Liquid–Liquid and Vapor–Liquid–Liquid Equilibrium in Food Processes

CHAPTER 7

Liquid
Liquid and Vapor
Liquid
Liquid Equilibrium in Food Processes Guilherme J. Maximo, Marcela C. Ferreira, Simone Shiozawa, Larissa C.B.A. Bessa, Antonio J.A. Meirelles and Eduardo A.C. Batista

7.1 INTRODUCTION The development and optimization of several food processes are based on the comprehension of the liquid
liquid or vapor
liquid
liquid equilibrium (VLLE) behavior of the systems involved. In these processes, when a certain pressure, temperature, and concentration conditions (P
T
x) are applied, the food biocompounds can be distributed in two or more liquid phases, and in certain cases, also in a vapor phase. This is the case of several separation processes in the food industry, in which the addition of a solvent, and the application of a special condition of temperature and/or pressure changes the equilibrium state. Therefore, the biocompounds can be extracted from the system, partially or almost totally, by their distribution among the phases, allowing their fractionation. The importance of the liquid
liquid equilibrium (LLE) in the food industry is evidenced by the processes in which two liquid phases are involved. The deacidification of vegetable oils, for example, is normally based on adding NaOH, followed by the soap production and removal. However, the addition of greener solvents, such as ethanol, has been shown good results in order to remove free fatty acids (FFAs) but maintaining the nutraceutical compounds of the feedstocks. The extraction of biocompounds generated during fermentation processes by using the solvent extraction is another important example in the food industry. This is the case of the production of organic acids such as citric or lactic acid

Thermodynamics of Phase Equilibria in Food Engineering

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used as food additives. In a similar way, adding solvents for liquid
liquid extraction is an interesting strategy for recovering phytochemicals from plant matrices or aroma compounds from evaporated streams. The transesterification of vegetable oils by using short-chain alcohols for the production of fatty acid alkyl esters are also a process realized in a two-phase system. In this case, the LLE data indicates the best strategies for the removal of byproducts, such as glycerol and alcohol in excess. VLLE systems are basically observed in the production of distilled beverages. The design of distillation processes is based on the relative volatility of the compounds, which is related to the thermodynamic equilibrium established. This is quite important because the distillation system is a multicomponent mixture in which some compounds must be removed in order to achieve the quality standards. A fraction of these compounds, especially represented by higher alcohols, also called fusel oil, establishes a VLLE behavior in the system. Whether in beverage distillation or production of neutral alcohol, used in food formulation, the removal of higher alcohols is achieved by the knowledge of the respective VLLE data. Fig. 7.1 represents typical LLE and VLLE diagrams observed in food systems involved in separation processes. They are the most found phase diagrams, despite other behaviors can also be observed. This will be discussed on the following topic. The LLE data is basically essential for the design of liquid
liquid extraction processes. In this process, two streams are generally generated in the extraction equipment. The extract stream is the stream rich in solvent, and concentrated in the target biocompounds. The raffinate stream is the other phase that after the process should present a lower concentration of the target biocompounds. Fig. 7.1A presents an LLE diagram of a binary system. This diagram is generally used in order to evaluate the solubility of a compound in a solvent or the solubility of a compound in a system. This information is quite important both for process design or product formulation. In this diagram, the behavior of the system is represented as a function of temperature and concentration. There are two main regions in this diagram: one-phase or homogenous area and two-phase or heterogeneous area. The two-immiscible phase region is the area limited by the solubility curve called binodal. On the other hand, the one miscible phase area is outside this limit. Fig. 7.1B shows a classical LLE diagram of a ternary or pseudoternary system at a constant temperature and pressure. It may represent, for example, a food system A
B mixed with a solvent C. The overall idea is the same of the binary system. It means that homogeneous 276

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Liquid Equilibrium in Food Processes

(A)

(B) One-phase region

One-phase region -p One

One-phase liquid region rich in A

in C

One regi phase li on r ich quid in B

rich

se ha o-p n Tw regio

on regi

liquid phase One- rich in A n regio

Two-phase region

uid

xB

e liq

h as

T

xC

xA (C) Vapor phase

Vapor + liquid rich in B

Heterogeneous azetropic point

Vapor + liquid rich in A

Two-phase region

liquid phase One- rich in A n regio

One regi phase li on r ich quid in B

T

xA , yA

Figure 7.1. Typical (A) LLE phase diagram for a binary system (A
B) at constant pressure; (B) LLE phase diagram for a ternary system composed of a diluent A
solute B
solvent C at constant temperature and pressure; and (C) VLLE phase diagram for a system (A
B) at constant pressure. For the LLE region, dashed lines are tie-lines, and straight lines are the binodal curves.

and heterogeneous conditions are delimited by the binodal curve. Furthermore, in the immiscible region inside the binodal curve, the concentrations of the two phases in equilibrium are obtained in the interception of binodal with the extremes of the dashed line (tie-line). In this diagram, the compound B may represent the solute to be removed. Thus, for the achievement of an efficient liquid
liquid extraction, it is desirable that the compound B be more concentrated in the solvent rich phase, or the extract stream. This is clearly observed by the slope of the tie-lines. It means that the main goal of a good extraction problem is obtaining the best differentiation between the concentration of the compound B in the mixture rich in C, and the concentration of the compound B in the mixture rich in A or a tie-line with a positive slope. 277

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On the other hand, Fig. 7.1C shows a typical VLLE phase diagram for a binary system A
B at constant pressure. In this case, the two compounds also generate two liquid phases, a stream rich in A, and a stream rich in B, similarly to what occurs in the classical LLE behavior represented in Fig. 7.1A. However, up to a specific temperature a vapor phase is obtained. If this occurs, at this particular temperature three phases are observed: one liquid phase rich in A, one liquid phase rich in B, and one vapor phase composed of both compounds. This point is called heterogeneous azeotropic point, established at a specific temperature and pressure. Similarly of what occurs in the vapor
liquid equilibrium problem, discussed in Chapter 6, this temperature and concentration condition makes the separation process more difficult. The observation of the phase equilibrium behavior of the system is crucial for obtaining the best separation process conditions. As observed, LLE and VLLE have several important details to be established. Then, in this context, this chapter: (1) describes the most common LLE and VLLE behaviors in food systems as well as the most important applications of LLE and VLLE for the design of processes in the food industry; (2) provides a discussion on the most significant industrial variables that affects the LLE and VLLE conditions; and (3) highlights the most common thermodynamic models/equations used for representing the LLE and VLLE phase data, taking into account the modeling of food processes and equipment design. Moreover, this chapter presents a case study on deterpenation and recovering of aroma compounds from orange juice essential oil. This study evaluated the LLE and VLLE phase behaviors of the systems involved in the design of the industrial process.

7.2 THE LLE PHASE DIAGRAMS Previously, the most representative LLE phase diagrams for binary and ternary mixtures were briefly presented. However, the classical phase equilibrium theory usually categorizes four different types of LLE phase diagrams for binary systems, six for ternary systems [1
3]. Fig. 7.2 shows the most known LLE diagrams found in binary mixtures. When two partially miscible liquid biocompounds 1 and 2 are placed in mutual contact, two liquid phases α and β can coexist in equilibrium in determined conditions. Fig. 7.2A present a system called “upper critical solution temperature” (UCST). Because the term “critical temperature” may cause a misunderstanding with the critical properties of pure compounds, the term “consolute” could be better applied in these cases. For this type of 278

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Liquid Equilibrium in Food Processes

(A)

(B) U

TU

B

A

A

Two-phases

Two-phases

B TL

L x1α

x1α

x1β

(C)

x1β

(D) U

TU

A

TL

Two-phases

Two-phases

B

B

A

L

x1α

x1β

x1α

x1β

Figure 7.2. Types of phase diagrams for LLE in binary systems: (A) with an upper consolute temperature (TU); (B) with a lower consolute temperature (TL); (C) with both upper and lower consolute temperatures; and (D) with no consolute temperature.

system, the mutual solubility between the compounds 1 and 2 increases as temperature increases up to the point U, i.e., the consolute temperature. Thus, at temperatures higher than that, the mixture forms a single phase. In this mixture, the left-hand side curve of the diagram comprises a homogeneous liquid phase α rich in the compound 2. Otherwise, at the right-hand side curve of the diagram a homogeneous liquid phase β, rich in the substance 1 is observed. This behavior is the most known LLE profile among systems composed of food biomolecules. It is observed in binary or pseudobinary systems composed of vegetable oils
solvents [4,5], of interest for the oil refining industry, systems composed of food biopolymers
solvents [6], among others. Fig. 7.2B sketches a situation with an opposite behavior: the mutual solubility between the compounds 1 and 2 increases as the temperature 279

Thermodynamics of Phase Equilibria in Food Engineering

decreases, up to the point L. It is popularly called “lower critical solution temperature” (LCST). Therefore, in this case, the consolute (or critical) temperature is a minimum point. Despite the UCST phase diagram is the most found for food systems, examples of this uncommon behavior can be seen for mixtures close to those seen in food systems, such as those composed of water and surfactants, which is the case of the nonionic surfactants poly(oxyethylene) alcohols
water and di(ethylene glycol) hexyl ether
water [7,8], and mixtures containing polymers and elastomers found in food packages or industrial accessories, such as polystyrene and poly(vinyl) methyl ether [9]. In more rare cases, considering the universe of systems of interest in the food industry, both profiles can also occur. It means that a “partial miscibility island,” or also known as “closed loop,” occurs at the center of the diagram, leading to the appearance of both upper and lower consolute temperatures in the equilibrium profile. This type of system is sketched in Fig. 7.2C. In this situation, the mixture will present a complete mutual solubility at both high and low temperatures, above and below the upper and lower consolute temperatures, respectively. Similarly to what was observed in both previous cases, at the left-hand side curve of the diagram a liquid phase rich in the compound 2 appears. Otherwise, at the right-hand side curve of the diagram, a liquid phase rich in the compound 1 is then observed. Inside the “immiscibility island” two-phases coexist. Aqueous mixtures of polymers are also good examples of this case of behavior. This includes mixtures of poly (ethylene glycol) (PEG) (mild molecular weights)
water [10,11], with a great importance in the formulation of aqueous two-phase systems (ATPS) largely used in the extraction and purification of biomolecules, and other polymers, close to the food industry universe, and used in the composition of industrial accessories, surface coatings, or adhesives [12]. In Fig. 7.2D, a typical temperature-composition profile for mixtures with no consolute temperature, i.e., not presenting UCST or LCST is sketched. In this case, the biphasic region is significantly altered with the composition of the mixture. It means that biphasic regions appear at the extremes of the mixture composition, and the mutual solubility increases at the central region of the diagram. For some authors, this behavior is called “hourglass” type diagram, which is also found in mixtures containing polymers used in food packages such as polystyrene mixed with solvents [13,14], or systems composed of lubricant oils used in the refrigeration cycle and refrigerant fluids, which is of interest of refrigerated food storages or freezing processes [15]. 280

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Liquid Equilibrium in Food Processes

In case of a mixture of three compounds, similar profiles to what is observed for binary mixtures can occur. However, considering that, in this case, two of three compounds can exhibit both complete immiscibility or miscibility situations, or even all of the three compounds can present partial miscibility, several phase equilibrium situations are seen. For ternary mixtures, then, the LLE behavior is classically categorized according to the proposed by Treybal [16] as: Types 0, 1, 2, and 3. This is sketched in Fig. 7.3. For systems “Type 0,” three completely miscible binary systems are seen. In this way, an “immiscibility island” or a “closed loop” is seen in a specific composition profile (Fig. 7.3A). The “Type 1” is the phase behavior most frequently found in the systems involved in the food industry. In this case, one of the pairs of compounds presents partial miscibility, and the two other pairs are completely miscible. Therefore, the phase diagram shows one region of immiscibility as sketched in Fig. 7.3B. Considering an extraction process in which the system is composed of a diluent, a solute, and a solvent, one supposes that the pair solute
diluent and solute
solvent is completely miscible. Otherwise, the pair diluent
solvent is partially miscible. This condition rules the extraction, and thus, the choice of the solvent for extraction. For systems “Type 2,” there are two pairs of partially miscible compounds and one completely miscible pair (Fig. 7.3C). In this case, depending on

Figure 7.3. Types of phase diagrams for ternary LLE: (A) Type 0; (B) Type 1; (C and D) Type 2; and (E and F) Type 3.

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the heterogeneous region of each pair, the heterogeneous areas are overlapped and only one heterogeneous region appears (Fig. 7.3D). For systems “Type 3,” the three binary pairs are partially miscible. Therefore, three heterogeneous regions might appear (Fig. 7.3E), including a special case when the heterogeneous areas are overlapped (Fig. 7.3F), when a “miscibility island” occurs at a certain composition profile. Some cases of systems “Type 1” will be better discussed in the following topics of this chapter and includes the systems involved in the vegetable oil refining process [17], biodiesel production of vegetable oils [18], partitioning of proteins [19], and organic acid production [20]. The other types of ternary phase diagrams are not commonly found in systems involved in food process. However, some examples can be seen: “Type 0” profiles are observed in mixtures presenting solvents used in chromatographic analysis of food biocompounds, such as the system phenol
tetrahydrofuran
water [21]; “Type 2” profiles are found in mixtures of solvents, also used in food analysis, such as the system cyclohexane
hexane
methanol [22]. Notably, hexane is also a solvent used in the refining process of vegetable oils; “Type 3” phase diagrams can occur in aqueous systems containing surfactants, ethylene glycols, alkanes, and fatty alcohols [23
25].

7.3 MAIN FACTORS AFFECTING THE LLE AND VLLE 7.3.1 Temperature One of the most important variables affecting the LLE is the temperature. This is because temperature strongly acts on the solubility of the compounds. In the most common food systems, this means that the solubility increases with the temperature increasing. This is highly significant and affects the size of the heterogeneous region, i.e., the region in which the system presents partial miscibility. Fig. 7.4 sketches the main effect of temperature on a ternary system presenting the most found phase profile in food processes (Type 1, see Fig. 7.3B). It can be seen that when the temperature increases from a lower temperature (T1) to a higher temperature (T2 or T3), the heterogeneous region of the system decreases. This means that the mutual miscibility of A and B increases when temperature increases until the consolute temperature (point P in Fig. 7.4A). Above this temperature, the ternary mixture is fully miscible. The size of the heterogeneous region largely impacts the process design or formulation of products. Liquid
liquid extraction, for example, depends on the 282

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Liquid Equilibrium in Food Processes

Figure 7.4. Effect of temperature on LLE. The diagrams (A) and (B) represent a ternary A
B
C system at three temperatures T3, T2, and T1 where T3 . T2 . T1. P is the consolute temperature for the binary A
B. The heterogeneous region decreases as the temperature increases. In this example, the same behavior occurs for the slopes of the tie-lines.

formation of two liquid phases, and, therefore, this process must be designed at temperatures below the consolute one. On the other hand, the heterogeneous region must be avoided, in case of the formulation of a product that should not present two or more phases, which directly impacts its sensorial quality. Temperature can also affect the slopes of the tie-lines [16] as well as their extent, due to the changes of the heterogeneous region, as also indicated in Fig. 7.4. This means that the concentration of each phase formed in the system will be modified as temperature changes. In fact, as temperature increases, the overall solubility of the system also increases. If concentration of the phases is modified, the partition ratio is also altered. The partition ratio (Ki) is defined as a relation between the composition of a component (i) in the phases α and β, Eq. (4.124), that can also be written on mass basis (Ki,w), i.e., Ki;w 5

wiβ wiα

(7.1)

where w corresponds to the mass fraction of component i, and the superscripts α and β stand for both liquid phases α and β. In Fig. 7.4, in mass basis, the phase α corresponds to the rich phase in component A and the phase β is rich in component B. If the partition ratio of a compound is 283

Thermodynamics of Phase Equilibria in Food Engineering

higher than unity, the tie-line will present a positive slope. Otherwise, a negative slope is observed. This determines how will be the composition of each phase. For illustrating the effect of temperature in the heterogeneous region, which also impacts the tie-lines of the phase diagram, the experimental LLE data for systems involved in the deacidification of vegetable oils [26] and biodiesel production from vegetable oils [27] are presented in Fig. 7.5A and B, respectively. In Fig. 7.5 and some other diagrams in this chapter, the axes are represented in terms of the mass fraction (wi), that is most commonly used in case of applying the LLE data for industrial processes, despite not usual for the classical LLE representation, which is done in mole fraction [26]. The first case describes the LLE data for the system vegetable oil (1)
oleic acid (2)
ethanol (3)
water (4) at four different temperatures. When temperature increased, the heterogeneous region decreased. Moreover, the tie-line also changed. This means that, considering the use of ethanol for removal of oleic acid from the vegetable oil, when temperature increases, the concentration of the oleic acid in the extract or solvent-rich phase decreases, relatively to the concentration of the raffinate or diluent-rich phase. This because the mutual solubility of the system oil
ethanol increases, decreasing the efficiency of the extraction. The second case describes the LLE data for the mixture soybean oil (1)
ethyl esters (2)
ethanol (3)
water (4) at 298.15 and 313.15 K. This system is the reactional medium during the ethanolysis for the production of biodiesel. In this case, again, when temperature (A) 0.10

(B)

0.3

W2

W2

0.2 0.05

0.1

0.00 0.0

0.2

0.4

W3

0.6

0.8

1.0

0.0 0.0

0.2

0.4

W3

0.6

0.8

1.0

Figure 7.5. Effect of temperature on LLE of the systems: (A) Jatropha curcas oil (1)
oleic acid (2)
anhydrous ethanol (3)
water (4) at 288.15 K (x); 298.15 K (’); 308.15 K (Δ); and 318.15 K (1) [26]. The symbols in w3 5 0.5 corresponds to the experimental mixture point; (B) soybean oil (1)
ethyl esters (2)
[ethanol (3)
water (4)], at 298.15 K (x) and 313.15 K (’) [27]. Adapted with permission from [26,27].

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increases, the heterogeneous region decreases. Considering that during the ethanolysis, the system must be homogeneous, if temperature increases, the overall solubility of the system increases and two phases are no longer seen, favoring the contact between the reagents.

7.3.2 Solvent As mentioned before, the determination of the LLE is highly important for the design of extraction operations, especially considering food processes. In this way, another important variable affecting the LLE behavior of a mixture is the solvent used in the process. Solvent extraction is established by the contact of a solvent and a feed, i.e., a mixture containing a diluent and a solute. After mixing and settling, two partially soluble liquids are formed, a rich-diluent phase or raffinate and a rich-solvent phase or extract. In this case, the choice of the better solvent that will establish the system solvent
solute
diluent, the LLE equilibrium data are necessary and will provide two important process parameters: the selectivity and the partition ratio. Moreover, solvent that are completely soluble in the medium, or that could not be recovered should be avoided. Thus, LLE data allow predicting how effective will be the separation process. As presented in the following topic, and considering a solvent extraction, the partition ratio (Ki,w) relates the ratio of a component in the solvent-rich phase and in the diluent-rich phase. Thus, the higher the partition ratio of the solute, less solvent will be required for the extraction. Moreover, taking into account the partition ratio of both diluent A and solute C, one should define the selectivity or separation factor (SCA) as the ratio between the partition ratios of the solute and diluent, Eq. (4.126), that can also be written on mass basis (SCA,w) as: SCA;w 5

KC;w w β =w α 5 Cβ C KA;w wA =wAα

(7.2)

The selectivity expresses how the diluent and the solute will be distributed in both liquid phases in the equilibrium state. If the SCA 5 1, the diluent and solute present the same partition ratio and the fractionation between these both components is impossible with this solvent. Therefore, for the occurrence of the extraction SCA should be 6¼1. Moreover, the higher this value, the better the solvent. It indicates how selective the solvent for a determined solute
diluent mixture is. 285

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In order to exemplify the impact of the solvent in the LLE data, as well as the impact of the solvent in the process parameters derived from the description of this phase equilibrium, Fig. 7.6 presents the LLE diagrams for two systems involved in the extraction of FFAs from vegetable oils [28,29]. The systems are composed of vegetable oil (1)
FFA (2)
solvent (3). Fig. 7.6A shows the changes in the LLE profile when the solvent of the ternary system is methanol, anhydrous or hydrated ethanol. When methanol is replaced by anhydrous ethanol, the changes in the heterogeneous region and in the tie-line slopes promoted the decrease in the selectivity value but a slight increase in the partition ratio of the FFA. Moreover, the hydrated ethanol decreased the heterogeneous regions, decreasing the efficiency of the process. These results suggest that methanol has a lower ability in the extraction of FFAs, probably due to lower solubility of the FFAs when compared to ethanol. Also, the presence of water decreases the extraction ability of the solvent. Fig. 7.6B shows the changes in the LLE profile when two grades of hydrated ethanol are used as solvent. First, the highest content of water (30 %) caused a significant increase in the biphasic region. However, there is an inversion of the behavior of the slope of the tie-lines. In case of lower water content (5 %), the slopes of the tie-lines indicate an improvement in FFA extraction. This is because the concentration of FFA is higher in the ethanol-rich phase (extract) than in the oil-rich phase (raffinate). Despite of the greater heterogeneous region with higher water (A)

(B)

0.2

W2

W2

0.2

0.1

0.1

0.0 0.0

0.2

0.4

W3

0.6

0.8

1.0

0.0 0.0

0.2

0.4

W3

0.6

0.8

1.0

Figure 7.6. LLE experimental data of the systems: (A) canola oil (1)
oleic acid (2)
solvent (3) at 303.15 K for the solvents methanol (x), anhydrous ethanol ( 3 ), and hydrated ethanol (▲) [28]; (B) babassu oil (1)
lauric acid (2)
(ethanol
water) (3) at 303.2 K for the system containing 5% (’) and 30% water (x) concentration [29]. The symbols in w3 5 0.5 correspond to the experimental mixture point. Adapted with permission from [28,29].

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content (30 %), the FFA prefers the raffinate, meaning that the extraction of the FFA from the vegetable oil, in this case, will not be efficient, and one would rather a lower water content (5%) for the extraction of FFA.

7.3.3 Pressure The influence of pressure in the LLE is very small and generally, this effect can be ignored. This because in practice the specific volumes of the liquid phases involved in the equilibrium are constant during a process under different mild pressures. The exception is for systems submitted to very high-pressure processes. High-pressure processing, or also called high hydrostatic pressure, or ultra-high pressure processing has been largely applied in food process, especially for sterilization of liquid or solid foods. Pressures applied can largely vary up to 800 MPa, but higher pressures can also be seen. With this pressure increasing, sterilization can be reached without thermal injuries, ensuring product quality. In the context of LLE experimental data, few works are seen considering the effect of high pressures and among them no one with food systems. An interesting example is for the system water (1)
ethanol (2)
1,1 difluoroethane (3) (Fig. 7.7) [30]. Considering the environmental concernings on the uses of chlorofluorocarbons, the 1,1-difluoroethane is a more environmentally 0.35 0.30 0.25

X2

0.20 0.15 0.10 0.05 0.00 0.00

0.10

0.20

0.30

0.40

0.50 X3

0.60

0.70

0.80

0.90

1.00

Figure 7.7. LLE of the system water (1)
ethanol (2)
1,1 difluoroethane (3) at 1.38 MPa (&), 6.08 MPa (x), and 10.13 MPa , at 323.2 K. Adapted with permission from [30].

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friendly refrigerant fluid with an industrial name of R152a. The design of refrigeration cycles is extremely important in food industries, taking into account freezing and cooling processes as well as refrigerated storage. In the refrigeration system, after the evaporation process, the pressure of the refrigerant is increased up to a temperature in which it is condensed, by using a compressor. For this particular ternary system, when pressure increased from 1 to 10 MPa, approximately, authors concluded that the solubility of the mixture slightly increased. This means that the two-phase region of the diagram tends to decrease, which is particularly interesting in this case. In fact, the decreasing is almost negligible, in accordance with the fact that the specific volumes of the phases do not alter with pressure at mild conditions. However, no data have up to now been found for LLE at higher pressures, such as those obtained in high-pressure processing in the food industry, that could impact the phase behavior with a slightly higher significance. Although in LLE the pressure has no considerable effect, at mild pressures, in VLLE this effect is more pronounced. The VLLE is characterized by a condition in which a heterogeneous azeotrope is formed, i.e., when a mixture in vapor
liquid equilibrium forms two liquid phases during its partial condensation. In the same way, the VLLE condition is also established when the temperature of a two-phase liquid system increases and a vapor phase appears before the consolute point of this two-phase liquid system is reached. An important example of VLLE in food processing is the binary mixtures formed during distillation processes for the production of spirits, such as cachac¸a, whiskey, or vodka and for the production of hydrated or neutral alcohol. Fig. 7.8 represents the VLLE diagram for the system water (1)
isobutyl alcohol (2), at pressures up to 40.52 3 102 kPa. During the sugarcane juice fermentation, the previous step for spirits production, such as cachac¸a, several compounds are formed. Ethanol is the major compound of the mixture, but other alcohols and esters are also formed, including isobutyl alcohol or isobutanol. Considering the fact that this alcohol is not a desirable product, in this context, it must be removed (together with the other nondesirable compounds) during the distillation of the wine, i.e., the fermented sugarcane juice. In this case, the VLLE data of this system were plotted at pressures from 1.013 3 102 to 20.26 3 102 kPa by using the Non-Random Two-Liquid (NRTL) model with parameters found in the ASPEN Plus databank for modeling the liquid phase. The fugacity coefficient of the compounds in the vapor phase was calculated by using the Virial equation 288

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550 530 510 490

T (K)

470 450 430 410 390 370 350 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

x1, y1

Figure 7.8. VLLE diagram for the system water (1)
isobutyl alcohol (2) calculated using the NRTL model (ASPEN databank) for the liquid phase and the Virial equation with HOC for the vapor phase: 1.013 3 102 kPa (s), 2.026 3 102 kPa (       ), 5.065 3 102 kPa (- - -), equilibrium 10.13 3 102 kPa (s   s), 20.26 3 102 kPa (s  s), and 40.52 3 102 kPa (s s s). Gray lines are the LLE data established below the azeotropic temperature.

of state with the Hayden
O’Connell (HOC) method for calculating the second Virial coefficient. When the pressure increases, the boiling temperature of the mixture increases. If the Vapor
Liquid Equilibrium (VLE) goes up in the phase diagram and the LLE behavior (at low temperatures) does not alter with the pressure increasing, the heterogeneous azeotropic point, Fig. 7.1C, becomes closer to the theoretical consolute point. It means, that the VLLE “region” decreases, i.e., the solubility of the two liquid phases in equilibrium with the vapor phase increases. Therefore, when pressure increases the VLLE behavior tends to become a single VLE behavior.

7.4 MODELING LLE AND VLLE OF FOOD SYSTEMS The calculations required for modeling LLE and VLLE, largely used for the design of separation processes are based on the phase equilibrium conditions established in Chapter 2. Therefore, the isofugacity criteria, based on the chemical potential of the compounds distributed in the phases, can be also applied in order to describe the LLE and VLLE of 289

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food systems. For LLE, taking into account the establishment of two liquid phases, the fugacity of the compound i in the phase α should equal to the fugacity of the same compound in the other phase β, i.e., α β f^i 5 f^i

(7.3)

If the isofugacity criteria are expressed in terms of the activity coefficient of the compound i, γi in each phase, and also assuming the same reference state for the compound i in both phases as the pure compound, the standard state fugacity of the compound i in the phase α, fiα,0, is equal to the standard state fugacity of the compound i in the phase β, fiβ,0, i.e., fiα,0 5 fiβ,0 5 fipure. Thus, the LLE can be expressed by: xαi γαi 5 xβi γβi

(7.4)

where xi is the mole fraction of the compound i in the phase α or β. In case of LLE of food systems, the great challenge in the calculation of the equilibrium state is to apply the activity coefficient model that better describes the non-ideality profile of the system. In fact, food systems are frequently comprised by multicomponent matrices composed of complex biomolecules, such as proteins, lipids, or carbohydrates. As the complexity of the biomolecules increases, the non-ideality behavior of the mixture increases, this demands a more judicious choice of the activity coefficient model. Also, this choice is extremely important, especially because the activity coefficients are the unique thermodynamic contribution in case of LLE, differently of what is observed in the VLE calculation. This will be discussed in Section 7.4.1. Therefore, Eq.(7.4) is the basis of the LLE calculation. The problem so consists in determining what occurs in the system, at constant pressure, for a given temperature and system concentration, i.e., (1) if the system is miscible or two (or more) immiscible phases appear, and (2) is the P what composition of each phase π. Taking into account that xπi 5 1, the activity coefficient of component i is generically given by:   γπi 5 γi xπ1 ; xπ2 ; . . .; xπn21 ; T; P (7.5) where n is the number of compounds in the system. This means that, for a binary case in LLE, at a given temperature T and pressure P, Eqs. (7.4) and (7.5) establish a system of two equations with two independent variables xα1 and xβ1 .

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Classically, the LLE calculation could be carried on through: (1) resolution of this system of equations composed of the phase equilibrium equations and the activity coefficient model. In this way, the composition of the phases at a single temperature (and/or pressure) can be calculated. The resolution of the system of equations will be as difficult as the complexity of the models used for the calculation of the activity coefficients increases. The non-ideality of the biocompounds found in food mixtures is a significant factor for this, as will be discussed in the next topic; and (2) resolution of the well-known stability test. Through the stability test, the Gibbs energy of mixing ΔG mix is calculated and analyzed. Thus, what will define if the system established is in equilibrium is the condition in which Gibbs energy of the mixture reaches its minimum value. It means, at a given temperature and pressure, the system will establish in the condition, one or two (or more) phases, whose energy of Gibbs of the mixture is the smallest possible. Stability test could be represented by Fig. 7.9A, where the solid line indicates a homogeneous phase (single phase), the dashed line indicates the heterogeneous phase. In the first curve (1), at T1, only one single or miscible phase is established, and the function that describes ΔG mix is smooth. On the other hand, in curve (2), at T2, there is a region where the Gibbs energy for the mixture to remain in a homogeneous phase is higher than Gibbs energy to be present in two phases, indicated by the dashed line. Thus, in this situation, the mixture will separate into two phases because ΔG mix for this situation is (A)

(B) One miscible phase

T1

ΔGmix

One miscible phase

T1

Two immiscible phases

T

T2

T2 Two immiscible phases xiα

xi

xiβ

xiα

xi

xiβ

Figure 7.9. (A) Behavior of the Gibbs energy of mixing of a binary system. Curve (1): occurring at T1, the mixture is a single or one miscible phase. Curve (2): occurring at T2, the mixture splits in the dashed line region limited by x1α and x1β ; (B) representation of phase diagram of such binary LLE system.

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lower (dashed line) than to remain in a single phase (solid line). In terms of stability criterion,  2 at constant  temperature and pressure, a single phase 2 will occur when d G =dx i . 0, while the separation in two phases mix   occurs when d2 G mix =dx2i , 0. The VLLE case could be presented, for a better comprehension, as a special condition of this LLE problem. In order to evaluate this special case, one can consider a two-phase system, established at a constant pressure and at a single temperature. For a typical LLE behavior, Fig. 7.1A, as the temperature of a binary system increases, the immiscible region decreases up to the consolute temperature, and, at temperatures higher than the consolute temperature, the compounds in the system are completely miscible. However, for some systems, when temperature increases a vapor phase with a particular composition can be formed. Thus, two liquid phases start to coexist with one vapor phase, Fig. 7.1C. Another way to understand this case is evaluating the VLE behavior described in Chapter 6. The most common VLE diagrams are given in Figs. 6.2 and 6.3. Two other cases are also sketched in Figs. 6.13 and 6.14, called minimum- and maximum-boiling homogenous azeotrope systems. One can suppose that, in case of the minimum-boiling case, when temperature decreases the vapor phase region also decreases and a miscible liquid phase appears at temperatures lower than the azeotropic temperature. However, in some cases, as the temperature decreases, depending on the system, not one liquid phase system appears, but a twophase liquid system in equilibrium condition with one vapor phase. Thus, the VLLE diagram also called heterogeneous azeotropic system is defined. It was previously sketched in Fig. 7.1C. Chapter 6 indicated that the VLE can be determined through the γ
φ approach using Eq. (6.1). Thus, this equation can be used in the calculation of the VLE of azeotropic systems both for the point of minimum or and for the point of maximum-boiling homogeneous azeotrope. In the VLE, as in LLE, the fugacity of component i in both phases must be equal. The non-ideality of the liquid phase could be then calculated by an activity coefficient model, similarly to what is used in the LLE case. Besides, for the γ
φ approach, it is necessary to calculate the reference fugacity of component i, fi0, that is a function of the fugacity coefficient at the saturation condition, φisat, the vapor pressure of component i, Pisat, and the Poynting correction factor POYi. The non-ideality of the vapor

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phase requires the calculation of the fugacity coefficient of component i V in the vapor phase φ^ . i

At the VLLE system, at temperatures lower than the heterogeneous azeotropic point, the equilibrium is calculated by LLE, Eq. (7.4). At temperatures higher than this point, the VLE rules the calculation, Eq. (6.1). However, at the heterogeneous azeotropic point, three phases appear and, through the isofugacity criteria, Eq. (7.6) is established: α β V f^i 5 f^i 5 f^i α β f^i ; f^i ,

(7.6)

V f^i

where and are the fugacities of component i in the liquid phase α, in the liquid phase β, and i in the vapor phase V, respectively. As defined by LLE and VLE, considering the same reference states, the isofugacity criteria in the VLLE condition could be rewritten in terms of activity and fugacity coefficients as follows: vap ^V xαi γαi φsat i Pi POYi 5 yi φi P

(7.7)

vap ^V xβi γβi φsat i Pi POYi 5 yi φi P

(7.8)

As presented for LLE, the VLLE situation can be also calculated by the minimization of ΔG mix or by the resolution of the system of equations that is established by the equilibrium equations. However, in this case, one should first take into account if, at a constant pressure and at a particular temperature, the system with a determined composition establishes one single liquid phase, two liquid phases in equilibrium, one single vapor phase, one vapor and one liquid phase in equilibrium or the threephase condition was established, i.e., two liquid phases in equilibrium with a vapor phase. In a separation process, as in the case of flash illustrated in Fig. 7.10, for instance, the resolution of this problem can be performed by determining two variables that answer the equilibrium condition: the ratio between the mass of vapor phase V and the mass of starting mixture F (or feed mixture), called a 5 V/F, and the mass fraction of the liquid phase Lα in relation to the summation of both liquid phases Lα 1 Lβ , i.e., b 5 Lα/(Lα 1 Lβ ). If a 5 0, no vapor phase is formed, and the problem is simplified to a simple LLE case. However, if b 5 1 or b 5 0, there is only one liquid phase, Lα or Lβ, respectively, and so the problem is simplified to a simple VLE case [31,32].

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Figure 7.10. The flash problem found in systems with VLLE sketched in a single vessel at a constant pressure P and temperature T and it relation with the VLLE phase diagram for a binary system. When the heterogeneous azeotropic point is established, two liquid phases establish the equilibrium condition with one vapor phase. At temperature higher than this point the system establishes a VLE case. At temperatures lower than it, an LLE case is reached.

7.4.1 Models Applied for the Calculation of the Non-ideality of the Compounds in LLE and VLLE of Food Systems As mentioned previously, food systems are generally composed by a complex set of biocompounds, such as lipids, proteins, carbohydrates, and electrolytes, which present a significant non-ideal behavior, in the thermodynamic point of view. It means that a large list of enthalpic interactions, composed of hydrophobic, electrostatic, and ionic forces, as well as relevant entropic contributions will promote the deviation of the ideal behavior. This deviation will be positive if the biocompounds present unfavorable interactions, which increases the tendency for phase separation, or negative, if the compounds establish favorable interactions, particularly interesting for solvent
solute relationship in the solubilization processes. The non-ideality of the liquid phase, which is the case of LLE, is determined by the calculation of the activity coefficients of the components. Otherwise, as previously discussed in the last topic, in case of VLLE, the non-ideality of the vapor phase is determined by the fugacity coefficient of the component in the vapor phase. Noteworthy, several

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authors consider that the vapor phase of food systems is thermodynamically ideal. In fact, in case of evaporation, drying, or even distillation processes in which vapor phase is almost composed by water, and at moderate pressures, close to ambient conditions, this consideration is often valid. Otherwise, non-ideality should be taken into account. For the calculation of the activity coefficients of food compounds in both LLE and VLLE cases, the most known thermodynamic models are those based on the calculation of the excess Gibbs energy (G E ) of the mixture. As explained in Chapter 4, by using a mathematical expression for the G E , two main groups of equations are frequently found in literature: the random-mixing models and the models with its basis on the local composition (LC) theory, within the particular case of food systems. In the LC group, the well-known correlative equations NRTL [33] and UNIQUAC (universal quasi-chemical) [34] are the main models applied in LLE. Without previously adjusted parameters, the parameters of these equations can be found by regression of experimental data. Besides the improvements of LC theory, Wilson model is not able to predict LLE, as previously presented in Chapter 4. Among the LC models, a special group, based on group contribution concept, presents the most used semiempiric and predictive model UNIFAC (UNIQUAC functionalgroup activity coefficients) [35] and its modifications [36,37]. The UNIFAC model could be applied in order to predict the behavior of the LLE or VLLE of food systems with some restrictions, which will be following discussed. In general, the LLE and VLLE data for a wide variety of food systems are most popularly correlated by using the correlative UNIQUAC and NRTL models. The NRTL equation is classically applied, not only in food systems, but popularly in general chemical processes, to mixtures that present partial miscibility, which is not the case of simpler G E models. This is very useful for correlating separation processes, in which partial miscibility is quite appropriate. In fact, in case of ideal or moderately ideal systems, what rarely occurs in mixtures of complex biocompounds, NRTL does not offer much advantage over the first G E models group such as Van Laar or Margules [3]. This is one of the reasons of the success of NRTL for describing LLE and VLLE of more non-ideal systems. Moreover, taking into account that food systems are often characterized by mixtures with more than three biocompounds, the other great advantage of this model is that it can also be easily extended for multicomponent cases.

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The UNIQUAC equation is also another option for correlating food systems with partial miscibility. Together with NRTL, it presents a great ability for adjusting the LLE and VLLE data of food systems. However, its advantage over the NRTL equation is that the number of adjustable parameters is decreased by only two adjustable parameters for each binary interaction. This is very interesting taking into account the evaluation of the equilibrium of multicomponent systems, and the great computational efforts of the adjustment of the experimental data in these cases. Also, the equation is based on the quasichemical theory [38], and two contributions for the non-ideality profile can be separately calculated: an entropic and an enthalpic one. It means that, if one considers the presence of great and anisotropic molecules, or the existence of a plethora of enthalpic contributions, UNIQUAC can provide a good characterization of the mixture. Anyway, there is a great databank on the UNIQUAC and NRTL parameters found in the well-known DECHEMA Chemistry Data Series [39] which also improve their use as predictive tools. Also, in the context of food systems, both equations can be successfully applied in case of LLE systems involved in several food processes. Some examples are the liquid
liquid extraction of terpenes from essential oils [40], production of esters from lipidic matrices by transesterification [41], production of organic acids and their recovery from aqueous mixtures [20], refining of vegetable oils by using solvents [42], extraction of nutraceuticals compounds from food matrices [43], extraction of sugars from aqueous systems [44], formulation of mixtures composed of polymeric surfactants [7], and the production and purification of amino acids from aqueous systems [45]. Some of these examples will be taken hereafter for more details on the ability of these models in the description of LLE of systems involved in food processes. Fig. 7.11 shows an LLE diagram for the pseudoternary system composed by Jatropha curcas oil (1)
oleic acid (2)
ethanol (3) at different temperatures [26]. The experimental data of this system were already presented in Fig. 7.5. Jatropha curcas oil is mostly produced in Central America and is taken as a nonedible oil. However, in order to be used for other purposes, such as production of fatty acids or esters, specially used in food industry, they must be submitted to refining processes. In this way, as previously discussed in this chapter, liquid
liquid extraction using a solvent, such as ethanol could be applied. This is also applied to the most common vegetable oils [4], such as soybean [46], corn [42], rice 296

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318.15 313.15

T (K)

308.15 303.15 298.15 293.15 288.15 100 80 60 100 w3

40 20 0

16

14

12

10

6 8 100 w2

4

2

0

Figure 7.11. LLE data for a ternary system composed of Jatropha curcas oil (1)
oleic acid (2)
ethanol (3) at 288.15 K (K), 298.15 K (’), 308.15 K (▲), and 318.15 K (V); calculated data using NRTL ( — ); experimental mixture points (x) and calculated consolute points ( 3 ). The axes are represented in mass percentage, i.e., 100w, where w is the mass fraction. Reprinted with permission from [26].

bran [47], and palm oils [48]. The NRTL model (but also the UNIQUAC model) could be, in this case, successfully in the description of the experimental data, including the good characterization of the behavior of the LLE with the variation of the temperature, as well as the calculation of the consolute point and plait point, i.e., the solubility limit of the system. Also, the mutual solubility between the oil and the solvent could be well described. This is quite important to emphasize the ability of these models in the correlation between calculated and experimental data at different temperatures. Fig. 7.12 shows an LLE diagram for the ternary system composed of water (1)
citric acid (2)
1-butanol (3) at 298.15 K [49]. Moreover, it presents the modeling of the experimental data by using the NRTL and UNIQUAC equations. This system is present in the extraction of polycarboxylic acids, such as citric or malic acid, produced by fermentation processes, using organic solvents. These acids are particularly important in the food industry because they are commonly used, among other applications, as acidifiers in non-alcoholic beverages, such as juices and sodas. Both models, NRTL and UNIQUAC, showed, in this case, a great ability to represent the LLE at low concentrations of the acid. This is easily observed through the precise tie-lines adjusted with the experimental 297

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Figure 7.12. LLE of the system water (1)
citric acid (2)
1-butanol (3) at 298.15 K: experimental data (’); and calculated data using NRTL (- - -) and UNIQUAC (—). Reprinted with permission from [49].

data. However, when the concentration of the acid increases in the system, the models presented a disagreement on the representation of the heterogeneous area. This is clearly observed through the determination of the plait point, which is significantly higher when UNIQUAC is used in the calculation. The authors concluded that the appearance of coulombic forces due to the partial dissociation of the organic acids in the aqueous media should be considered in order to improve the LLE modeling. In order to overcome the problem generated due to the presence of electrolytes in the description of the LLE of systems composed of food bioproducts, as well as improve the description of the phase equilibria for system presenting other complex interactions, which is the case of protein solutions, the literature suggests considering, together with the NRTL or UNIQUAC equations, or in place of these equations, other models related to these more complex contributions to the non-ideal behavior. This is the case of the Debye
Hu¨ckel equation [50] applied for mixtures with electrolytes, such as salts or amino acids [51
55], or the statistical association fluid theory [56] applied to polymers solutions, such as proteins or polysaccharides [57,58]. 298

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Despite their great applicability, NRTL and UNIQUAC equations cannot be used in the absence or scarcity of experimental phase equilibrium data. In these cases, predictive approaches need to be applied. In the context of food mixtures, the most popular approach used is the UNIFAC model, despite its inabilities. As detailed in Chapter 4, UNIFAC uses the LC theory proposed in the formulation of the UNIQUAC but introducing the group contribution concept for the complete calculation of the non-ideal behavior. This means that it admits that a mixture does not consist of molecules, but of functional groups. Thus, the properties of a mixture can be represented by the sum of the individual contributions of each group comprising in it. The great advantage of this approach is that with a relatively small number of groups, you can represent a large amount of mixtures. This definition meets with the context of food biomolecules in which experimental data is quite difficult to be obtained due to the purity of the compounds or their price. However, despite the fact that even this method is also not reliable in case of complex systems, which is not the case of only proteins or polysaccharides, but also lipids, higher alcohols, and acids, it is a good starting point for further evaluations. As commented in Chapter 4, from the original version of Fredenslund et al. [35], new binary interactions parameters between groups have been readjusted or reevaluated. Also, slight changes in the original equations were proposed, such as those presented by the modified UNIFACDortmund [37], and modified UNIFAC-Lyngby models [36]. However, all these versions were originated from VLE experimental data, and thus, applied in order to calculate the activity coefficient of the compounds in the liquid phase during the establishment of a VLE condition. Additionally, their use were successfully extended in case of solid
liquid equilibrium prediction [59]. However, since parameters of these versions took into account V
L phase transition data, they are not suitable for LLE and VLLE cases. For this reason, Magnussen et al. [60] proposed that the UNIFAC parameters should be readjusted by using a LLE databank, establishing so a modified version called UNIFAC
LLE. Fig. 7.13A compares the phase behavior of a simple food mixture water (1)
acetic acid (2)
butyl acetate (3) at 298.15 K [61] with calculated data by UNIFAC
LLE and Fig. 7.13B presents the phase behavior of water (1)
ethanol (4)
ethyl acetate (5) at 298.15 K [62] with calculated values by original UNIFAC and UNIFAC-Lyngby. Ethyl acetate and butyl acetate are biocompounds used in the formulation of industrial fruit essences. 299

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Figure 7.13. LLE equilibrium data of the systems: (A) water (1)
acetic acid (2)
butyl acetate (3) at 298.15 K, experimental data (—, W) [61] and calculated values using UNIFAC
LLE (- - -, &) ; (B) water (1)
ethanol (4)
ethyl acetate (5) at 298.15 K, experimental data (W) [62]; calculated values (- - -) using UNIFAC (x) and UNIFACLyngby (K); (C) triolein (6)
oleic acid (7)
ethanol (4) at 293.15 K, experimental data (’) [63] and calculated values using UNIFAC-LLE (- - -); and (D) same system with calculated values using UNIFAC
LLE with new parameters. wi is for mass fraction of component i, 100w is for mass percentage, and xi is for mole fraction of component i. Reprinted with permission from [61
63].

The UNIFAC model and its modifications present in these cases a reliable behavior. However, one must take into account the simplicity of the systems, the similarity of the molecules and so their low deviation from the expected behavior. Fig. 7.13C and D present the phase behavior of a more complex situation: triolein (6)
oleic acid (7)
ethanol (4) at 290 K. This system is a model system involved in the deacidification of a vegetable oil rich in triolein by using the liquid
liquid extraction with ethanol. By using the UNIFAC
LLE and the parameters matrix of Magnussen et al. [60], Fig. 7.13C, the prediction of the phase behavior was largely altered, the two-phase region was overestimated and the solubility of the oleic acid in ethanol was poorly described. However, with the readjustment of the interactions parameters of the main groups of the 300

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lipidic molecules CH2COO, CH 5 CH, COOH, and OH, proposed by Batista et al. [63] the UNIFAC
LLE could precisely describe the LLE behavior of this system. As presented, in order to overcome the sensibility of the UNIFAC model when the non-ideality of the mixture increases, in some cases, authors opt to readjust the parameters of the original UNIFAC groups or create new UNIFAC functional groups. This is because some molecular groups establish specific interactions that the original UNIFAC group matrix does not take into account. This is the case of sugar molecules, such as glucose, mannose, galactose, fructose, sucrose, lactose, and maltose, found in the composition of food matrices such as fruits and vegetables, sugarcane, and milk, and also largely used in the formulation of several foods [64
67]. Fig. 7.14 sketches the sucrose molecule with some proposals to interpret the functional groups of this molecule. The original UNIFAC model [35] take into account that all molecules are aliphatic structures, which is not the case of sugar molecules. This is also valid for the UNIFAC
LLE and all other UNIFAC modifications. Therefore, cyclic groups were created in this particular case, a pyranose and a furanose group, taking into account the C6 and C5 rings. This is justified because significant π
π interactions are promoted when cyclic groups are in the mixture. So, the non-ideality of the compounds increases, which could not be considered if the molecule is interpreted as an aliphatic structure. The disaccharide osidic bond, CH
OCH, between the

Figure 7.14. New interpretation of the sucrose and diacylglycerol molecules taking into account the new UNIFAC functional groups created (in detail): For sucrose, OHring, pyranose group, furanose group, and osidic bond [64
67]. For diacylglycerol (monoacylglycerol and glycerol molecules), a new OH group. R1 and R2 are aliphatic carbon chains [68].

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pyranose and furanose groups, and the hydroxyl group OHring linked with the cyclic group were also taken into account as new UNIFAC groups. These cases increased the ability of the model for determining the activity coefficients of the compounds in the liquid phase and, consequently, the determination of the phase equilibrium. As a prediction tool, this approach increases the UNIFAC group databank for future investigations. Other examples on the creation of new functional groups from food molecules, or the readjustment of the parameters of the original UNIFAC groups matrix for specific cases are for systems composed of: dicarboxylic and hydroxycarboxylic acids, used as acidulants in food products [69]; PEG, used in ATPS for separation of biomolecules in aqueous systems [70]; polyols, used as sweeteners in food products [71,72]; and lipids [68,73], such as triacylglycerols (TAGs), partial acylglycerols, glycerol, fatty acids, and fatty esters, very important in the description of the systems of interest in the fat and oils industry. Fig. 7.15 presents the LLE of the system composed of sunflower oil (i.e., a multicomponent mixture

Figure 7.15. LLE for the system [sunflower oil (1)
diacylglycerol (2)
monoacylglycerol (3)]
ethyl linoleate (4)
ethanol (5) at 303.15 K. Experimental data (K) [74] in mass fraction (wi): compounds i 5 1, 2, and 3 are in a specific composition presented by the authors; UNIFAC
LLE (- - -); calculated data using the parameters readjusted by [68]. Adapted with permission from [74].

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of TAGs), partial acylglycerols, ethyl linoleate, and ethanol. The presence of ethanol is of importance for vegetable oils processing. In this phase diagram, the equilibrium predicted by the UNIFAC
LLE was compared to that obtained by the approach of Bessa et al. [68]. In this case, it was considered that the OH group attached to the carbon chain of the glycerol or partial acylglycerols behaves differently than the traditional OH group of alcohol molecules due to the effect of strongly polar hydroxyl groups bonded to consecutive carbon atoms in the glycerol and acylglycerol molecules. The new OH group was also sketched in Fig. 7.14. This proximity effect may force this OH group to present specific interactions, different from those of the usual alcohol group. This modification resulted in an important improvement in the agreement between calculated and experimental data without losing the simplicity of the group contribution approach. Besides the UNIFAC model and their modifications, other predictive approach applied for the calculation of the non-ideality of the liquid phase in case of LLE is COSMO (conductor-like screening model) [75] and their variations COSMO-SAC (segment activity coefficient) [76,77] and COSMO-RS (real solvent) [76]. Although many times defined as a model, strictly speaking, COSMO is not an activity coefficient model, it is a dielectric continuum solvation method used in quantum mechanics programs, i.e., it is a software package. Different to the activity coefficient models based on the calculation of the G E , the COSMO methods use quantum mechanics theory to describe the thermophysical properties of fluids, among them the activity coefficients. In contrast to the UNIFAC model, COSMO proposes to overcome the problem of the group contribution method in which groups are defined as promoting the same energy and entropy quality in the system. This is very interesting, especially taking into account food biomolecules, in which molecular groups can present different chemical functionalities. Some examples of its use in describing the LLE of systems of interest for food industry, even not directly evaluating food systems, are the extraction of biomolecules from food matrices [78], recovery of biocompounds from aqueous systems by using solvents [79], the prediction of the equilibrium of aqueous salt (food model) solutions [80], mutual solubility of alcohols and water, and the extraction of sugar and aromatic compounds from ethanolic mixtures [81]. However, despite their relevance, significant non-ideal behavior, such as for biopolymeric situations, is still a thermodynamic challenge. All mentioned equations applied in LLE of food systems for calculation of the non-ideality of the biomolecules in both liquid phases are also 303

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well used in VLLE. Similarly, pros and cons are also found in this situation. It means that all of them are limited in describing the thermodynamics of the liquid phase in equilibrium with the vapor phase and the limitation takes into account the significance of the non-ideality of the system. In fact, the VLLE case is characterized by the formation of the heterogeneous azeotropic point, in which two liquid phases coexist with a single vapor phase. This is a non-ideal situation of both VLE and LLE conditions of the mixture. Therefore, the non-ideality of both phases must be considered. If LLE uses only one equation in order to describe the non-ideality of the phases, in VLLE, according to Eqs. (7.5) and (7.6) additional parameters are used, and they are related particularly to the non-ideality of the vapor phase. One of the thermodynamic parameters, in this case, is the fugacity coefficient of the biocompound in the vapor phase and it can be calculated by several approaches. The most applied approach for systems of interest in the food industry is the equations of state (EoSs), detailed in Chapters 3 and 5. One of the characteristics of EoS is to correlate volume, pressure, temperature, and composition of the mixture, and this is very useful in the calculations of the fugacity coefficient of the component in the vapor phase. In case of moderate densities, the Virial equation truncated at the second term is a simple and accurate method to calculate the fugacity coefficient of the components in the vapor phase. The challenge is how to determine the value of the second virial coefficient. Chapter 2 presents a discussion about this subject. On the other hand, in the case of biocompounds that establish great interactions in the vapor phase, in the most cases, the non-ideality of vapor phase has been determined by EoSs, estimating the interaction parameters from the regression of phase equilibrium experimental data. The EoS used with great advantage in phase equilibrium calculations for food systems are the cubic EoSs [1], such as van der Waals (vdW), Redlich
Kwong (RK), Soave
Redlich
Kwong (SRK), and Peng
Robinson (PR). All of them were also discussed previously in Chapter 3. Fig. 7.16 presents the VLLE diagram for the binary system isoamyl alcohol (1)
water (2) at 1.013 3 102 kPa [82], with experimental data obtained by Cho et al. [83]. The isoamyl alcohol is a compound produced during fermentation of sugarcane juice and separated during the production of food-grade alcohol. The experimental data were compared to calculated values obtained by using classical NRTL model applied for

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410 405 400 395

T (K)

390 385 380 375 370 365 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

x1, y1

Figure 7.16. VLLE diagram for the system water (1)
isoamyl alcohol (2) at 1.013 3 102 kPa [82]. Experimental data (W,x) presented by [83]; calculated data using the NRTL parameters from ASPEN databank (- - -), calculated data using readjusted NRTL parameters (—). Both approaches used the Virial EoS with the HOC model for the vapor phase.

determining the activity coefficients of the components on the liquid phase [82]. Moreover, the fugacity coefficients of the compounds in the vapor phase were determined by the Virial EoS using the HOC model for calculating the second Virial coefficient. The HOC equation can be well-used for systems presenting associating compounds, which is the case of dimerization of compounds found in distillation of spirits, mixtures describing solvation phenomena effects, and for both polar and non-polar molecules [84]. In this diagram, authors used the NRTL parameters from databank of the ASPEN Plus software and also readjusted parameters. Both approaches presented a reliable description of the system, especially the composition of the heterogeneous azeotropic point that establishes the VLLE condition. However, the improvement of the description of the VLLE after regression of the data showed that this particular system presents a significant deviation from the expected behavior that could not be precisely predicted by the original parameters from ASPEN databank. The group contribution UNIFAC model can also be used for determining the activity coefficients of the molecules in liquid phase for VLLE

305

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380 360

T (K)

340 320 300 280 260 0

0.1

0.2

0.3

0.4

0.5 x1, y1

0.6

0.7

0.8

0.9

1

Figure 7.17. VLLE diagram for the system water (1)
ethyl acetate (2) at 1.013 3 102 kPa. Experimental data (W, &, x) [86,87], calculated data (this work, —, - - -) using the original UNIFAC model (liquid phase) and the RK EoS (vapor phase). Adapted with permission from [86,87].

cases. Among the modifications of the original model, the modified version UNIFAC-Lyngby [36] is an interesting choice since it takes into account the temperature dependence of the group binary interaction parameters [85]. Fig. 7.17 presents the VLLE diagram for the binary system water (1)
ethyl acetate (2) at 1.013 3 102 kPa. Ethyl acetate is an aroma compound largely found in fruits and also used in formulation of food products. Experimental data were compared with calculated values obtained by using the ASPEN Plus software. In this case, the original UNIFAC model and its original parameters matrix [35] were used for predicting the activity coefficients of the compounds in the liquid phase and the RK EoS was used for predicting the fugacity coefficient of the components in the vapor phase. For the RK EoS, parameters were taken from the ASPEN Plus data bank. Especially for the liquid phase, the original UNIFAC model was able to reproduce the VLLE condition, i.e., the azeotropic composition and temperature. This is quite interesting taking into account that the original UNIFAC model does not consider that the interaction parameters are temperature-dependent. In general, thermodynamic modeling of LLE and VLLE of mixtures found in industrial processes is not simple. In fact, this topic presented 306

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many binary and ternary examples successfully described by the usual approaches. It also presented the failures of these models, especially considering the complexity of food molecules. However, when the phase behavior of multicomponent systems needs to be predicted, not only more reliable models are fundamental, but also reliable numerical methods. This is also important because the existing parameters databanks were developed for binary or up to ternary systems. Consequently, their ability in predicting multicomponent mixtures is decreased. Many algorithms were already developed, in different programming languages such as Fortran, C1 1, or MATLAB in order to obtain the interaction parameters of NRTL and UNIQUAC equations, taking into account the description of the LLE (or VLLE) of binary, ternary but also multicomponent systems [31]. An important example is the algorithm proposed by Stragevitch and d’Avila [88]. The method is an extension of the proposed by Niesen and Yesavage [89] for cases with restricted number of compounds. It is based on the maximum-likelihood principle, and estimates the parameters of the models by regression of the experimental data, considering also multicomponent cases, and several equilibrium conditions, LLE and VLLE. In case of using the UNIFAC model, Hirata et al. [73] proposed an algorithm for readjusting the group binary interaction parameters of the UNIFAC model, in case of systems containing fatty compounds. This method used a significant databank comprising LLE of systems of interest in the refining of vegetable oils, such as mixtures with fatty acids, TAGs, and ethanol. This implies in a great number of independent variables to be adjusted, requiring a robust numerical strategy. In this case, authors used the flash isothermal approach [31], analogous to that presented in Fig. 7.10, for calculating the equilibrium, and the Simplex method [90] for readjusting parameters. In a similar way, Bessa et al. [68] adjusted new UNIFAC groups, taking into account a more comprehensive databank. Their results were previously described in Fig. 7.15. Beside these particular approaches, a variety of commercial software packages can also be used for the calculation of phase diagrams. Although most of the software packages can be used for the calculation of LLE and VLLE, their features and user interfaces differ. However, most of the model descriptions are common to all of them. This is valid for NRTL, UNIQUAC, and UNIFAC models, for representing the non-ideality of the components in the liquid phase, and the most common cubic EoS (vdW, RK, SRK, and PR) for describing the fugacity coefficients of the 307

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compounds in vapor phase. Among these commercial software, one can mention the ASPEN Hysys and ASPEN Plus (AspenTech, Burlington), PROII (Schneider Electric, Rueil
Malmaison), ProSim Plus (ProSim SA, Labe`ge), and ChemCAD (Chemstations, Houston).

7.5 APPLICATIONS OF LLE AND VLLE IN FOOD INDUSTRY 7.5.1 Deacidification of Vegetable Oils The term refining is used in edible fat and oil industry to characterize the purification steps for removal of undesirable compounds from crude fatty materials, such as FFAs, phospholipids, waxes, metals, pigments, and other [91]. Deacidification is the process of removing FFAs from crude oils. The main step of the refining process is deacidification or removal of FFAs because it largely impacts the product yield and the overall process cost. In chemical refining, the fatty acids are neutralized with the utilization of sodium hydroxide or in the case of physical refining by distillation at very low pressures and high temperatures. The use of liquid
liquid extraction with environmentally friendly solvents, such as ethanol is also an alternative to obtain vegetable oils with acceptable FFA contents but keeping other important bioactive compounds found in the vegetable matrix [92]. This process is entirely based on the LLE behavior of the systems involved. It means that this process is based on the difference between FFA and TAG solubilities in the solvent chosen [93
95]. Several LLE data have been published in order to understand the deacidification process by liquid
liquid extraction. This is because the equilibrium established between the two liquid phases determines the composition of both phases and the fractionation of the compounds. LLE data for systems involved in the deacidification process by using ethanol and other short-chain alcohols, such as methanol, propanol, and isopropanol were largely presented in the literature for palm, soybean, cottonseed, canola, corn, sunflower, and rice bran oils; and also for less common oils, such as babassu, garlic, grape, and sesame seed oils, J. curcas oil but also for pure TAGs [28,29,42,43,48,63,96
98]. These LLE data also comprise the common FFA presented in those crude oils such as oleic and linoleic acids but also lauric and palmitic acids, mainly found in specific oils, such as babassu and palm oils. Ethanol, among all the solvents used in this case, is an interesting choice because it presents low toxicity, easy recovery, good selectivity values, and good partition ratios for the extraction of fatty acids [97]. 308

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Figure 7.18. LLE data at 298.15 K for grapeseed oil (1)
linoleic acid (2)
ethanol (3) (’) [98]; grapeseed oil (1)
oleic acid (2)
ethanol (3) ( 3 ) [99]; and palm olein (1)
oleic acid (2)
ethanol (3) (x) [48]. Adapted with permission from [48,98,99].

Fig. 7.18 shows a representative set of LLE data for the main system involved in this process, composed of vegetable oil (1)
FFA (2)
ethanol (3) at 298.15 K [48,98,99]. These data present the LLE for two different oils, grapeseed oil and palm olein, and two different FFA, linoleic acid and oleic acid. The vegetable oil and the free fatty acid do not affect significantly the heterogeneous area and the tie-line slopes of the systems. Therefore, one might conclude that temperature and solvent are thus the main factors in order to design a deacidification process by liquid
liquid extraction. This was previously presented in Figs. 7.5 and 7.6 that also showed some cases for deacidification of vegetable oils.

7.5.2 Producing Biofuels From Vegetable Oils Within the biorefinery concept, the production of refined vegetable oils has been nowadays studied as an accomplished process to the production of biodiesel [100]. These researches on biodiesel or any other renewable fuels have been done due to the global concern about the shortage of non-renewable natural energy resources. Transesterification is the principal method used for industrial biodiesel production from vegetable oils [101]. During the transesterification reaction, the TAGs of the vegetable oil or animal fat are converted into methyl or ethyl esters, i.e., the biodiesel itself. This will depend on the alcohol used. Methanol and 309

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ethanol are the most commonly employed. If on the one hand methanol is the most used alcohol, ethanol is renewable, less toxic, and highly available in several regions, especially in the developing countries [102,103]. Glycerol is a byproduct of the reaction that occurs in a presence of a catalyst, commonly NaOH. The reaction occurs in three consecutive steps, in which TAGs are converted to diacylglycerol (DAG) and monoacylglycerol (MAG). It means that they are intermediate compounds [102,104]. Because the reactional system is essentially biphasic at the beginning (it means one oil-rich phase and the other alcohol-rich phase), and at the end of the process (ester-rich phase and glycerol-rich phase) [74,105], the LLE measurements of the systems involved in the reaction are essential in order to allow determine the distribution of the components into the phases, and then optimize the steps for industrial application. In fact, at the beginning of the reaction, a monophasic system is desirable in order to increase the contact between the alcohol and TAG. On the other hand, a purification step is needed in order to remove the esters from the medium. In this way, the biodiesel is washed with water to remove the excess of catalyst, alcohol, and glycerol. In this step, two liquid phases are formed, an ester rich-phase and a water rich-phase [41]. Fig. 7.19 shows pseudoternary LLE phase diagrams of systems composed of ethyl esters, fatty acids, ethanol, sunflower oil (HOSO), and partial acylglycerols at different temperatures [74]. These systems describe the initial stage of the ethylic biodiesel production, i.e., the reactional system itself. The fatty acids were included in these systems due to their presence in crude vegetable oils. The LLE data, in these cases, reveals the distribution of the minor compounds of the system, i.e., ethyl esters, fatty acids, MAGs, and DAGs in both phases: oil-rich phase and ethanol-rich phase. In these cases, ethyl esters and DAGs presented a slight preference for the oil-rich phase. Otherwise, MAGs and FFAs preferred the ethanol rich phase. This is quite significant taking into account the biodiesel production of a vegetable oil rich in FFAs and partial acylglycerols, which is the case of rice brain oil and corn oil, extremely susceptible to TAGs degradation after their extraction. Moreover, at higher temperatures or with the increase in ethyl esters, DAGs or MAGs compositions, the heterogeneous area decreases, which represents the desirable increase in the contact of both phases during reaction. LLE data for similar systems involved in the final step of the transesterification have been also presented [41,106
112]. These systems are composed of fatty acid alkyl esters, alcohol, glycerol, and also a fraction of 310

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(B) 0.2

0.012

0.1

0.006

0.0 0.0 (C)

w5

w4

(A)

0.2

0.4 w 0.6 6

0.8

0.000 0.0

1.0

0.4 w 0.6 6

0.8

1.0

0.2

0.4 w 0.6 6

0.8

1.0

(D)

0.08

0.10

0.06

0.04

w3

w2

0.2

0.05 0.02

0.00 0.0

0.2

0.4

w6

0.6

0.8

1.0

0.00

0.0

Figure 7.19. LLE data for systems containing (A) HOSO oil (1)
DAGs (2)
MAGs (3)
ethyl oleate (4)
oleic acid (5)
ethanol (6) at 303.15 K (’) and 318.15 K (Δ), explicit in ethyl oleate (4); (B) HOSO oil (1)
DAGs (2)
MAGs (3)
ethyl oleate (4)
oleic acid (5)
ethanol (6) at 303.15 K (K) and 318.15 K (&), explicit in oleic acid (5); (C) HOSO oil (1)
DAG (2)
MAGs (3)
ethanol (4) at 303.15 K (▲) and 318.15 K (x), explicit in DAGs (2); and (D) HOSO oil (1)
DAGs (2)
MAGs (3)
ethanol (4) at 303.15 K (x) and 318.15 K (V), explicit in MAGs (3). Adapted with permission from [74].

TAGs, MAGs, and DAGs that did not react. The presence of alcohol in the final step is due to the fact that it is commonly added in excess in order to increase the efficiency of the transesterification reaction. In this case, the process generates two streams: a top phase, rich in biodiesel, and a bottom phase, rich in glycerol. These studies show that in this step, the alcohol (methanol or ethanol) prefers the glycerol phase. This is clearly verified when one takes into account the partition ratio of ethanol [113] and methanol [112] in the systems, higher than three, i.e., the composition of the alcohol in the glycerol phase is significantly higher than its composition in the biodiesel phase. The fatty ester-rich phase is removed from the system and purified in order to fulfill the international quality standards. In the washing process, the biodiesel is mixed with water at ambient temperature, and the excess of catalyst is removed, as well as some fraction of alcohol and glycerol. 311

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Figure 7.20. LLE diagram for ethyl palmitate (1)
water (2)
ethanol (3) at 298.15 K (▲) and 333.15 K (&). Adapted permission from [114].

Two-liquid phases are formed: an ester-rich phase and a water-rich phase, being the alcohol distributed in both phases. The experimental LLE data presented in the literature for these systems, containing pure esters, such as ethyl palmitate, ethyl oleate, and ethyl linoleate, or biodiesel from cottonseed, crambe, fodder radish, and macauba pulp oils, mixed with water and ethanol, showed that water and ethyl esters are practically immiscible. Fig. 7.20 presents a representative example showing the LLE data of the system ethyl palmitate (1)
water (2)
ethanol (3) [114]. The great inclination of the tie-lines represents that the composition of ethanol is significantly higher in the water-rich phase than in the ester-rich phase, probably due to their polarities. This proposes that water is an excellent solvent for extracting ethanol from biodiesel. Moreover, the decrease in temperature increased the two-phase region, as well as the concentration of ethanol in the water-rich phase. Furthermore, at low temperature, the concentration of ethanol and water in the ester-rich phase also decreased, which is also quite important for the process.

7.5.3 Production and Purification of Organic Acids Organic acids, such as citric, tartaric, malic, butyric, and lactic acids are largely found in natural matrices, such as fruits, vegetables, and milk. In the food industry, they are mainly used as preservatives or acidifiers in the 312

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formulation of a several set list of products. They are also important ingredients for pharmaceutical and chemical industries [115]. Industrially, organic acids are mainly obtained by fermentation processes. Fermentation media are very complex systems and for this reason, the downstream processes are quite significant in order to obtain purified compounds. The recovering of citric acid in industry, for example, is commonly based on four steps. First, calcium hydroxide is added in the fermentation medium, calcium citrate is formed and then precipitated. The salt is then filtrated, and treated with sulfuric acid. Sulfate calcium is formed and the free organic acid is obtained. The filtrate is purified using activated carbon and finally evaporated, until obtaining citric acid crystals. This last step is quite long and shows a very low efficiency. Because of this, the process is considered ecologically harmful, due to the utilization of reactants with significant ecotoxicity [49,116]. Therefore, in order to obtain a more environmentally friendly purification step, in this case, alternative routes, based on liquid
liquid extraction could be employed [20,49,117
122]. Taking into account the recovering of organic acid, two main liquid phases are formed in the liquid
liquid extraction: a solvent-rich phase and a water-rich phase. In this way, the LLE data of the corresponding systems, mainly composed of water
solvent
organic acid, are essential for the design of the process. Firstly, the LLE data can be used for the choice of the best solvent, for each case. This is reached by taking into account the partition ratio of the organic acid and the selectivity of the solvent. N-alkanes and alcohols are good choices [118
121] for recovering organic acids from aqueous media. With lower partition ratios up to values of 0.5, fatty acids and vegetable oils are also greener but worst alternatives [117,122]. In order to sketch these effects, the LLE data of a system containing water (1)
butyric acid (2)
decanol (3) is represented in Fig. 7.21A, at two temperatures [119]. The temperature, in this case, was not significant with very overlapped tie-lines. Otherwise, the great inclination of the tie-lines proposes that the concentration of the butyric acid in the alcohol-rich phase is extremely higher than in the water-rich phase. This behavior can be altered with the addition of a cosolvent. Fig. 7.21B shows the increase of propionic acid preference to the organic phase in the system water (2)
propionic acid (3)
1-butanol (4) with the addition of o-xylene (1) [118]. Salts can also promote the same effect. Through salting-out effect, the heterogeneous region increases, 313

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Figure 7.21. (A) LLE diagram for water (1)
butiric acid (2)
decanol (3) at 298.15 K (’) and 318.15 K (x) [119]. (B) Distribution diagram of propionic acid between aqueous (I) and organic phases (II) [118] in the system composed of o-xylene (1)
water (2)
propionic acid (3)
1-butanol (4), at 298.15 K, with different proportions of o-xylene (1) and 1-butanol (4): 75/25 (’); 50/50 (x); and 25/75 ( 3 ). Adapted with permission from [118,119].

increasing the selectivity (separation factor, Eq. (7.2)) in the recovering of the organic acids [20].

7.5.4 Obtaining of Phytochemicals Plants are sources rich in biocompounds with important benefits for human metabolism and health. These phytochemicals, as they are also known, include carotenes, phenols, terpenes, sterols, among others. Because processing can lead to a non-desired extraction of them, processes need to be designed in order to allow their maintenance in the matrices. This denotes in no further addition of them in the food system, increasing the quality of the final product [123]. On the other hand, the design of new functional foods or nutraceutical products implies in incorporation of these bioactives in the system, requiring so efficient extraction processes of these compounds from their matrices [124]. In both cases, the control of the process variables, temperature, pressure, and solvent, should ensure no injuries on their bioactivity and functionality [125]. All these requirements can be designed by the phase equilibrium data of the systems involved. Crude palm oil, for example, is a natural source very rich in carotenes, which is the precursor of vitamin A. This biocompound is easily degraded during the conventional refining process due to the application of high temperatures. Crude palm oil also contains a significant amount of other important phytochemicals, such as tocopherols, tocotrienols, 314

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squalene, and phytosterols. Carotenes and tocopherols are well-known as natural antioxidants for human metabolism, but also offer in the same way an oxidative protection to the oil, because they are oxidized prior to unsaturated TAGs [126]. Taking into account that the vitamin A deficiency afflicts millions of children throughout the world, one should consider that the degradation of the carotene content of palm oil is an important target in the design of the refining process, considering that the palm oil represents the most produced vegetable oil of the international market [127]. In this context, and as previously commented, the liquid
liquid extraction for deacidification of vegetable oils can avoid or decrease the losses of phytochemicals, especially because it uses low temperatures and solvents that do not cause injuries in the nutraceutical functionality of the oils. Liquid
liquid extraction by using ethanol, for example, allows the maintenance of up to 99 wt% of carotenoids in the refined palm oil [43]. The same solvent can be employed in similar cases. Fig. 7.22 shows the partition ratio values for γ-oryzanol and tocols (tocopherols and tocotrienols) obtained from the LLE equilibrium data for the 0.40

0.35

K5-W, K6-W

0.30

0.25

0.20

0.15

0.10 0.2

0.4

0.6

0.8

1.0

1.2 O:S

1.4

1.6

1.8

2.0

2.2

Figure 7.22. Partition ratios for γ-oryzanol (K5,w, &) and tocols (K6,w, X) in the system rice bran oil (1)
hydrated ethanol (2)
FFA (3) at 298.2 K [128]. O:S is the ratio between oil (rice brain oil) and solvent (hydrated ethanol). Dashed lines are for calculated data using UNIQUAC. Reprinted with permission from [128].

315

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system composed of rice bran oil (1)
hydrated ethanol (2)
FFA (3) at 298.2 K [128]. Rice bran oil is an important source of tocopherols, tocotrienols, and γ-oryzanol. According to in vivo studies, γ-oryzanol can contribute to the hypocholesterolemic activity of the rice bran oil. Conventional deacidification processes using alkali compounds can degrade most of these phytochemicals [129]. The partition ratios show that both γ-oryzanol and tocols prefer the oil phase. This is quite significant considering the maintenance of these phytochemicals in the final product [128,130,131].

7.5.5 Partitioning of Proteins Food products are rich in proteins and their extraction are an important target for obtaining macromolecules used in several cases, such as thickening agents or food supplements. Otherwise, proteins can be obtained from fermentation broths, which require further operations for purification and separation of this biocompounds. Milk proteins are a classical example. Whole milk is approximately 87 wt% water and 13 wt% solids among which proteins are included. An extremely important coproduct of cheese manufacturing is the milk whey, i.e., a liquid fraction highly concentrated in protein [132]. Its main proteins are α-lactalbumin and β-lactoglobulin, accounting for more than 70 wt %. These proteins are composed by essential amino acids, especially the branched-chain amino acids, and, consequently, they have attractive functional properties to human nutrition, being a food ingredient with high potential in replacing other more expensive. The separation of these and other proteins from the aqueous media of the milk whey must be well-planned because losses in the bioactivity of these proteins can be caused, among other factors, by using high temperatures. In order to avoid the denaturation by high temperatures, extraction with ATPS allows the isolation of milk whey proteins and offers advantages such as short processing time and simple scale-up, as well as ensuring purity and high yield. An ATPS is formed by mixing, classically, two polymers, or a polymer and an inorganic salt in an aqueous media, containing the biocompound to be extracted. In this process, the partitioning of the biocompound will be based on the concentration and molecular weight of the polymer, the hydrophobicity of the polymers, temperature, solubility mechanisms, such as salting-in and salting-out effects, and pH [133]. Fig. 7.23 shows the 316

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(A)

(B)

80

3.5 3.0

60

2.5

50

KProtein-w

% PPG 400 (w/w)

70

40 30

2.0 1.5 1.0

20

0.5

10

0.0

0 0

10

20

30

40

% MD 2000 (w/w)

50

60

30

40

50

60

70

80

90

100

% TLL (w/w)

Figure 7.23. (A) LLE data for the ATPS composed of PPG400 and MD 2000 at 298.15  II I K and (B) the partition ratios Kprotein;w 5 wprotein =wprotein of the proteins in this system: bovine serum albumin (▼), α-lactalbumin (’), and β-lactoglobulin (x) as a function of the TLL (%). In this case, “II” is the PPG-rich phase and “I” is the MD-rich phase. Reprinted with permission from [134].

LLE data for the ATPS composed of polypropylene glycol (PPG) 400 (1) and maltodextrin 2000 (2) at 298.15 K and the Ki,w data for different proteins [134]. This LLE diagram is slightly different to what presented in Fig. 7.3. The axes do not show the solute and the solvent concentrations, as conventionally presented in case of liquid
liquid extraction processes. In this case, the diagram represents the concentrations of the polymers used (or the polymer and the salt used, in case of a polymer
salt ATPS). This is because one should observe the concentration of the polymers (or the polymer
salt pair) in order to form the two-phase system. Therefore, below the binodal, only one phase is observed. Above it, the tie-lines show the concentration of each polymer in each one of the phases: the bottom and top phase. The Ki,w data show that proteins, in this case, prefer the PPG-rich phase. However, in an ATPS, the choice of the polymer, as well as the presence of a salt in the system, can highly influence in the partitioning of the protein. An ATPS composed of PEG
sodium citrate, for example, leads to the concentration of α-lactalbumin in the PEG-rich phase and β-lactoglobulin, in the citrate rich-phase [135]. On the other hand, the increase in the molecular weight of the polymer can also lead to different partitioning profile. The partition ratios of the α-lactalbumin and β-lactoglobulin in ATPS composed of PEG 1500 or PEG 4000
potassium phosphate, for example, show that these proteins have higher affinity for the PEG 1500 [19]. These are few examples that 317

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illustrate the complexity and the versatility of the ATPS for partitioning of proteins in food systems, which justifies a careful LLE evaluation for the design of this process.

7.5.6 Production of Spirits and Neutral (Food Grade) Alcohol Spirits, such as cachac¸a, vodka, tequila, and whiskey, are produced by fermentation of a vegetable matrix, such as sugarcane juice, potato, or other plants, followed by distillation of the wine, i.e., the fermented product, in which ethanol is concentrated. In these cases, plates-columns are the mostly used for the high-scale distillation process, but batch processes are also seen [84,136
138]. After the fermentation, the profile of the wine is highly diverse and this composition will depend on the feedstock used for the production of the beverage, the fermentation conditions, and also the type of inoculums used. Table 7.1 shows a representative profile of a wine from sugarcane juice, used for the production of cachac¸a [139]. These compounds largely alter the sensorial but also the toxicological profile of these beverages, because high concentrations of these compounds can cause health problems. Because of this, some of these compounds are required at certain values, in order to reach a perceived and desired flavor. They are mostly, esters and higher alcohols such as isoamyl alcohol, isobutyl alcohol, and propanol. Otherwise, acetaldehyde and methanol, for example, must be removed up to concentrations in which their negative effects are not significant [140]. Because of this, their concentration must Table 7.1. Mass fraction composition of the main compounds found in a standard wine for cachaça or food-grade alcohol production and their representative phase profile during distillation Component

Composition (mass fraction)

Phase profile (in mixture with water)

Water Ethanol Methanol Isobutanol Isoamyl alcohol Ethyl acetate Acetaldehyde Acetic acid

0.932 0.066 3.20 3 1027 2.78 3 1025 1.42 3 1024 7.69 3 1026 1.58 3 1025 4.35 3 1024


VLE VLE VLLE VLLE VLE VLE VLE

Adapted from [139].

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be controlled and so part of them removed during the production of the beverage. These minor compounds, in mixture with water (the major component of the wine), can exhibit VLE or VLLE behavior. Mixtures of isoamyl alcohol or isobutanol (isobutyl-alcohol) with water are classical examples of VLLE profiles, as shown in Table 7.1. The phase profiles of these both systems were previously described in Figs. 7.8 and 7.16. The VLLE data will drive the extraction of these compounds in the distillation column. This is quite significant taking into account that the VLLE behavior is characterized by the formation of a heterogeneous azeotrope with a minimum point, which makes this separation more difficult at a concentration close to this value. The isoamyl alcohol
water mixture, for example, forms a VLLE profile at 368.25 K and 1.013 3 102 kPa, with an azeotropic point close to 0.81 water mole fraction in the vapor phase, 0.45 and 0.99 water mole fraction in both liquid phases. This means that, below this temperature, the mixture is a two-liquid-phase system. Otherwise, above this temperature, one vapor phase is established. In this sense, the first observation is that, in order to separate the isoamyl alcohol of the wine by distillation, the temperature of the column must be set at values higher than the heterogeneous azeotrope temperature. Otherwise, a two-liquid-phase system will flow through the system without further extraction of this compound. Because pressure and temperature vary along the distillation column, the determination of this temperature by VLLE data will determine in which plate this stream must be removed. Significantly, the isoamyl alcohol is the most found higher alcohol in the distillation of the wine. Therefore, it will be the majority compound of the side stream of the distillation column called fusel oil stream. The VLLE, in this case, is also used for the purification of this stream. It means that the formation of the azeotrope in the VLLE drives the purification of the isoamyl alcohol through a distillation
decantation route. This because between two liquid phases formed one will be very rich in isoamyl alcohol, i.e., the desired stream after fusel oil purification. The purified product obtained is used for the synthesis of several organic compounds, such as isoamyl acetate, a high-valued ester widely used for the production of banana flavor [82]. In case of food-grade alcohol, i.e., 94 wt% ethanol, all of these minor compounds are removed, in order to reach the quality standards for uses of this product in food formulation. For this, in a traditional neutral 319

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ethanol plant, three additional columns for the purification of the hydrated ethanol are required: a hydroselection, a rectifier, and a demethylizer column [139]. The hydroselection column is used for separation of the higher alcohols. In this column, potable water is added with the hydrated ethanol obtained from the first distillation column in order to dilute the higher alcohols. The presence of water increases the activity coefficients of the higher alcohols altering the VLLE behavior and increasing their volatilities, i.e., their partition ratios. This means that they are concentrated in the top of the column and removed in this stream [139].

7.6 CASE STUDY In this context, a system of great interest for the food industry is the concentration and purification of aroma from orange juice. During the production of concentrated orange juice, most flavor components are stripped away together with the water vapor. This vapor phase is a complex mixture including terpenes, hydrocarbons, and oxygenated compounds. The vacuum distillation is the most common process to recover and fractionate this mixture into two liquid phases, one rich in water and polar compounds, named aqueous essence or water phase and the other one rich in non-polar compounds, the oil phase or essential oil. These fractions concentrated in aroma compounds are added back to the final product in order to avoid the decreasing of the sensorial quality of the concentrated product [138]. Terpenes are a predominant proportion of this essential oil. A great problem in this fraction is that its unsaturated portion, mostly represented by limonene, is chemically unstable, and it tends to decompose by heat, light, and oxygen to unpleasant off flavors [92]. The oxygenated compounds, such as linalool, usually have better sensory properties, and some processes have been developed to enrich the mixture with oxygenated compounds. Such process is commonly known as deterpenation, and consists in a concentration of these compounds by removal of limonene from essential oil. However, it is very difficult to separate oxygenated compounds and terpenes, due to their closing boiling points [40]. Thus, for this fractionation, separation processes such as liquid
liquid or supercritical CO2 extraction have been evaluated [141,142]. In case of liquid
liquid extraction, perforated rotating disc contactor seems to be the most likely equipment which can be used for essential oil fractionation in industrial scale, since it has been widely used 320

Liquid
Liquid and Vapor
Liquid
Liquid Equilibrium in Food Processes

in petroleum refining and chemical industries. This liquid
liquid extractor is a column with inner perforated discs, which provides good dispersion between the phases, improving mass transfer [143]. In this case study, a liquid
liquid extraction process for fractionation and recovering of the aroma compounds from orange juice was simulated and the evaluation of the process as performed considering LLE data. The simulation was carried out using the commercial simulator ASPEN Plus, according to the flowchart shown in Fig. 7.24. The first column is a distillation column used in the recovery of the aroma compounds from the aqueous stream produced during orange juice evaporation (feed stream in Fig. 7.24). The second column is a liquid
liquid extraction unit for recovering the aroma compounds from the essential oil. For this investigation, it was considered that the feed stream, i.e., the evaporated phase from orange juice was a mixture composed of water, ethanol, limonene—as the representative of the class of unsaturated terpenes—and linalool as the representative of the class of oxygenated compounds of the aroma. The feed mass flow rate was set to 100 kg/h with the following composition (in mass fraction): 0.0482 of limonene, 0.9264 of water, 0.0248 of ethanol, and 0.0006 of linalool, based on composition described by Haypek et al. [142]. The distillation of these major components, including limonene, water, and ethanol was studied for better understanding the process. For the accurate design of the distillation equipment, an appropriate

Figure 7.24. Concentration and purification processes of aroma compounds. Adapted with permission from [142].

321

Thermodynamics of Phase Equilibria in Food Engineering

knowledge of the phase equilibrium properties of the liquid and vapor phases was required. No experimental equilibrium data are available in the literature for this quaternary system, so the UNIFAC model was used for predicting the phase equilibrium, using the ASPEN Plus (parameters used is shown in Table 7.2). The RK EoS was used for the calculation of the fugacity coefficients of the vapor phase. The distillation was simulated to perform at 1.013 3 102 kPa. From the calculations, VLLE diagram for the ternary system composed of limonene (1)
water (2)
ethanol (3) was determined, as can be observed in Fig. 7.25. A distillation boundary divides the system into two distillation regions. From the analysis of this diagram, it is observed that the distillation boundary is created by the presence of the two azeotropes relative to the binary systems water
ethanol and water
limonene (black points in the catheti of the rightangled triangle of Fig. 7.25). A column with five trays was simulated with the feed in stage 2, plus two stages, where the first stage corresponds to the condenser and the last one represents the reboiler, totalizing seven stages. The reflux ratio was set in 1.5 and the bottom mass flow was 86 kg/h, defined as the maximum mass flow, which allowed obtaining a distilled product with the lowest amount of water. The results for the distillate and bottom compositions for simulation and the location of the feed composition are also marked in Fig. 7.25. Using a distillate product mass flow rate of 14 kg/h, a composition of 34.4 wt% limonene in this stream was acquired. The bottom stream is composed mainly of water with low limonene concentration (1.83 3 10214 in mass fraction). However, it was noted that the distillate of this column still has a high concentration of water (0.478 in mass fraction). The point of the distillate composition is located near to the distillation boundary in the VLLE diagram. This boundary cannot Table 7.2. UNIFAC group interaction parameters (ASPEN plus databank) used for modeling the VLLE of the limonene (1)
ethanol (2)
water (3) system (Fig. 7.24) i

H 2O CH2 OH C5C

322

j H2O

CH2

OH

C5C


1318 353.5 270.6

300
156.4 235.36

2229.1 986.5
524.1

496.1 86.02 457


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Liquid and Vapor
Liquid
Liquid Equilibrium in Food Processes

1

Azeotrope

0.9

Tie lines VLLE

0.8

Boundary

0.7

Material balance line

W3

0.6

Vapor phase composition - VLLE

0.5 0.4 0.3 Distillate

0.2 0.1

Feed

0 0

0.1 0.2 Bottom

0.3

0.4

0.5 W1

0.6

0.7

0.8

0.9

1

Figure 7.25. Ternary VLLE diagram for limonene (1)
water (2)
ethanol (3) at 1.013 3 102 kPa, 298.15 K and the mass balance lines of the distillation column design. Liquid phase calculated using UNIFAC. Vapor phase calculated using RK EoS (ASPEN plus databank).

be crossed; consequently, in this case, it is not possible to obtain a stream with a higher purity of limonene. It was noted that the distillate stream exhibited two liquid phases. This is in accordance with the VLLE diagram shown in Fig. 7.25, since the points of the liquid composition of this stream are located in the VLLE region. Following the process represented in Fig. 7.24, the distillate stream feeds a decanter at 298.15 K and two phases are formed: the aqueous essence, rich in water, and the essential oil fraction, rich in limonene. The LLE of the linalool (1)
limonene (2)
ethanol (3)
water (4) system was evaluated in ASPEN Plus using a decanter block with two models: NRTL and UNIFAC
LLE, whose parameters are presented in Table 7.3. Fig. 7.26 shows the LLE diagrams for two different proportion of water in ethanol (30 and 40 wt%) on a free basis in the overall composition with NRTL parameters. The increasing amount of water in the 323

Table 7.3. NRTL binary interaction parameters for limonene (1), linalool (2), ethanol (3), and water (4) [40] and the UNIFAC
LLE group interaction parameters (ASPEN plus databank) used to calculate the LLE of the linalool (1)
limonene (2)
ethanol (3)
water (4) system (Fig. 7.26) NRTL

UNIFAC
LLE

Pair ij

Bij (K)

Bji (K)

αij

i

1
2 1
3 1
4 2
3 2
4 3
4

49.473 218.98 448.37 21315.6 2523.64 210.641

2714.27 470.81 5907.2 1226.2 2078 64.505

0.18 0.1 0.11 0.21 0.39 0.1

H 2O CH2 OH C5C



j H2O

CH2

OH

C5C

0 1300 28.73 896



342.4 0 328.2 292.3



2122.4 644.6 0 724.4



220.6 74.54 470.7 0



Liquid
Liquid and Vapor
Liquid
Liquid Equilibrium in Food Processes

0.45 0.40 0.35 0.30

W4

0.25 0.20 0.15 0.10 0.05 0.00 0

0.1

0.2

0.3

0.4

0.6 0.5 W2+W3

0.7

0.8

0.9

1

Figure 7.26. LLE phase diagram for limonene (1)
water (2)
ethanol (3)
linalool (4) system at 1.013 3 102 kPa and 298.15 K. Calculated values using UNIFAC
LLE and 30 wt% of water in ethanol (&), NRTL and 30 wt% , and NRTL and 40 wt% (x).

solvent leads to a lower migration of the oxygenated compounds to the phase rich in ethanol
water. The LLE of the quaternary system containing 30 wt% of water in ethanol (in a free basis in the overall composition) calculated by using NRTL and UNIFAC
LLE were very different, with significant differences in the heterogeneous area and the slopes of the tielines. Therefore, considering that the parameters of the NRTL model were adjusted from the experimental data, one may conclude that the UNIFAC
LLE was not able to describe this case. The LLE phase diagram predicted by NRTL shows that the increase of the linalool content led to the increase in the solubility of the system, resulting in a lower migration of linalool and a higher migration of limonene to the solvent phase. Furthermore, the high concentration of limonene in terpene phase shows that there is little affinity between the solvent and terpene phase. The deterpenation of the essential oil was then simulated using a liquid
liquid extraction column (Column 2 in Fig. 7.24) using a mixture 325

Thermodynamics of Phase Equilibria in Food Engineering

of ethanol and water as the solvent for liquid
liquid extraction and the NRTL model. The main objective is the separation of the limonene content from the essential oil fraction, but keeping the linalool content, i.e., the aroma. The extraction column generates two streams: (1) the raffinate stream, which contains most of the limonene (generically called terpene), and the (2) extract stream, which contains mainly the solvent along with the recovery linalool. In order to evaluate the recovery of the aroma during the liquid
liquid extraction process, here represented by the linalool, two variables were calculated: (1) the aroma/terpenes ratio in the extract stream, i.e., the ratio between the mass concentration of aroma and the mass concentration of limonene; and (2) the amount of aroma, or linalool, recovered. The influence of the solvent concentration in the extraction was evaluated, maintaining the solvent/feed ratio equal to unity and the number of stages at eight. The results presented in Fig. 7.27 show that the aroma/terpene ratio increases as the water concentration in solvent increases indicating that the solvent selectivity increases and lower quantities of terpene are extracted together with the aroma compound. However, the recovery of the aroma compound decreases as the water concentration in solvent increases, increasing the loss of aroma in the raffinate stream. 0.12

1.00

0.80

0.08 0.60 0.06 0.40 0.04

Aroma Recovery

Aroma/Terpenes ratio

0.10

0.20

0.02

0.00

0.00 15

20

25

30

35

40

45

50

55

100 wwater

Figure 7.27. Effect of the water concentration in the solvent mixture (water
ethanol) on the deterpenation of orange juice essential oil by liquid
liquid extraction (column 2, Fig. 7.24).

326

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Liquid Equilibrium in Food Processes

This case study showed that hydrated ethanol can be used for recovering the aroma compounds from the evaporated fraction generated during evaporation processes of the orange juice. Also, limonene can be removed, decreasing off flavors of essential oil of orange juice. However, this process is partially effective due to the great affinity between linalool and limonene. Therefore, the knowledge of phase equilibrium is fundamental for the evaluation, design, and optimization of the physical separation for food process using computational simulation.

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