for the
following independent variables: density, viscosity,
neutralization number, mean molecular weight,
interfacial tension, surface tension and bed permeability, and the following RSR equation was developed:
lnV k = 1137.218 − 331.66lnK 0 − 0.005(lnK 0 )2 +
+16.071lnμ − 1.684(lnμ )2 − 171.685lnρ +
(4)
+0.05lnK 0lnμ + 48.653lnK 0lnρ − 0.802lnK 0lnN b
The calculated multiple R was 92.50%, multiple
R-squared 85.55%, and adjusted R-squared 75.05%.
As already mentioned, the high calculated Fisher variance (F-value) of 8.1428 implies that most of the
variation in the critical velocity can be explained by
Eq. (4). The Model F-value of 8.1428 implied that the
model was significant. The model p-value was very
low (0.001119), proving the significance of the model.
Equation (4) gives the possibility to estimate the
values of the critical velocity for a given set of factors,
and it may be useful for the estimation of the upper
limit of the critical velocity for a successful operation.
In order to confirm the normal distribution of the
data, a normal probability plot of the residuals was
analyzed (Figure 5). Since the points on the normal
probability plot fall approximately on a straight line,
the regular residuals were normally distributed [28].
The ANOVA and Pareto analysis illustrated that
the linear terms of density and viscosity, as well as
the quadratic term of viscosity, had the highest effect
on the critical velocity. It was found that these terms
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Chem. Ind. Chem. Eng. Q. 22 (3) 309−318 (2016)
Figure 5. Normal probability plot of the residuals of the RSR.
have a more significant effect on the coalescence
compared to the other parameters. The dispersed oil
viscosity influences the flow of unstable emulsions
through the bed and dominance of the competitive
coalescence mechanisms.
As can be seen from the interdependence of the
critical velocity, bed permeability and viscosity
(Figure 6), the region of highest critical velocity can
be observed for high viscosity and high fiber bed permeability. In this region, the critical velocity is above
50 m/h.
effluent oil concentration were followed based on a
statistical approach using RSR, and several quadratic
nonlinear models were developed. Based on the RSR
and sigma-restricted parameterization, the effluent oil
concentration was found to be a function of fluid velocity, bed permeability, viscosity, density and neutralization number. Since the RSR and ANOVA analysis
demonstrated that the effluent oil concentration is
dominantly influenced by bed permeability, further
analysis was conducted to verify response surface
models over constant bed geometry. The response
surface models over constant bed permeability were
a responsive statistical method, with the calculated
multiple R higher than 90%.
An RSR equation was developed for predicting
the critical velocity. It was found that dispersed oil
density, viscosity, neutralization number and bed permeability influence significantly the oil removal efficiency of fiber bed coalescers. The region of highest
critical velocity can be observed for high viscosity and
high fiber bed permeability.
The presented mathematical models can be
used to predict the coalescer performances under a
great range of flow rates, bed permeabilities, and
nature of dispersed oil phase.
Acknowledgment
Figure 6. Three-dimensional plot representing the interdependence of the critical velocity, viscosity, and bed permeability.
CONCLUSION
In this work, the effects of dispersed oil properties, fluid velocity, and bed permeability on the
316
The work was supported by the Ministry of
Education, Science and Technological Development
of the Republic of Serbia, Grant number 172022.
NOMENCLATURE
K0 = bed permeability, m2
V = fluid velocity, m/h
D.D. GOVEDARICA et al.: SEPARATION OF OIL-IN-WATER EMULSIONS…
Vk = critical velocity for the effluent oil concentration of
Chem. Ind. Chem. Eng. Q. 22 (3) 309−318 (2016)
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15 mg/l, m/h
Ci = effluent oil concentration, mg/ l
Nb = neutralization number of dispersed oil, mg KOH/l
M = mean molecular weight of dispersed oil, kg/kmol
Greek letters
μ = dispersed oil viscosity, mPas
ρ = dispersed oil density, kg/m3
γ = surface tension, mN/m
σ = interfacial tension, mN/m
317
D.D. GOVEDARICA et al.: SEPARATION OF OIL-IN-WATER EMULSIONS…
DRAGAN D. GOVEDARICA1
RADMILA M.
1
ŠEĆEROV SOKOLOVIĆ
OLGA M. GOVEDARICA1
DUNJA S. SOKOLOVIĆ2
SNEŽANA V.
1
SINADINOVIĆ-FIŠER
1
Tehnološki fakultet Novi Sad,
Univerzitet u Novom Sadu, Bulevar
cara Lazara 1, 21000 Novi Sad, Srbija
2
Fakultet tehničkih nauka, Univerzitet u
Novom Sadu, Trg Dositeja Obradovića
6, 21000 Novi Sad, Srbija
NAUČNI RAD
Chem. Ind. Chem. Eng. Q. 22 (3) 309−318 (2016)
SEPACIJA EMULZIJE ULJE U VODI
KOALESCENCIJOM U VLAKNASTOM SLOJU:
METODOLOGIJA ODZIVNE POVRŠINE
U ovom radu ispitivane su mogućnosti primene metodologije odzivne površine na separaciju ulja iz emulzije tipa ulje u vodi. Kao filtarski materijal korišćena su vlakna polietilen
tereftalata. Cilj je bio da se analizira i proceni izlazna koncentracija dispergovane faze i
kritična brzina u širokom opsegu radne brzine, permeabilnosti sloja vlakana i prirode
dispergovane uljne faze. Razvijene su regresione jednačine metodologijom odzivne
površine pri konstantnoj permeabilnosti sloja koje su pouzdane i imaju koeficijent korelacije R viši od 90%. Utvrđeno je da gustina, viskoznost, i neutralizacioni broj ulja kao i
permeabilnost sloja značajno utiču na efikasnost separacije dispergovanog ulja. U
regionu visoke viskoznosti ulja i visoke permeabilnosti sloja nalazi se maksimalna vrednost kritične brzine. Modeli razvijeni medologijom odzivne površine mogu se primeniti i
na koalescentnu filtraciju da se prognozira efikasnost separacije, kao i da se optimizuje
efikasnost separacije pri simultanim promenama permeabilnosti sloja, intenziteta hidrodinamičkih sila i prirode dispergovane uljne faze.
Ključne reči: separacija tečno-tečno, zauljena voda, koalescencija u vlaknastom
sloju, vlakna, kritična brzina, metodologija odzivne površine.
318