A shrinking core model and empirical kinetic approaches in supercritical CO2 extraction of safflower seed oil

J. of Supercritical Fluids 94 (2014) 81–90

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A shrinking core model and empirical kinetic approaches in supercritical CO2 extraction of safflower seed oil Nezihe Ayas ∗ , Ozlem Yilmaz Anadolu University, Faculty of Engineering, Department of Chemical Engineering, 2 Eylul Campus, 26470 Eskis¸ehir, Turkey

a r t i c l e

i n f o

Article history: Received 4 February 2014 Received in revised form 18 June 2014 Accepted 19 June 2014 Available online 9 July 2014 Keywords: Carthamus tinctorius L. Supercritical fluid extraction Experimental design Optimization Modeling Shrinking core model

a b s t r a c t Utilization of supercritical CO2 in safflower seed extraction was performed using a semi-batch extractor. Different extraction parameters, such as 40–60 MPa pressure, 323–347 K temperature, 20–76 min time, and 1–3 mL/min CO2 flow rate were applied. A two-stage experimental design application was performed in order to maximize the oil yield. First of all, a 32 factorial design was applied to estimate the effect of the main factors and their interactions. The second part of the experimental design was improved and accelerated using the steepest ascent method. Optimum extraction parameters were determined to be 50 MPa pressure, 347 K temperature and 76 min time at a constant CO2 flow rate (3 mL/min) according to the 22 design. Under these conditions, the oil yield obtained was 39.42%, comparable with Soxhlet extraction (40%) for 8 h. Shrinking core and empirical kinetic models were applied in order to generalize the extraction process. The predicted data was compatible with the experimental data. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Safflower (Carthamus tinctorius L.) has become a progressively important crop due to its stability and adaptability to different environmental conditions, as well as its high nutritional content [1,2]. Pharmacological studies have demonstrated its biological activity, such as antioxidant, anticoagulantion and antihypertension properties [3]. The seed oil content of safflower ranges from 20 to 45% [4]. Safflower seed oil has beneficial effects on human health due to its vitamin E content, flavonoids, conjugated linoleic acid, amino acids and trace elements. Therefore, it is considered to be one of the best edible oils [5]. It has become an increasingly important crop in Turkey and throughout the world due to the rich nutritional value of its edible oil [2]. Turkey has a major deficit in oil seed production to meet its current needs; only 35–40% of oil seed needs are met by crops grown in Turkey. Every year Turkey produces 0.6 million tons of edible oil compared to a consumption rate of 1.50 million tons. Safflower has a potential to meet much of Turkey’s oil needs [6]. Since it is resistant to drought conditions, it could be grown successfully on the dry lands of Central Anatolia and surrounding regions with insufficient precipitation, such as

∗ Corresponding author. Tel.: +90 3350580 6508/+90 2223350580x6508; fax: +90 222323 9501. E-mail addresses: [email protected] (N. Ayas), [email protected] (O. Yilmaz). http://dx.doi.org/10.1016/j.supflu.2014.06.019 0896-8446/© 2014 Elsevier B.V. All rights reserved.

the Ankara, Eskis¸ehir, Konya and C¸ankırı provinces [4]. Standard safflower oil contains about 6–8% palmitic acid, 2–3% stearic acid, 16–20% oleic acid, and 71–75% linoleic acid. Additionally, different sources of variation for very high oleic acid content (>85%) have been reported. The fatty acid composition of vegetable oil determines its best commercial uses [7]. High-oleic safflower oil retains the light color and flavor characteristics of normal safflower oil, but the oxidative stability has increased by three and a half times [8]. Oleic acid is thermal-oxidation stable due to the presence of only one double bond in its molecular structure. This high oleic refined oil is of special interest directly in the food industry or after transformation into a suitable breed of industrial applications (bicarbonates, bisulphate, bio-lubricants solvent, hydraulic drilling fluid, dispersing agent, cosmetics, etc.) [9]. There are several methods, such as Soxhlet, accelerated solvent, sonication-assisted, microwave-assisted and supercritical fluid extractions used to extract seed oil [10,11]. Supercritical fluid extraction (SFE) offers several advantages over conventional solvent extraction methods. Supercritical fluids can penetrate into the pores of solid materials more effectively than techniques based upon liquid solvents and therefore, it enables a much faster mass transfer, resulting in faster extraction [12]. Carbon dioxide is the most favored fluid due to it being non-toxic, non-explosive, nonflammable, readily available and easily removed from extracted products. Due to its mild critical temperature (304.1 K), thermal degradation can also be avoided. It also has good solvent properties for the extraction of non-polar components, such as hydrocarbons

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Nomenclature C Ci Csat Cm C∞ D12 DL De dp kf k L q q0 q¯ r rc R t u U/S

v z ϕ M2 T    V¯ 1 EKM SC–CO2 SCM SEM SFE PUFA

solute concentration in the fluid phase in the bed volume, (kg/m3 ) solute concentration in the pore volume, (kg/m3 ) saturation concentration of solute in the fluid phase, (kg/m3 ) safflower seed oil concentration at time t, (kg/m3 ) safflower seed oil saturation concentration, (kg/m3 ) 2 binary diffusivity coefficient,    (m /s) D12 = 1.173 × 10−16 (ϕM2 )0.5 T/ V¯ 10.6 ) axial dispersion coefficient, (m2 /s) effective diffusivity, (m2 /s) mean particle diameter, (m) film mass-transfer coefficient, (m/s) extraction coefficient bed length, (m) solid phase concentration, (kg/m3 ) initial solid phase concentration, (kg/m3 ) average solid phase concentration, (kg/m3 ) radial coordinate in particle, (m) radius of the core, (m) radius of the solid particle, (m) time (s) superficial velocity, (m/s) unsaturated/saturated index interstitial velocity of solvent in the bed, (m/s) axial distance, (m) solvent association factor molecular weight of solvent, (kg/kmol) absolute temperature, (K) fluid viscosity (kg/m s) bulk density, (kg/m3 ) true density, (kg/m3 ) molar volume of solute empirical kinetic model supercritical CO2 shrinking core model scanning electron microscopy supercritical fluid extraction polyunsaturated fatty acid

Statistics Adj MS adjusted mean of square Adj SS adjusted sum of square degree of freedom DF F F test P significance level R-Sq sum of square R-Sq (adj) adjusted sum of square MS mean squared error S Standard deviation Seq SS sequential sum of squares xi experimental design factor coded variable Dimensionless groups a dimensionless interstitial velocity, ( R2 /De L) Csat /q0 b Pe peclet number (Pe = 2R/DL ) Re Reynolds number (Re = 2R/) Sc Schmidt number (Sc = /De ) Sherwood number (Sh = 2Rkf /De ) Sh

xx xxi   c ε εp

dimensionless concentration in the external fluid, (C/Csat ) dimensionless concentration in the pore of the particle, (Ci /Csat ) dimensionless time, (De t/R2 ) dimensionless radial coordinate in the particle, (r/R) dimensionless core radius in the particle, (rc /R) bed porosity porosity of the solid

[13–18]. SFE offers unusual possibilities for selective extractions and fractionations because the solubility of a chemical in a supercritical fluid can be manipulated by changing the pressure and/or temperature of the fluid [10]. Several researchers have studied SC–CO2 extraction of seed oil from a wide range of seed species, such as sesame [19], sunflower [20], soybean [21], hazelnut and walnut [22]. In addition, some authors have studied the optimization of the experimental conditions for SFE of different seed oils, such as rapeseed, linseed and soybean [12,15,23]. Mathematical models help to generalize the experimental results and formulate knowledge about process behavior. Moreover, they are useful in the development of scaling-up procedures from bench to pilot and industrial scales [24,25]. Mathematical models, which have no physical correspondence to the materials and the process studied, are of limited validity, although they can be used to fit certain experimental data. Three different approaches have been proposed for the mathematical modelling of SFE empirical, based on a heat and mass transfer analogy, and differential mass balances integration. The most proper analysis is obtained from the integration of differential mass balances: time dependent concentration profiles are obtained for the fluid and solid phases [24]. Han et al. [26] studied a mathematical model of Sovova’s extended Lack’s Model to define supercritical CO2 extraction from safflower seed oil. It is the only one conducted on the supercritical extraction of safflower seed. A maximum oil yield (<27%) was obtained at 308 K, 28 MPa, and 0.35 mm particle size [26]. The objective of this study is to determine the supercritical CO2 extraction parameters (flow rate, temperature, pressure and time) on the yield of safflower seed oil and to identify optimum extraction parameters by experimental design. Then in order to characterize the supercritical extraction of safflower seed oil, shrinking core and empirical kinetic models were applied. 2. Materials and methods 2.1. Materials The safflower seeds were supplied by the Institute of Anadolu Agricultural Research Center (Eskis¸ehir, Turkey). Prior to all of the experiments, the seeds were previously dehulled and milled to 0.50 mm particle size. All of the chemicals and solvents were purchased from the Sigma-Aldrich chemical company, apart from high purity carbon dioxide (99.95%). 2.2. Soxhlet extraction The total oil content of the seed was determined by Soxhlet extraction. The seeds were extracted by n-hexane (solid:solvent ratio of 1:6.6 (a/a)) for 8 h using a 250 mL capacity Soxhlet apparatus. The solvent was then evaporated under reduced pressure using a rotary evaporator at 313 K. The oil yield was calculated on

N. Ayas, O. Yilmaz / J. of Supercritical Fluids 94 (2014) 81–90

a dry material basis after determining the moisture content of the seeds [27]. 2.3. Supercritical fluid extraction A semi-continuous bench type supercritical fluid extractor (SFX 220, ISCO Inc., Lincoln, USA) was used to extract the safflower oil. The supercritical CO2 extraction system consists of a SFX-220 extractor, a SFX-200 controller D series syringe pump and restrictor. The maximum operating temperature and pressure of the system is 423 K and 68.95 MPa, respectively. 4 g safflower seed, average particle size of 0.50 mm, were charged into a 10 mL extraction vessel (5.75 cm height). Then liquid CO2 was pumped into the extractor at a flow rate of 1.0–3.0 mL CO2 /min. The effect of the extraction parameters on the oil yield was investigated involving time (0–60 min), temperature (323–333 K) and pressure (30–60 MPa). Each experiment was repeated at least twice. 2.4. Analytical methods 2.4.1. Determination of the moisture content of the raw material The moisture contents of the seed were determined using a volumetric water determination apparatus. Approximately 10 g of crushed kernel seeds and 150 mL of saturated xylene were placed into a flask. Water within the plant material is codistilled with the xylene and condensed in a graduated tube, with water collecting at the bottom of the tube below the solvent line. The solvent continuously returns to the distillation flask until the water level in the graduated part of the apparatus is constant [28,29]. 2.4.2. Gas chromatography analysis Prior to a gas chromatography analysis the safflower seed oil was converted to a fatty acid methyl esters form [28]. The fatty acid composition of the safflower seed oil was analyzed using an Agilent 6890N Network Gas Chromatography (GC) system coupled with a flame ionization detector (FID) with a capillary column (HP-Innowax, 60 m × 0.25 mm × 0.25 ␮m nominal) with helium used as a carrier gas at a flow rate of 1 mL/min. The injection and detector temperatures were adjusted, respectively, at 523 and 553 K. The initial temperature of the oven was 333 K for 10 min with the temperature program rate as follows: 16 K/min/493 K//493 K–10 min//2 K/min–513 K//513 K–50 min. The injection was performed in a split mode (60:1) and the injection volume was 2 ␮L [30]. 2.4.3. Morphological imaging Knowledge of the botanical aspects and/or optical microscope or scanning electron microscope (SEM) analysis of the material are necessary in order to visualize its structure. Particles can be spherical, plate-like, and so on, as a result of the original shape of the material and of the grinding process. The shape influences the diffusion of the supercritical solvent into the particle [24]. A structural analysis of non-extracted and extracted seed particles was carried out in a scanning electron microscope (SEM) (Zeiss Supra 50 VP). 3. Experimental design In order to determine the optimum extraction parameters (temperature, and time), an experimental design (factorial design analysis) was performed in two steps; 32 and 22 designs for a level of 95% confidence. The purpose of the full factorial design is to determine the importance of factors and the relationship between the factors influencing the process [31].

83

Table 1 32 factorial design parameters and levels. Factor

Variable

Level

Temperature [K] Time [min]

A B

323 20

328 40

333 60

For the first step, a 32 factorial design was applied to estimate the effect of the extraction variables and their interaction with each other (Table 1). Then a steepest ascent was applied due to the fact that operating conditions are normally far from optimum response settings. The model was a 22 factorial design with three center points. Replicates at the center were used to estimate any experimental error. Also, the design was centered about the current operating conditions in the process [32]. A statistical analysis of variance (ANOVA) of the experimental results was employed to determine the main effect and interaction of the factor effects using Minitab 14 statistical software. 4. Mathematical modeling Experimental results were generalized by mathematical models in order to simulate the extraction process. Mathematical models enable a generalization of the experimental results to new process conditions and extracted materials. Several approaches were applied. 4.1. Shrinking core model The shrinking core model (SCM) assumes that the extraction process is similar to irreversible desorption of a solute from a porous adsorbent, where the pores are assumed to be initially filled with the solute, followed by diffusion in the porous solid through the pores [33–35]. When the mass transfer rate of the solute in the non-extracted inner part is much slower than that in the outer part, where most of the solute has been extracted, or solute concentration is much higher than the solubility of the solute in the solvent phase, a sharp boundary may exist between the outer and inner regions. A core of the inner region shrinks with the progress of the extraction [17,36]. This model assumes that the solute inside the particle is located within a core that shrinks as the solute is extracted [33]. Where there is a sharp boundary within a particle between the extracted part and the non-extracted part, the shrinking core model may be useful. These situations can be modelled by SCM [36]. Most of these models include resistance in one or both of the bulk phases. They take into account particle and bed characteristics via porosity and diameter. Although the models imply many assumptions and/or determination of several coefficients involved in the equations, they reflect the various mechanisms that contribute to the overall behavior of an extraction process [37]. In this study, SCM was used in order to characterize the extraction process, after taking into consideration mass transfer mechanisms, such as diffusion and solubility. The following criteria were assumed: (i) All the components to be extracted behaved similarly in the mass transfer and therefore, could be described by a single component called a ‘solute’. (ii) Fatty acids were homogeneously distributed in the solid phase under initial conditions. (iii) The system is isothermal, isobaric. (iv) The matrix is a porous material where oil is uniformly distributed throughout the particle. (v) The physical properties of the supercritical fluid are constant during the extraction.

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(vi) Radial dispersion is neglected, Extraction is irreversible desorption, The extraction vessel is considered to be filled with homogeneous spherical particles of uniform size [36,38–40]. Based on these assumptions, the dimensionless material balancing in the bulk phase and solid phase are [17]:

2

∂C ∂C ∂ C (1 − ε) 3kf − + = DL (C − Ci (R)) ε R ∂t ∂z ∂z 2

(1)

The time variation of the solid phase concentration (average oil concentration in a particle) was equated with the rate of mass transfer of the solute within the external film surrounding the particle. 3kf ∂q¯ (C − Ci (R)) = R ∂t

(2)

Assuming a pseudo-steady state, the diffusion in the outer region is given by [35,41,42]; De ∂ r 2 ∂r

r2

∂Ci ∂r



=0

(3)

(ii) Solid phase; Solid phase solute exists within the core.

 r 3

q¯ = q0

c

(4)

R

Boundary conditions are given as follows: At the core boundary the concentration in the fluid phase is at its saturation value. Ci = Csat

at r = rc

(5)

Diffusion flux at the outer surface of a particle is equal to mass transfer through the external film.



De



∂Ci ∂r

= kf (C − Ci (R))

∂C = 0 at z = 0 ∂z

(7)

∂C = 0 at z = L ∂z

(8)

C = 0 at

(9)

Ci = Ci,0 rc = R

t=0 at t = 0

(10)

t=0

(11)

at

The following dimensionless groups are defined to derive dimensionless formulas of the fundamental equations. xx =

C , Csat

a=

R2 , De L

y¯ = c3

(15)

Boundary and initial conditions are:

xxi = =

Ci , Csat De t , R2

xx0 = y¯ =

1 ∂xx = 0 at Pe ∂Z

Ci,0 , Csat

q¯ , q0

b=

=

r , R

Z=

Csat q0

z , L

(16) (17)

xx = 0 at

=0

(18)

xxi = xxo ,

=0

(19)

c = 1,

=0

(20)

These differential equations coupled with boundary and initial conditions were solved numerically by MAPLE 17 software. 4.1.1. Model parameters Mass transfer coefficients are influenced by variables, such as diffusivity, viscosity, density, solvent flow rate, and porosity of the fixed bed. The characteristic behavior of these variables can be expressed in terms of dimensionless numbers; i.e., the Reynolds number (Re), Sherwood number (Sh) and Schmidt number (Sc). Correlation models were developed to predict the mass transfer coefficient using dimensionless grouped numbers during a supercritical extraction process [43]. The mass transfer coefficient, kf , is a function of the solvent velocity, density, viscosity and the binary diffusion coefficient (D12 ) of the solute in the solvent [35]. Sh was estimated with the correlation of Mongkholkhajornsilp et al. correlation [17]. The axial dispersion coefficient in the supercritical phase was estimated using empirical correlation [36]. The diffusion coefficient, D12 , was calculated using the following equation [43]. D12 = 1.173 × 10−16

(ϕM2 )0.5 T V¯ 0.6

(21)

1

ϕ denotes the solvent association factor, M2 denotes its molecular weight (kg/kmol), T denotes the absolute temperature (K),  denotes the solvent viscosity (Pa s) and V¯ 1 denotes the molar volume of the solute. D12 , containing parameters characteristic of random molecular motion within phase, is a function of T, , ϕ, M2 , and V¯ 1 . This increases with absolute temperature, but decreases with the solvent viscosity and molar volume of the solute. Higher diffusion coefficients in supercritical CO2 give rise to a more rapid extraction. The effective diffusivity in the particle is given by [34]: De = ε2p D12

(22)

In Eq. (22), the porosity was calculated using the following equation derived at by Mohsenin in 1986 [5,44]. The true density () was determined using the toluene displacement method [44]. Dimensionless groups are defined to derive dimensionless formulas of fundamental equations. Model parameters were used in the SCM to predict oil yield by solving the fundamental equations.

(12) 4.2. Empirical kinetic model (EKM)

After several manipulations of dimensionless equations, variations of dimensionless concentration and dimensionless core radius are given by: 2

∂xx ∂xx 3Bi (xx − 1) a ∂ xx (1 − ε)    − +a = Pe ∂Z 2 ε ∂ ∂Z 1 − Bi 1 − 1/c

Z=0

∂xx = 0 at Z = 1 ∂Z

(6)

r=R

Initial conditions are given as follows. C − DL

(14)

xx −

(i) Fluid phase;



∂c bBi(x − 1)     = 2 ∂ c 1 − Bi 1 − 1/c

(13)

In order to describe the extraction process and the assessment of the parameters’ effect on the extraction efficiency and product quality, different types of model can be attributed to the variations in the target compounds and structures of natural sources, as well as the type of process [45]. Within this aim, an empirical kinetic model is used. A careful examination of the extraction kinetic

N. Ayas, O. Yilmaz / J. of Supercritical Fluids 94 (2014) 81–90 Table 2 Fatty acid composition of safflower seed oil.

85

Table 3 Extraction conditions and SCM parameters.

Fatty acid

%

P [MPa]

T [K]

kf × 107 [m/s]

DL × 103 [m2 /s]

De × 1011 [m2 /s]

Re

Palmitic (C16:0) Stearic (C18:0) Oleic (C18:1) Linoleic (C18:2) Linolenic (C18:3) (C20:0) Arachidic  Saturated

7.41 2.83 37.85 44.62 5.60 1.69 11.93

30

Unsaturated Unsaturated/saturated

88.07 7.38

323 333 323 333 323 333 323 333 323 333

2.24 2.51 1.97 2.18 1.78 1.96 1.71 1.87 1.64 1.80

2.18 1.78 2.81 2.34 3.43 2.88 3.74 3.15 4.06 3.43

1.55 1.81 1.30 1.49 1.14 1.29 1.08 1.22 1.02 1.15

0.82 0.86 0.76 0.79 0.71 0.75 0.69 0.72 0.67 0.70



40 50 55 60

data reveals that the oil concentration in the bulk liquid solution initially increases very quickly, and then slowly approaches a saturation value. This phenomenon suggests that the following empirical model may describe the extraction kinetics adequately: dCm = k(C∞ − Cm )n dt

Cm = 0



Cm = C∞ 1 − e

 −kt

(24) for

n=1

(25)

Cm = C∞ − for

1/n−1 



1

(n − 1) k t + C∞ /enIn(C∞ ) k (n − 1)

n= / 1

 (26)

The parameters of the mathematical model were calculated by a Microsoft Excel Solver based on the generalized reduced gradient method to optimize nonlinear problems. These parameters were estimated by minimization of the mean squared error (MS).

2 1  Cm,exp − Cm,cal n n

MS =

Obtained oil yields were compared with the results of the model. The extraction process could be modeled using shrinking core and empirical kinetic models.

(23)

where k is the extraction coefficient, n is the extraction order, and Cm and C∞ are safflower seed oil concentrations at time t, and saturation concentration, respectively [46]. The first-order ordinary differential equation was solved by MAPLE 17. t = 0,

5.2. Mathematical modeling

(27)

i=1

5. Results and discussions The moisture content of the seed was determined as 2.25%. In this study, the initial oil content of safflower seed was determined as 40.0% on a dry material base by Soxhlet extraction. The oil was analyzed by GC (Table 2). The fatty acid components characterized in the oil were palmitic, stearic, oleic, linoleic, linolenic, and arachidic. Of them, the main fatty acid component was linoleic acid. The U/S [unsaturated/saturated] index, 7.38, considered a taxonomic marker, is presented in Table 2 [47]. The high content of PUFA in the oil suggests a possible use as a food additive; such acids have been reported as helping to lower blood cholesterol. 5.1. Supercritical fluid extraction Supercritical CO2 extraction of seed oil was carried out under various extraction conditions. All the extraction data’s is expressed as % w/w (extracted oil/starting material) on a dry material basis.

5.2.1. Analyses of the extraction curves by the shrinking core model The values of the critical properties of CO2 were obtained at the NIST library [48]. The mass transfer coefficient in a packed bed extractor and the axial dispersion coefficient in the bulk phase were estimated using empirical correlations. The solubility of safflower seed oil in supercritical CO2 was obtained experimentally as the slope of the initial linear region of the extraction curves. Then differential equations, coupled with boundary and initial conditions, were solved using MAPLE 17. The calculated diffusion and mass transfer coefficients and the Re numbers are tabulated in Table 3. The bed void and raw material void fractions were found to be 0.63 and 0.55, respectively. 5.2.1.1. Effect of solvent flow rate. Fig. 1 reports the effect of a supercritical CO2 flow rate on the oil yield at three levels of 1, 2 and 3 mL/min, while keeping the temperature (323 K) and pressure (30 MPa) constant. Results show that oil yield depends to a large extent on the flow rate of supercritical CO2 . A faster extraction rate was observed at a high CO2 flow rate. The highest oil yield (25.87%) was obtained with the 3 mL/min flow rate of CO2 for a 120 min extraction time. At low flow rates of the solvent, the mass transfer resistance limits the amount of solute transported into the bulk of the solvent and the supercritical carbon dioxide leaves the extractor unsaturated. An increase in flow rate leads to a reduction of mass transfer resistance surrounding the solid particles based on a reduction of the thickness of the film layer around the solid particles and hence, a maximum yield was attained [36,49]. Furthermore, the increasing CO2 flow rate generally causes an increase in the number of CO2 molecules per unit volume entering the extractor, thus increasing inter-molecular interaction between CO2 and the solute, thereby increasing the solute dissolution [33]. As it can be seen, the higher the total CO2 passed through bed, the higher are the oil yields. A pressure deviation was observed after the CO2 flow rate exceeded 3 mL/min was therefore kept constant for further experiments. The predicted oil yields by the model were compared with the experimental results at a flow rate of 1–3 mL/min in order to test the SCM in Fig. 1. The lines indicate the predicted curves and the points show those of the experimental data. Fig. 1 shows that the model and the experimental data are in excellent agreement for a 100 min period, where the SFE is controlled by the solubility of the oil. Therefore, the SCM is a useful tool for safflower seed extraction.

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a)

a)

30

35 T= 323 K 30

25

SCM-1 mL/min

Yield, %

Yield, %

Model-30 MPa

25

20 Exp.-1 mL/min

15

SCM-2 mL/min

10

Exp.-2 mL/min

100

Model-50 MPa Exp.-50 MPa Model-55 MPa Exp.-55 MPa

5

0 50

Exp.-40 MPa

15

Exp.- 3 mL/min

0

Model-40 MPa

20

10

SCM- 3 mL/min

5

Exp.-30 MPa

Model-60 MPa Exp.-60 MPa

0

150

0

20

Time, min

b)

60

Time, min b)

30

40 T=333 K 35

25

Model-30 MPa

30

20 15

1 mL/min 2 mL/min

10

3 mL/min

Yield, %

Yield, %

40

Exp.-30MPa Model-40 MPa

25

Exp.-40 MPa

20

Model-50 MPa Exp.-50 MPa

15

5

10

0

5

Model-55 MPa Exp.-55 MPa Model-60 MPa

0

100

200

300

400

TotalCO 2 passed through bed (kg) Fig. 1. (a) Effect of CO2 flow rate on the oil yield at 323 K and 30 MPa, (b) variation of oil yield with the total weight of supercritical CO2 that has been passed through the packed bed extractor at 323 K and 30 MPa.

5.2.1.2. Effect of pressure and temperature. The variation in oil yield through a change in pressure (30 MPa to 60 MPa) and temperature (323 to 333 K) is given in Fig. 2a and b. Predicted values were compared with the experimental data in order to test the SCM. Fig. 2a and b shows the oil yield obtained at a pressure of 30–60 MPa and an extraction time of 0–60 min. The oil yield dramatically increases to 31.92% at 50 MPa and 323 K in 60 min. Then a gradual decrease is observed after 50 MPa. The oil yield increased linearly at 30 MPa pressure, since all the oil content had not been extracted after 60 min. When the pressure increased to 40 MPa, the oil yield increased polynomial with time. At a constant temperature, the mass transfer coefficient decreased, but the solubility of the oil increased with increased pressure. At a constant temperature, increasing the pressure causes higher SC–CO2 density, and therefore, improves the solubility of oil. Ultimately, it allows the SC–CO2 to contact more surface area and leads to a higher oil yield of up to 50 MPa, but any further increase in pressure decreases the yield due to the crossover effect of a decrease in mass transfer coefficient. The effective diffusivity and film mass transfer coefficients decreased, but the axial dispersion coefficient increased with increased pressure, resulting in a lower oil yield. An axial dispersion or back mixing of the fluid flow is not desirable, since it leads to ineffective extraction. Ideally, it is desirable to have a plug flow of the SC–CO2 across the bed [50]. For this reason, supercritical CO2 did not disperse into solid particles easily and decreasing the particle size may lead to solvent channeling [33,36,51–53]. Fig. 2a and b illustrates that there is a moderate increase in oil yield not only with time, but at all temperatures, also peaking at 333 K with the yield of 37.21%. No further increase in temperature was applied, since the oil yield approached the total (40.0%) oil yield obtained by Soxhlet extraction. Two different factors

Exp.-60 MPa

0 0

20

Time, min

40

60

Fig. 2. Effect of pressure and time on oil yield with comparison to SCM using a flow rate of 3 mL/min (a) at a temperature of 323 K, (b) at a temperature 333 K.

significantly contribute to the crossover effect, density and volatility. An increase in temperature decreases the density at constant pressure, which would lower the solubility, thereby decreasing the oil yield. The opposing effect is the volatility effect. At high pressures, the density dependence on temperature is small compared with the effect of vapor pressure, which results in increased solubility [54,55]. Moreover, increasing the temperature increases the mass transfer coefficient. Also, the effective diffusivity increased, but the axial dispersion coefficient decreased with increasing temperature, resulting in a higher oil yield. Fig. 2a shows that the model and experimental data are in excellent agreement and show a consistent trend. However, there is a slight deviation at 323 K and 40 MPa pressure up to 30 min. The linear part of the curves were significantly influenced by the solubility, and the latter part was almost controlled by the diffusivity. Fig. 2b shows the low deviation between experimental and predicted curves. Consequently, according to Figs. 1 and 2a and b SCM gives an acceptable correlation with the experimental results of safflower seed oil extraction by supercritical CO2 . When we compare the results with previous work, it is clear that the used model is more suitable, especially using all the flow rates of the supercritical CO2 . It can be concluded that the proposed model described the experimental data as consistent. At a constant pressure, the axial dispersion coefficient decreased while the effective diffusivity, mass transfer coefficient and saturation concentration increased with increasing temperature. For this reason, supercritical CO2 shows better penetration behavior in solid particles [36]. 5.2.2. Analyses of the extraction curves by the empirical kinetic model An empirical kinetic model (EKM) of safflower seed oil using supercritical CO2 was used to fit the experimental data. The predicted EKM parameters and the mean square error values of the

N. Ayas, O. Yilmaz / J. of Supercritical Fluids 94 (2014) 81–90

CO2 flow rate [mL/min]

n

k

MS

1 2 3

1.155 1.076 1.051

0.016 0.025 0.031

0.122 0.729 2.357

35

a)

T= 323 K 30 Model-30 MPa

25 Yield, %

Table 4 Calculated parameters and mean squared error for EKM for 1–3 mL/min at 323 K and 30 MPa.

87

Exp.-30 MPa Model-40 MPa

20

Exp.-40 MPa Model-50 MPa

15

Exp.-50 MPa

10

Table 5 Calculated parameters and mean squared error for EKM for all temperatures and pressures at 3 mL/min. T [K]

n 0.293 0.498 2.17 1.124 2.182 1.652 2.235 2.416 2.321 2.526

323 333 323 333 323 333 323 333 323 333

30 40 50 55 60

k

0.018 0.038 0.823 0.669 0.204 0.044 0.246 0.939 0.241 0.982

EKM for all flow rates, temperatures and pressures are given in Tables 4 and 5. While k (extraction coefficient) value increases with flow rate, n (extraction order) and decreases. Therefore, oil concentration increases in the supercritical CO2 phase. The effect of CO2 flow rate on oil yield compared to the EKM at a temperature of 323 K and 30 MPa is given in Fig. 3. The predicted values were compared with the experimental data at a pressure of 30–60 MPa and a temperature of 323 and 333 K in order to test the EKM model (Fig. 4a and b). The extraction kinetic curves of different extraction conditions are in good agreement with the experimental data as can be seen in Figs. 3 and 4a and b. A shrinking-core model that cannot give model parameters shows a significant trend, and the empirical kinetic model results in model parameters that have a good correlation with the oil yield [45]. 5.3. Morphology of safflower seed Figs. 5 and 6 show the SEM morphology of non-extracted and extracted safflower seed using SFE, respectively. The rigidity of the non-extracted and extracted samples was investigated according to SEM images (Figs. 5 and 6). In Fig. 6, the porosity of the treated seeds easily shows that oil was extracted.

Model-60 MPa Exp.-60 MPa

0

MS

0.131 0.076 0.0005 0.025 0.00113 0.0122 0.00084 0.00076 0.00061 0.00048

Exp.-55 MPa

5

0

10

20

30 40 Time, min

50

60

40

b)

T= 333 K

35

Model-30 MPa

30

Exp.-30 MPa

Yield, %

P [MPa]

Model-55 MPa

25

Model-40 MPa

20

Exp.-40 MPa Model-50 MPa

15

Exp.-50 MPa

10

Model-55 MPa Exp.-55 MPa

5

Model-60 MPa Exp.-60 MPa

0 0

10

20

30 40 Time, min

50

60

Fig. 4. Effect of pressure and time on oil yield with comparison to EKM using a flow rate of 3 mL/min (a) at a temperature of 323 K, (b) at a temperature of 333 K.

These enhancements increased lipid disolvation in the SC–CO2 , and hence, the oil yield [53]. 5.4. Experimental design An analysis of variance is given in Table 3, in which A, and B indicate temperature and time, respectively. The P-values of the main effects are significant at the 5% level. R-Sq and R-Sq (adj) values indicate that the accuracy and general availability of the model are adequate (Table 6). An R-Sq (adj) value close to 100% means that it is a suitable model with excellent predictive value [56,57].

30

Yield, %

25 20 Exp.-1 mL/min Model-1 mL/min

15

Exp.-2 mL/min

10

Model-2 mL/min Exp.- 3 mL/min

5

Model- 3 mL/min

0 0

50

100

150

Time, min Fig. 3. Relationship between experimental and predicted value’s for different flow rates using EKM.

Fig. 5. SEM image of non-extracted safflower seed particle.

88

N. Ayas, O. Yilmaz / J. of Supercritical Fluids 94 (2014) 81–90 Table 7 In comparison to experimental and predicted oil yields for second step design.

Fig. 6. SEM image of extracted safflower seed particle.

Temperature [K]

Time [min]

Experimental yield [%]

Predicted yield [%]

323 323 323 323 323 323 328 328 328 328 328 328 333 333 333 333 333 333

20 20 40 40 60 60 20 20 40 40 60 60 20 20 40 40 60 60

24.44 24.30 30.45 31.17 32.34 31.50 26.82 26.73 32.18 33.11 33.24 34.40 31.20 31.86 36.25 36.38 36.97 37.44

25.53 25.53 28.91 28.91 32.29 32.29 28.52 28.52 31.90 31.90 35.28 35.28 31.51 31.51 34.89 34.89 38.27 38.27

Table 6 Variance analysis of oil yields, according to the temperature and time. Source

DF

Seq SS

Adj SS

Adj MS

F

A B Error Total S R-Sq R-q (adj)

2 2 13 17

110.973 158.487 4.295 273.755

110.973 158.487 4.295

55.486 79.244 0.330

167.94 239.85

75

Yield < 37.0 - 37.5 - 38.0 - 38.5 - 39.0 > 39.0

37.0 37.5 38.0 38.5

70

P 0.000 0.000

65

Time

60 0.574793 98.43% 97.95%

55 50 45

According to Table 6, oil yield is a function of time and temperature. Eq. (28) was obtained as a function of time and temperature by a regression analysis. Yield (%) = −171 + 0.598A + 0.169B

40 338

1

− 328 5

and

(28)

− 40 20

2

x2 =

(30)

0.169 x2 = = 0.28 (time) 0.598

(31)

344

346

Fig. 7. Contour plot of yield as a function of temperature and time.

Depending on calculated x1 and x2 values, a new experimental design is shown in Table 8. There is an increment in response up to the 3rd step, after which a decrement was observed. The turning point was calculated as 2.83 according to the variation of yield in relation to the step [32]. The values of temperature and time at the turning point are 342 K and 56 min, respectively. At this point a 22 design with 3 center points was applied. The extraction conditions and yield are summarized in Table 9. A variance analysis of oil yield is given in Table 10. A contour plot of the oil yield as a function of time and temperature is given in Fig. 7. According to Fig. 7, the contour lines (oil yield %) change from 37.0 to 39.0%. The contour lines are parallel with the axis of time, which indicate that insignificant interaction exists between these two variables. Consequently, the optimum extraction conditions

(29)

x1 = 1 (temperature)

342

Temperature

Eq. (28) gives the predicted oil yield, which increases with temperature and time. This was compared with experimental yields (Table 7). The regression model shows that time and temperature both affect yield in a positive way. The steepest ascent method was used in the direction of maximum increases in oil yield. To move away from the design center, 328 K and 40 min (x1 = 0, x2 = 0), along the path of steepest ascent, we could move 0.598 units in an x1 direction for every 0.169 units in an x2 direction from Eq. (28). Thus, the path of steepest ascent passes through the point (x1 = 0, x2 = 0) and has a slope of 0.169/0.598 [32]. Therefore, the steps along the path of steepest ascent are: x1 =

340

Table 8 Process data for fitting the first order model. Origin

0 Origin + Origin + 2 Origin + 3 Origin + 4

Natural variables

Coded variables

x1

x2

0 1.00 1.00 2.00 3.00 4.00

0 0.28 0.28 0.56 0.84 1.12

Response extraction yield [%]

1

2

328 5.0 333 338 343 348

40 5.6 45.6 51.2 56.8 62.4

32.65 36.37 38.22 38.86 37.78

N. Ayas, O. Yilmaz / J. of Supercritical Fluids 94 (2014) 81–90

89

Table 9 Data for second first-order model. Temperature [K]

Time [min]

x1

x2

Oil yield [%]

337 337 347 347 342 342 342

36 76 36 76 56 56 56

−1 −1 1 1 0 0 0

−1 1 −1 1 0 0 0

36.89 37.32 39.02 39.42 38.21 38.65 37.96

Table 10 Variance analysis of yield (22 augmented by three center points). Source

DF

Seq SS

Adj SS

Adj MS

F

Main effects 2-Way Curvature Residual error Pure Error Total S R-Sq R-Sq(adj)

2 1 1 2 2 6

4.64545 0.00023 0.02106 0.24407 0.24407 4.91080

4.64545 0.00023 0.02106 0.24407 0.24407

2.32273 0.00023 0.02106 0.12203 0.12203

19.03 0.00 0.17

were determined as 347 K temperature and 76 min extraction time, with a 39.42% oil yield by experimental design. 96.33% of the total oil yield was recovered under optimum extraction conditions (347 K and 76 min) determined by experimental design.

P 0.050 0.970 0.718

0.349333 95.03% 85.09%

Acknowledgment The authors would like to thank AUBIBAM (Anadolu University Plant, Pharmaceutical and Scientific Research Center) for allowing the use of SFE in their laboratory.

6. Conclusions References In this study, oil was extracted from dehulled safflower seed by SC–CO2 at various temperatures, pressures and CO2 flow rates. In terms of extraction time, SC–CO2 takes less time than Soxhlet extraction. The recovery of oil was 96.33%, which is in good agreement with the literature [26]. However, the oil yield (40%) is higher than Han et al.’s [26] study (29%), and linoleic acid was determined as the main fatty acid compound at 44.62%, whereas it was 81.47% in the study of Han et al., since oil content and composition vary considerably depending on the genotype, location, cultivation, dehulling and climate. Based on the experimental design, the optimal conditions for safflower seed oil yield within the experimental range were found to be 347 K, 50 MPa, and 76 min with the predicted oil yield found to be 38.53%. Under these optimal conditions, the experimental values were in good agreement with the predicted values. Although pressure and temperature are higher than the published data (308 K and 28 MPa, 4 h), extraction time is significantly lower [26]. SCM and EKM were applied to experimental data in order to generalize the extraction process. For the SCM, several empirical equations were used in order to obtain adjustable parameters of the model, which are the effective diffusivity (De ), mass transfer coefficient (kf ) and axial dispersion coefficient (DL ) of the bed. The effective diffusivity and mass transfer coefficient decreased with increasing pressure, decreasing binary diffusivity and increasing the axial dispersion coefficient. The effect of temperature on the adjustable parameters was in opposition to the effect of pressure [58]. According to the findings, SCM fitted well with the experimental data. The extraction coefficient (k) and the order (n) were determined using EKM. The extraction order increased with temperature and pressure and therefore, the extraction rate increased and extraction time decreased. Both models can interpret the extraction process satisfactorily.

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