Analysis and simulation of an industrial vegetable oil refining process

Journal of Food Engineering 116 (2013) 840–851

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Analysis and simulation of an industrial vegetable oil refining process Gabriele Landucci a,⇑, Gabriele Pannocchia a, Luigi Pelagagge b, Cristiano Nicolella a a b

Dipartimento di Ingegneria Civile e Industriale, Università di Pisa, Largo Lucio Lazzarino, 56126 Pisa, Italy SALOV – Società Alimentare Lucchese Oli E Vini S.p.A. 1582, Via Montramito, San Rocchino 55054, Italy

a r t i c l e

i n f o

Article history: Received 10 August 2012 Received in revised form 1 November 2012 Accepted 27 January 2013 Available online 4 February 2013 Keywords: Vegetable oil refining Process simulation Advanced thermodynamic models Formation of flammable mixtures

a b s t r a c t This work focuses on the performance analysis of an industrial vegetable oil refinery. Using a commercial process simulator, a process model was developed and validated against actual vegetable oil refinery field data. The simulator allowed investigating both energy and safety aspects related to the presence of residual extraction solvent (extraction grade hexane) in the processed crude vegetable oil. The critical nodes for hexane accumulation in the process were evaluated, both considering ordinary operative conditions and undesired process deviations due to increase of the hexane content. In this latter case, the control actions able to restore the normal operation were simulated, in terms of increased utility consumption (e.g., motive steam for ejectors and cooling water) or by modifying and optimizing equipment operating conditions. Finally, the possibility of flammable mixtures formation inside process vent pipes, caused by the entrainment of air due strong vacuum conditions, was also investigated. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Edible oil production by extraction processes greatly increased in the last century due to both higher request and consumption (FAO, 2011) and the progressive availability of more efficient process technologies and equipment (Bockisch, 1998; Mielke, 1990; Shahidi, 2005; Veloso et al., 2005; Calliauw et al., 2008; Cuevas et al., 2009; Haslenda and Jamaludin, 2011; Szydłowska-Czerniak et al., 2011; Zulkurnain et al., 2012). A critical phase of the edible oil production chain is the final refining aimed at removing free fatty acids, which, in too high concentrations, lead to the rancidity of the oil (Cavanagh, 1976; Sullivan, 1976; Keurentjes et al., 1991; Bhosle and Subramanian, 2005; Martinello et al., 2007; Calliauw et al., 2008; Cuevas et al., 2009; Carmona et al., 2010; Akterian, 2011), and other minor components such as phospholipids, pigments, proteins, oxidation products and the possible residual content of the solvent used for the extraction process. The main operations involved in conventional refining for removing the mentioned components are degumming, neutralization, washing, drying, bleaching, deodorization and filtration (Gunstone et al., 1994; Mag, 1990; Loft, 1990; Shahidi, 2005; Santori et al., 2012). This stage of the production chain is crucial for the quality enhancement of the final product. One the more critical aspects of vegetable oil refining is related to the presence of residual volatile solvent used for the extraction. In particular, due to the low vapor pressure, the residual solvent may cause a loss of efficiency in high temperature vacuum operations ⇑ Corresponding author. Tel.: +39 050 2217907; fax: +39 050 2217866. E-mail address: [email protected] (G. Landucci). 0260-8774/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jfoodeng.2013.01.034

(such as drying, bleaching and deodorization). In these operations, vacuum conditions are often obtained by ejector systems (Bockisch, 1998; Mag, 1990; Loft, 1990; Muth et al., 1998; Akterian, 2011), whose costs are mainly related to the consumption of steam and cooling water for condensation. A possible increase of the residual solvent concentration has a negative impact on these costs, besides worsening the environmental impact related due to higher emission factors (odors, pollutant, etc.) (MRI, 1995; Muth et al., 1998). Another criticality is due to the fact that the extraction solvent is typically technical hexane (extraction grade hexane) (Dunford and Zhang, 2003; MRI, 1995) a highly flammable liquid and vapor (GHS hazard statement, Shell, 2012). In some critical nodes of the process, the solvent accumulates in the vapor phase and mixing with air may occur, potentially leading to the formation of flammable mixtures and confined explosion of the equipment in case of accidental ignition (NFPA, 2007; Lees, 1996; Tugnoli et al., 2012). As reported in a previous work (Landucci et al., 2011) this mainly affects crude oil storage tanks, as also experienced in two recent severe accidents which involved several fatalities (La Repubblica, 2006; El Economista, 2007). Nevertheless, since very low pressure vacuum operations characterize several stages of the process (Bockisch, 1998; Mag, 1990; Loft, 1990; Shahidi, 2005; Muth et al., 1998; Akterian, 2011; Santori et al., 2012), a low but significant amount of atmospheric air is entrained by seals or gaskets mixing with the process vents. This may lead to the formation of flammable mixtures also in process lines. Even if the vegetable oil refining process is well known, the industrial facilities are continuously subjected to modifications, revamping and new technologies implementation in order to achieve a higher process efficiency (Shahidi, 2005). In the literature, several examples of simulation and experimental analysis of each

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single stage of the refining process are available (Keurentjes et al., 1991; Wills and Heath, 2005; Zin, 2006; Ceriani and Meirelles, 2006; Didi et al., 2009; Farhoosh et al., 2009; Sampaio et al., 2011), while a systematic performance analysis, which has been extensively applied in the framework of process/chemical industry (Motard et al., 1975; Shaw, 1992; Biegler et al., 1997; Vadapalli and Seader, 2001; Hoyer et al., 2005; Towler and Sinnott, 2013) and aimed at taking into account the mentioned critical aspects, is still lacking. The present analysis was therefore addressed at investigating the vegetable oil refining process by the development of detailed simulation model using the commercial software ‘‘Honeywell UniSimÒ Design’’ (Honeywell, 2010a,b). The analysis was aimed at identifying the main process streams, the reference substances, and quantifying the mass and energy fluxes among the refining plant. The process simulator was applied to case studies representative of the current industrial applications, deriving the input data from inlet conditions of an actual vegetable oil refinery. In particular, the vegetable oil refinery of SALOV S.p.A., located in San Rocchino (Massarosa) (Italy), was considered in the analysis. The simulation model was validated against actual field data of the same plant and a sensitivity analysis was performed in order to evaluate the utility consumption and potential safety relevant situations depending on the quality of the input feedstock, in particular evidencing the effect of the residual solvent content on the whole process efficiency.

2. Materials and methods 2.1. Methodological approach The flowchart of the methodology is reported in Fig. 1, and is based on the approach followed in a previous work by Landucci et al. (2011) for the analysis of crude vegetable oil storage systems. The first step of the methodology was related to characterization of the crude vegetable oil composition, which, for each type of seed or fruit, is determined by environmental conditions during plant grow and farming soil characteristics. A reference composition representative of different types of oil was used to perform the further steps of the methodology. The second step (see Fig. 1)

1

Characterization of the crude vegetable oil composition

2

Schematization of the oil refining process

3

Thermodynamic model for the estimation of vapor/liquid equilibrium

4

5

Software implementation of the refining process

Analysis of a case study and optimization of process conditions

6

Sensitivity analysis

7

Safety aspects

Collection of typical operations and process conditions from actual plants Validation with experimental data

UniSim tool Set up of optimal equipment operative conditions

Assessment of utilities requirement Increase of residual solvent concentration

Fig. 1. Flowchart of the methodology.

consisted in the schematization of the typical process operations for oil refining, with definition of operative conditions for process equipment and evaluation of energy requirements (steam consumption and other utilities). Then, a thermodynamic model was applied in order to reproduce the vapor/liquid equilibrium of the crude vegetable oil system (step 3 in Fig. 1), implementing the presence of water and residual solvent content. The model was validated against available experimental data. Next (step 4 in Fig. 1), the refining process was simulated with Honeywell UniSimÒ Design. Specific subroutines were implemented for the simulation of non-standard utilities such as the ejectors used for keeping vacuum conditions in process vessels and the deodorization operation. The process simulator was used to perform the optimization of operative conditions given the optimal composition of the feedstock, in order to minimize the costs related to utilities (step 5 in Fig. 1). A sensitivity analysis was performed (step 6 in Fig. 1) aimed at identifying the system response to the increasing residual solvent content in the feedstock and possible restoration control measures. Finally, the possibility of formation of flammable mixtures inside process lines was investigated (step 7 in Fig. 1).

2.2. Characterization of the crude vegetable oil Crude edible oil is a complex multicomponent system. Recent studies were focused on the detailed experimental or numerical characterization of the vapor/liquid equilibrium of this system (Christov and Dohrn, 2002; Rodrigues et al., 2004; Calliauw et al., 2008; Ceriani et al., in press). Furthermore, advanced modeling tools were implemented for the analysis of the refining process taking into account different relevant triacylglycerols (TAGs), partial acylglycerols (monoacylglycerols MAGs, diacylglycerols DAGs), and the possible residual acid components, such as free fatty acids of different type (Rodrigues et al., 2004; Farhoosh et al., 2009; Chiyoda et al., 2010; Silva et al., 2011; Sampaio et al., 2011; Gerasimenko and Tur’yan, 2012; Teles dos Santos et al., in press; Ceriani et al., in press). Nevertheless, since the aim of the present study was to evaluate the effect of residual hexane content on the safety and energy performance of process equipment, a simplified reference composition was considered. The same approach was followed in several studies on edible oil processing available in the literature (Zhang et al., 2003; Ruiz-Mendez and Dobarganes, 2007; Cerutti et al., 2012). The reference composition implemented in the simulation model is reported in Table 1. Such composition is based on the typical crude sunflower oil feedstock used in SALOV S.p.A. vegetable oil refinery, as already considered by Landucci et al. (2011). The oil phase of the edible oil was schematized as pure triolein (reference TAG), while the free fatty acids content is assumed as pure oleic acid. Minor components such as sterols, tocopherols and squalene are also present and were implemented in the UniSimÒ Design list of components as ‘‘hypo component’’ (Honeywell, 2010a). The hexane residual content (schematized as pure n-hexane) was taken as

Table 1 Reference composition of the crude vegetable oil considered in the present study based on SALOV refinery data. Components

Mass fraction (%)

Triolein Oleic acid n-Hexane n-C29H60 Sterols Tocopherols

97.29 2.00 0.10 0.15 0.40 0.06

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0.1% in the baseline case, the maximum value allowed for the crude oil processed by SALOV S.p.A. Nevertheless, this value may be higher, up to 8–10 times the reference value, depending on the type of seed and oil origin. Moreover, the presence of residual and/or process water was also taken into account in the evaluation of vapor/ liquid equilibrium (see Section 2.4).

fatty acids are stripped by steam in a tray tower and then condensed in a spray tower, while steam with non-condensable vapors are sent to the ejectors section. Ejectors are also used to keep the required vacuum conditions in other low-pressure sections (flash separator and bleaching reactor, see Fig. 2). Table 2 provides the detailed operative conditions used in each section of the refining process.

2.3. Schematization of the oil refining process 2.4. Thermodynamic model In order to consider typical refining process conditions, the purification of sunflower oil was simulated assuming a free fatty acid content of 2% to be reduced up to 0.04% (percentages are expressed on a weight basis). A schematization of SALOV process is reported in the Process Flow Diagram (PFD) shown in Fig. 2. This process is similar to others reported in the literature (Bockisch, 1998; Ceriani and Meirelles, 2006; Mag, 1990; Loft, 1990; Muth et al., 1998; Shahidi, 2005; Santori et al., 2012). The oil is first neutralized by adding sodium hydroxide to an intermediate grade of acidity removing the neutralized soaps and waxes with a centrifugal separator. Next, the oil is degummed by adding water and subsequently it is sent to centrifugal separation to split the oil fraction from the solid waste. During this step, the oil is washed with water and consequently it is dried in a flash separator under vacuum conditions. Next the oil is sent to the bleaching treatment, aimed at the removal of color-producing substances and further impurities. In this operation the oil is mixed with bleaching earth and activated carbon in a stirred reactor operating under vacuum conditions for the adsorption of the mentioned contaminants. The stream containing bleaching earth and activated carbon is modeled as pure water in the process simulator. Next the oil is filtered and sent to the deodorization treatment. This section consists of a ‘‘physical neutralization’’ with low pressure steam at high temperature under vacuum conditions. The free

The choice and the software implementation of the thermodynamic model is a crucial step for a sound modeling of the refining process, since it allows determining the operative conditions in each equipment unit. The UniSimÒ Design software can implement the thermodynamic model with different ‘‘property-packages’’ (Honeywell, 2010b) for determining the correct vapor/liquid equilibrium of complex mixtures. The use of the process simulator for the thermodynamic modeling of complex multicomponent systems is extensively diffused in both scientific and technical studies (Harwardt et al., 2008; Szabo et al., 2011; Towler and Sinnott, 2013). It is worth mentioning that equation of state models, in general, and the Peng–Robinson one and its variants, in particular, are recommended models in most commercial simulators for hydrocarbon mixtures, also in the presence of water, over a wide range of pressure and temperature combinations. More details on the UniSimÒ Design code validation are reported elsewhere (Honeywell, 2010a,b). In the present study, the selected Property Package is based on the Peng–Robinson equations (Peng and Robinson, 1976) corrected with the Twu Alpha function (Twu et al., 1995; Honeywell, 2010b), which takes into account the excess free energy in order to have more accurate prediction of vapor pressure. More details on the thermodynamic model implemented in software are reported

V E5

E6

E8

W

W W EJ3c E7 W

MPS EJ1a/b

EJ3a/b

EJ2a/b

EA

E4

C2 PI1

W SH

P1

P2 E1b

E1a

E2

F1

G5

TI4

FFA

FO C1 R3

S1 R1

R2

EE TI3 FI1

TI1

G1

SW

G2

WW

G3

G4 TI2

E3

RO

LPS NEUTRALIZATION DEGUMMING

WASHING

DRYING

BLEACHING

DEODORIZATION

Equipment items: C: column; E: heat exchanger/condenser; EJ : steam ejector; F: filter; G: pump; P: centrifugal separator; R: reactor; S: flash separator. Material streams : EA: bleaching earth & activated carbon; EE: exhausted earth; FFA: free fatty acids; FO: feedstock oil; LPS: low pressure steam; MPS: medium pressure steam; RO: refined oil; SH: sodium hydroxide; SW: soaps & waxes; V: vents; WW: Waste water; W: Water. Fig. 2. Schematization of the vegetable oil refining process. Tags represent the process variables used for model validation.

G. Landucci et al. / Journal of Food Engineering 116 (2013) 840–851 Table 2 Operative conditions of the main sections of the refining process. Process section

Operative temperature (°C)

Operative pressure (kPa)

Neutralization Degumming Washing Drying Bleaching Deodorization

20 60–70 90 90 105 230

100 100 100 5 6 0.2

843

accumulated, both in ordinary process conditions and following unexpected process deviations. For the sake of brevity only the main issues related to vegetable oil refining simulator and innovative aspects connected with the analysis of the more important equipment are summarized in the following sections. In order to highlight the complexity of the developed process simulation model and the potentialities of the method, the Supplementary information file reports samples of the UniSimÒ Design process flow diagrams (PFDs).

The process simulation model, implemented in the UniSimÒ Design software, was aimed at evaluating the energy consumption of the plant and the more critical nodes in which hexane is

2.5.1. Condensers The condensers are critical units under the point of view of energetic efficiency of the process. These units are aimed at condensing the steam outlets from the ejectors connected to the main process equipment to keep vacuum conditions (see specific description in Section 2.5.3) by the use of cooling water available in the refinery plant. Fig. 2 shows the condensers associated to the ejector of the drying section (E5), bleaching (E6) and deodorization (E7 for the first and second stage ejectors, E8 for the third stage ejector). The sample UniSimÒ Design PFD for the condenser E5 is shown in Supplementary information. The cooling water flowrate is the variable manipulated by the software (ADJ 1 operator) which determines its value by imposing a fixed temperature of 20 °C for the condensate. This implementation allows for a better stability of the model in presence of input deviations on the crude oil composition. The condenser parameters were determined after a preliminary rating operation. The typical range of cooling water flowrates, derived from actual plant design data, was imposed in a preliminary dedicated simulation model together with the geometry documented in the equipment datasheets, thus calculating in the so-called rating mode an average value for the pressure drops and heat transfer coefficient. Then, condensers are implemented in the overall simulation model by imposing the pressure drops on both tubes and shell sides, and the product of the geometry area times the overall heat transfer coefficient (‘‘design mode’’, see Honeywell (2010a) for more details). This modeling approach was associated to the condensers E5, E6 and E7 (see Fig. 2), while for condenser E8 a different approach was followed. Since this unit receives the cooling water already used in condenser E7, associated to ejectors EJ3a and EJ3b (see Fig. 2), its modeling using an a priori fixed value for the overall transfer coefficient may be inaccurate. In fact, the cooling water is manipulated to satisfy specifications on other upstream units and may vary significantly. Therefore, the so-called ‘‘rating mode’’ (see Honeywell (2010a) for more details on this procedure) was used, in which one specifies the exchanger geometry (number/ dimensions/arrangements of tubes, shell passes, etc.) and appropriate correlations are internally used to evaluate the heat transfer coefficients and pressure drops on the basis of actual flowrates.

Fig. 3. Validation of the thermodynamic model developed in UniSimÒ Design. HEX: residual hexane content in the crude vegetable oil (% by weight basis). Experimental data were derived from Smith and Wechter (1950).

2.5.2. Deodorization column The deodorization stage is aimed at removing minor components (e.g., squalene and polycyclic aromatic compounds) which cause odor and the loss of quality of the final product. The deodorization column (C1 in Fig. 2) is a stripping column made of five chambers, each fed with low pressure steam (LPS, at 1.5 bar). The total LPS mass flowrate is set as the 1.8% of the total refined oil flowrate. The hot exhausted vapors from each chamber are collected and fed to a water scrubber (C2 in Fig. 2), where the fatty acids are removed and purged. In order to reach the required strong vacuum conditions (in particular, 0.2 kPa pressure and temperature higher than 220 °C) the ejector system depicted in Fig. 2 is required. The column was modeled in the UniSimÒ Design software by implementing six separators in series, aimed at representing the five chambers of the column C1 plus the bottom of the column, in which the separation is also carried out thus reaching the

elsewhere (Honeywell, 2010b), while Appendix A summarizes the key parameters and equations used to predict enthalpy, entropy, the fugacity coefficients for each component of the mixture and thus the vapor/liquid equilibrium. In order to test the validity of the model, a comparison with available experimental data was carried out. A significant number of literature studies focuses on vegetable oil/hexane mixtures at high concentrations of hexane in the liquid phase (Fornari et al., 1994; Ceriani and Meirelles, 2004; Smith and Florence, 1951), typical of extraction processes. The only available data for diluted solutions, which are significant in the present case, are reported by Smith and Wechter (1950). Data are referred to the soybean oil/n-hexane solutions with a residual solvent content in the range 0.2–1.32% by weight. The hexane vapor pressure is measured in the experiments as a function of the temperature. The model was fitted on the experimental results by setting the triolein–hexane binary interaction coefficient to 0.095 (Honeywell, 2010b). Notice that for all other pairs of compounds, the default values of binary interaction coefficients were used. All binary interaction coefficients are reported for completeness of exposition in Appendix A. Fig. 3 reports a comparison between experimental data and values calculated with UnisimÒ Design of n-hexane partial pressure in the vapor phase as a function of temperature and hexane concentration in the oil phase. As can be observed in this figure, the model gives a quite accurate prediction with major deviations on the safe side (e.g., 17% overprediction of n-hexane vapor pressure). The data were linearly extrapolated for temperatures lower than 75 °C as already performed in a recent publication (Landucci et al., 2011), in which, however, the effect of water on the vapor phase composition was neglected and the model was set up only for the analysis of storage conditions. 2.5. Simulation model implementation

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vapor/liquid equilibrium conditions. For the first four separators an energy stream is added in addition to the LPS stripping stream in order to simulate the presence of high pressure steam (saturated steam at 40 bar) fed to internal heating coils inside the C1 column chambers in order to keep high temperature conditions. The UnisimÒ Design PFD is reported in Supplementary information.

2.5.3. Ejectors Several steam driven ejectors are used in the refinery to obtain the needed vacuum conditions in the process equipment. As evidenced in Section 2.5.1 these pieces of equipment are critical for the energy performance assessment of the refinery plant. However, no dedicated model is available in the process simulator for ejectors. Thus, a specific modeling tool was implemented in the software in order to achieve an accurate performance evaluation exploiting the UniSimÒ Design software ‘‘User Unit Operation’’

function. The function allows inserting the data derived from actual ejector systems datasheets, in particular the design curves. These curves report the entrainment ratio (1/l), given by the suction flow related to air at 20 °C respect to the motive steam flow, as a function of the ratio between the discharge and suction pressures (Pd/Ps). The curves vary according to the parameter given by the ratio between suction and motive steam pressures (Ps/Pm). The analysis of the design curves and optimization of ejector systems is extensively described in the technical literature (Meherwan, 1999; Akterian, 2011). Hence, by setting the pressures of the equipment in vacuum conditions (e.g., Ps), of the motive steam (e.g. Pm) and of the discharge (Pd) it is possible to derive by reading on the curves the entrainment ratio and calculating the necessary mass flows as follows:

1

l

X1

X2

0.001 0.002 0.005 0.010 0.020 0.050

4.14 3.81 3.38 3.03 2.70 2.26

0.983 0.910 0.732 0.673 0.615 0.489

ma 1 MS K ej

ð1Þ

where ma is the entrained flow of air at 20 °C, MS is the flow of motive steam and Kej is a correction factor for suction flows other than air, expressed as follows:

Table 3 Fitting parameters for the approximation of the ejectors design curves (see Eq. (3)). Parameter (Ps/Pm)

¼

sffiffiffiffiffiffiffiffiffiffi RS T S K ej ¼ RL T L

ð2Þ

where RS is the gas constant of suction flow, RL the gas constant of air (=287 J kg1 K1), TS the temperature (in K) of suction flow, TL the reference air temperature for the ejector (=293 K).

Table 4 Comparison between the process parameters evaluated by the model and the available field data. For tags locations, see Fig. 4. TAG

Description

Units

Model results

Field data

FI1 PI1 TI1 TI2 TI3 TI4

Refined oil exit flow Pressure in the deodorization column Temperature of the bleaching reactor Temperature of crude oil at the deodorization inlet Refined oil exit temperature Temperature of the deodorization column top side

kg/h kPa °C °C °C °C

14,558 0.2 104.8 231.7 160.9 135.8

14,075 0.22 110.1 230.7 154.8 153.0

E1 C1 C2 V1

CW2

1

2

Crude oil from neutralization

C3 C4

V2

Drying CW4

C5 E5 E6 E7 V3

CW1 3 Bleaching earth & activated carbon

CW6

Bleaching 5

H1 H2 H3 4 W1

CW3

Refined oil to storage

Deodorization

LEGEND: C CW E H V W

Condensed steam Cooling water Energy stream Low or medium pressure steam Vent Process waste Material stream tag

H4 H5 H6 E2

W2

CW5

H7 H8 H9 E3 E4

Fig. 4. Schematic representation of the heat and material balance on the analyzed plant sections.

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Table 5 Heat and material balance on the plant sections analyzed in the present study. For the identification of the streams, refer to Fig. 4. Composition is expressed in percentages by weight basis. Item

Material streams 1

Temperature (°C) Pressure (kPa) Flowrate (kg/h) Triolein (%) Water (%) n-Hexane (%) Oleic acid (%) Other (%) a b c

2 a

90.0 195.0 14,887.5 98.14 0.55 0.10 0.60 0.61

84.3 200.0a 14,795.2 98.74 0.01 0.02 0.61 0.62

3

4 a

105.2 210.0a 14,695.0 98.75 0.01 0.01 0.61 0.62

20.0 186.0 14,558.4 99.62 0.0 0.0 0.0 0.38

5b

W1

W2

25.0a 200.0a 14.8 0.0 100.0 0.0 0.0 0.0

105.0 8.0 97.8 100.0 0.0 0.0 0.0 0.0

48.4 0.2 133.0a 5.2 0.0 67.3 0.0 27.5c

Value imposed to process simulator. The stream containing bleaching earth and activated carbon is modeled as pure water. Spent bleaching earth.

Table 6 Heat and material balance on the plant utilities. For the identification of the streams, refer to Fig. 4. C = steam condensate; CW = cooling water; E = energy stream; H = steam; V = vent. ID

Physical state

Drying C1 C2 CW1 CW2 H1 H2 H3 V1 E1

section Liquid Liquid Liquid Liquid Vapor Vapor Vapor Vapor –

Bleaching section C3 Liquid C4 Liquid CW3 Liquid CW4 Liquid H4 Vapor H5 Vapor H6 Vapor V2 Vapor E2 – Deodorization section C5 Liquid CW5 Liquid CW6 Liquid H7 Vapor H8 Vapor H9 Vapor V3 Vapor E3 E4 E5 E6 E7

– – – – –

Description

Thermal power (kW)

Steam condensate associated to ejector EJ1a Steam condensate associated to ejector EJ1b Cooling water fed to the drying section condensers Cooling water exiting the drying section condensers Motive steam fed to first stage ejector EJ1a Motive steam fed to second stage ejector EJ1b Drying steam pre-heating in E1a Vent exiting from drying section Heat removed in downstream degumming section with heat exchanger Steam condensate associated to ejector EJ1a Steam condensate associated to ejector EJ1b Cooling water fed to the bleaching section condensers Cooling water exiting the bleaching section condensers Motive steam fed to first stage ejector EJ2a Motive steam fed to second stage ejector EJ2b Bleaching steam pre-heating in E1b Vent exiting from bleaching section Bleaching pre-heating

 X 2 ma X1 ¼ MS Pd =P s

ð3Þ

where X1 and X2 are fitting constants reported in Table 3 for different values of the parameter Ps/Pm. In the process simulator, for each equipment operating in vacuum conditions the suction temperature, the suction pressure and the motive steam pressure are specified as input parameters; hence the software applies Eqs. (1)–(3) to evaluate the motive

Temp. (°C)

Pressure (kPa)

150.1 1153.0 9282.0 9282.0 70.1 53.4 1153.0 70.6

19.0 127.5 8.0 18.0 175.5 175.5 127.5 123.2

16.5 250.0 150.0 149.9 900.0 900.0 250.0 108.0

301.0 30.4 1180.8 1180.8 15.6 27.6 301.0 35.4

127.5 19.8 8.0 20.0 175.5 175.5 127.5 134.0

250.0 16.5 150.0 150.0 900.0 900.0 250.0 108.0

1537.0 240,000.0 240,000.0 1100.1 157.1 26.0 33.8

19.8 8.0 12.0 175.5 175.5 175.5 132.4

102.5 150.0 140.9 900.0 900.0 900.0 108.0

142.0

11.0

Steam condensate associated to ejector EJ3 Cooling water fed to the deodorization section condensers Cooling water exiting the deodorization section condensers Motive steam fed to first stage ejector EJ3a Motive steam fed to second stage ejector EJ3b Motive steam fed to third stage ejector EJ3c Total vent flowrate exiting from deodorization section condensers (E7 and E8) C1 chambers external coil heating Steam (40 bar) for oil preheating Cooling of scrubber C2 recycle Air cooler Cooling unit

In order to obtain more realistic results, the actual datasheet of industrial ejector systems were obtained (Körting Hannover AG, 1994) inserting in the UniSimÒ Design software ‘‘Unit Operation’’ function the numerical interpolation of the design chart curves as follows:

Flowrate (kg/h)

89.0 448.0 53.0 1055.0 163.0

steam flow which is necessary to keep an imposed discharge pressure. Therefore, by varying the input conditions, e.g. due to deviations in the process (in particular, the increase of volatile compounds affect the suction flow), the energetic consumptions are evaluated by calculating the necessary motive steam flow needed to restore the optimum process conditions. 3. Results and discussion 3.1. Model validation and case study analysis In order to validate the process simulator, actual field data were derived from SALOV S.p.A. refinery during typical working

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Fig. 6. Variation of the main process parameters as a function of the increasing hexane concentration in the inlet crude oil: (a) cooling utility (water) and heating utility (ejectors motive steam) consumption; (b) hexane removal (% of the initial residual content in the crude oil) in the different refinery sections.

Fig. 5. (a) Example of optimization for ejector EJ1a/b for the base case with hexane residual content of 0.1% by weight basis; (b) optimization of intermediate pressure (Pint) as a function of different hexane residual content in the inlet crude oil (% by weight basis) for the three ejectors groups reported in Fig. 2. MS = motive steam.

Table 7 Air infiltration considered in the process vents of the different plant sections operating under vacuum conditions. Type of infiltration

operations, and compared with the ones predicted by the model in the correspondent locations. The results of the validation are reported in Table 4, in which the tags of the monitored process variables are indicated in the PFD shown in Fig. 2. As shown in the table, the simulator allows for a good reproducibility of actual process conditions, such as temperature, pressure and material streams, with a maximum relative error of 11%. The simulator was used to investigate the criticalities of the vegetable oil refining process and the influence of the residual solvent content on the process efficiency. In particular, the simulator allowed identifying the more critical nodes in which the solvent is accumulated and tracing the different sections respect to the initial crude oil content. The main hexane accumulation node is the drying flash, in which 76.6% of hexane is removed, while minor residual are accumulated in the other sections, in particular 12.7% and 10.7% respectively in bleaching and deodorization sections. Thus, a possible increase of hexane residual may lead to process efficiency decrement, in terms of motive steam consumption for the ejector system. In order to systematically quantify the refinery energy consumption and to determine the critical factors affecting the efficiency, the process simulator results were analyzed. Fig. 4 reports the block diagram of the process evidencing the main material streams together with the energy and utility lines. The main product streams are marked together with the possible process vent and wastes/residuals (respectively labeled with ‘‘V’’ and ‘‘W’’ in Fig. 4). The cooling utility is mainly water (‘‘CW’’ in Fig. 4), while steam at different pressures is the heating utility, also

Type 1 Type 2a Type 3 a

Air infiltration (kg/h) Drying

Bleaching

Deodorization

3 5 8

3 5 8

3 6 10

Value derived from manufacturer data (Körting Hannover AG,1994).

employed in the mentioned ejector system. The steam entering each block are labeled with ‘‘H’’ in Fig. 4, while the exit condensate is labeled with ‘‘C’’. In order to simulate further heat exchanges in and out of the simulator boundaries and passing between unit operations (steam coils, air coolers, etc.) several ‘‘energy streams’’ were added to the scheme (labeled with ‘‘E’’ in Fig. 4) using a specific UniSimÒ Design software function. The quantification of the heat and material balance for the scheme (Fig. 4) is reported in Tables 5 and 6, respectively for process streams and utilities. As can be seen in Table 5, the oil content (schematized as pure triolein) increases passing through the different sections. The major part of water is eliminated in the drying section, as expected, while the acid fats content, residual of the upstream neutralization is totally removed in the deodorization section. Considering the energy consumptions, synthetically represented by the results shown in Table 6, the bleaching section features the lowest thermal requirements, both in terms of hot and cold utilities. On the contrary, it clearly appears that the most critical section, under the point of view of energy requirements, is the

G. Landucci et al. / Journal of Food Engineering 116 (2013) 840–851

Fig. 7. Comparison between the flammability range of hexane considering two inert reference gases (carbon dioxide and nitrogen) and vapor concentration in the venting line for (a) drying, (b) bleaching and (c) deodorization considering a residual hexane content of 0.1% by weight basis in the inlet crude oil. For air infiltration types characterization, see Table 7.

deodorization. Both steam and cooling water utilities have the highest requirements in order to keep the severe operative conditions imposed by the process. In particular, low pressure (0.2 kPa) leads to major motive steam consumption and associated cooling water for condensation, while the high operative temperature of the column (230 °C) is kept also by the use of additional heating (energy stream E4 in Table 6) carried out with high pressure steam. Besides, additional heat exchangers are needed for cooling the scrubber C2 (see Fig. 2) recycle and the vents before the treatment and the discharge in the atmosphere.

3.2. Process optimization and sensitivity analysis The analysis of the refinery in the baseline case (0.1% hexane by weight basis in the inlet crude oil) highlighted the criticalities related to the energy consumptions in the refinery low pressure units (e.g., drying, bleaching and deodorization). Since the ejector systems operative conditions affect the whole refinery energetic performance, the process simulator was applied in order to optimize

847

the operating conditions for the minimization of motive steam consumption. The optimization was carried out on the three ejector systems considering that the motive steam is available in the plant at the same pressure (medium pressure steam, MPS at 9 bar). Fig. 5a reports an example of optimization, in particular related to the ejector system connected to the drying flash drum (EJ1a/b with condenser E5, see Fig. 2). As can be seen in the scheme, the ejector is constituted by two different sections in which Ps is the suction pressure, representative of the equipment operative conditions, Pout the system discharge pressure, MSA and MSB the motive steam streams respectively for the first and second stage, and Pint is the intermediate pressure, which is the degree of freedom (DOF) to specify for the optimization. The optimization is carried out by varying both MSA and MSB and finally obtaining the Pint which minimizes the overall steam consumption (e.g., the sum of MSA and MSB), as shown in Fig. 5a. Determining the intermediate ejectors pressure allows for the process energetic efficiency enhancement. The described optimization method can be performed also by considering a possible increase of the inlet residual hexane content, as reported in Fig. 5b. In particular the figure shows the optimized intermediate pressure for all the considered ejector systems (see Fig. 2 for tags and equipment representation). These outcomes might be potentially applied when a different feedstock quality is accepted and processed by the refinery for a mid- or long-term period, with the need of a systematic improvement of the operating conditions. As shown in Fig. 5b, the increase of the residual hexane content has a stronger influence on the drying and bleaching sections respect to the deodorization, since in these sections the major part of hexane is removed (see Section 3.1). This results in the increase of the intermediate pressure for optimizing the motive steam consumption. The results of the sensitivity analysis carried out by varying the inlet hexane concentration and optimizing the operating conditions and process variables are reported in Table B1 of Appendix B. The table allows determining the optimized operating conditions referring to the base case discussed in Section 3.1. On the basis of the sensitivity analysis results, the overall utilities requirements were derived and shown in Fig. 6. Fig. 6a shows the increase of the overall motive steam and cooling water consumption by varying the inlet hexane concentration of one order of magnitude (e.g., ranging from 0.1% to 1.5% by weight basis). Motive steam consumption is increased by 40%, showing a more significant variation respect to cooling water utility, which increase is limited to 1%. This is due to the fact that the highest flowrate of cooling water is a fixed simulation parameter, since it is fed to the condenser of the third ejector (EJ3c, see detailed description of simulation set up in Section 2.5.1). This flowrate is almost twenty times higher than the sum of the other cooling water utilities, which can be varied in order to control the condensate temperature (see Section 2.5.1). In order to determine the variation in the process vents behavior due to the increase of inlet hexane concentration, Fig. 6b presents the change in the hexane removal percentage (thus, starting from the values evaluated at 0.1% residual hexane content, see Section 3.1) in each process section. The results highlight that the excess hexane is mainly removed in the drying section, due to the oversizing of the equipment. Hence, this allows decreasing the hexane amount fed to the downstream units, which hexane removal decreases as shown in Fig. 6b. Therefore, the sensitivity analysis allowed determining the change in process parameters and utility requirements for restoring the process operating conditions given unforeseen changes of the inlet feedstock. It clearly appears that the increase of volatile solvent residual has a negative impact on the energetic costs of the refining process.

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3.3. Formation of flammable mixtures inside process streams The process simulator pointed out the more critical nodes for hexane accumulation, also considering the potential variation of the initial hexane residual in the process feed. Among the possible hazards related to the presence of hexane inside process pipes, one of the critical issues is related to the possibility of air entrainment from gaskets and seals in strong vacuum operating pipes, thus leading to the formation of flammable mixtures in confined spaces. This might lead to fire and explosion hazards in case of accidental ignition of the flammable mixture, already highlighted for the storage equipment in a previous work (Landucci et al., 2011). Therefore, the process simulator was employed to investigate this problem, considering an additional air flow in the three vent lines (V1, V2 and V3, see Fig. 4) given a reference air entrainment value, specified by the ejector manufacturer (Körting Hannover AG,1994) for the vent discharge line. Table 7 reports the considered entrainment value (infiltration type 2), also considering a possible negative or positive variations respect to this reference value (respectively, infiltration types 1 and 3 in Table 7). Fig. 7 reports the evaluated residual hexane concentration in the vent lines evidencing the possibility of formation of flammable mixtures in the drying (Fig. 7a), bleaching (Fig. 7b) and deodorization (Fig. 7c) sections as a function of the different air entrainment rates given a fixed hexane residual content in crude oil feed (e.g.,

0.1% by weight basis). A flammable mixture is potentially formed if the calculated concentration point enters inside the flammable range, i.e. the region of the chart included inside the reference continuous lines. In absence of data for water as inerting fluid, the effect of nitrogen (bright lines in Fig. 7) and carbon dioxide (dark lines in Fig. 7) as diluents was taken into account in order to obtain preliminary indications for the methodology (Mashuga and Crowl, 1998; Zabetakis, 1965). Furthermore, the flammability range is affected by operative pressure and temperature, but the use of data referred to 25 °C temperature and 1.01 bar allows for evaluation of the flammability hazards on the safe side in the considered process sections (Lees, 1996). The results make clear that in the case of higher hexane concentration in the vent line, the entrained air is not sufficient to form flammable mixtures, thus leading to a less hazardous situation. This is the case of the drying section, in which the major part of hexane is removed and, as shown in Fig. 7a, and in which none of the calculated points fall under the flammable region even for high air entrainment rates. On the contrary, for the other two sections, the hexane vent content is lower and some points calculated for high air entrainment rates especially in the deodorization section vent (see Fig. 7c), fall into the hazardous zone. This evidences a safety criticality for strong vacuum operating equipment in presence of flammable vapors. Hence, this type of hazard might be taken into account during the vent pipeline design and in maintenance operations.

Table A1 Main parameters and equations implemented in the thermodynamic model (Honeywell, 2010b). ID

Equation

Description

Parameters

Eq. (1)

RT a P ¼ Vb  VðVþbÞþbðVbÞ

Peng–Robinson state equation

Eq. (2)

Z3 - (1 - B)Z2 + (A - 2B - 3B2)Z - (AB - B2 - B3) = 0

Peng–Robinson expressed in terms of the compressibility factor Z

Eq. (3) Eq. (4) Eq. (5)

A = aP/(RT)2 B = bP/(RT)2 P RT c;i b¼ N i¼1 xi bi ; bi ¼ 0:077796 Pc;i

Parameter in Eq. (2) Parameter in Eq. (2) 1st Peng–Robinson equation coefficient for mixtures

Eq. (6)

a¼

P = Pressure (Pa) R = 8314 (J kmol1 K1) universal gas constant T = Temperature (K) V = Volume (m3) a = see Eq. (6) b = see Eq. (5) Z = Compressibility factor = (PV)/ (RT) A = see Eq. (3) B = see Eq. (4) a = see Eq. (6) b = see Eq. (5) xi = mass fraction of the ith component of the mixture of N components. Tc,i = critical temperature of the ith component Pc,i = critical pressure of the ith component Tr,i = T/Tc,i kij = system specific experimental binary interaction factor mi = see Eq. (7) xi = Acentric factor of the ith component

PN PN i¼1

2nd Peng–Robinson equation coefficient for mixtures – original formulation

0:5 ð1  kij Þ; ai ¼ ac;i i j¼1 xi xj ðai aj Þ ðRT c;i Þ2 0:5 ; ¼ 1 þ mi ð1  T 0:5 r;i Þ i Pc;i

ac;i ¼ 0:457235

a

a

Eq. (7)

mi ¼ 0:37464 þ 1:5422xi  0:26992x2i ; xi 6 0:49 mi ¼ 0:379642 þ ð1:48503  ð0:164423  0:016666xi Þ xi Þxi ; xi > 0:49

Polynomial factor for Eq. (6) – original formulation

Eq. (8)

ai ¼ T Nr;ii =ðMi 1Þ expðLi ð1  T Nr;ii Mi ÞÞ

Twu Alpha function for Peng– Robinson correction for Eq. (6)

Eq. (9)

HHID RT

Eq. (10)

SSID R

Eq. (11)

  V þð20:5 þ1Þb da ln V þð20:5 1Þb ¼ Z  1  21:51bRT a  T dT ¼ lnðZ  bÞ  lnðP=P  Þ 



A T da 21:5 bRT a dT



ln



Vþð20:5 þ1Þb Vþð20:5 1Þb

Enthalpy equation 

      PN bi Pb 1 0:5 ln /i ¼  ln Z  RT þ ðZ  1Þ bbi  1:5a 2a0:5 j¼1 xj aj ð1  kij Þ  b  i 2 bRT a   0:5 ln V þð20:5 þ1Þb V þð2

1Þb

Entropy equation

Evaluation of fugacity coefficient

Li, Mi, Ni = Parameters of pure ith substance (see details in Honeywell (2010b)) H = predicted enthalpy HID = reference enthalpy evaluated at 25 °C and 1.01 bar S = predicted entropy SID = reference entropy evaluated at 25 °C and 1.01 bar P° pressure in the reference state (1.01 bar) / = mixture fugacity coefficient of for the ith component

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4. Conclusions

Acknowledgement

In the present work a quantitative methodology was developed for the performance analysis of the vegetable oil refining process. An advanced thermodynamic model was implemented in order to reproduce the vapor/liquid equilibrium of crude vegetable oil – residual solvent system. The model was validated against available experimental data and was implemented in the refining process simulator, developed on the Honeywell UniSimÒ Design software. The simulator allowed for a detailed performance analysis of the process. The results were compared with field data obtained from an actual vegetable oil refinery showing good agreement in reproducing the refining process in the reference conditions. The effect of the residual solvent content increase on the process efficiency was investigated, determining the most significant nodes of solvent accumulation among the plant process operations and evaluating its influence on the global energy requirements. In particular, the ejector systems, aimed at keeping vacuum operating conditions, were deeply investigated, evaluating the utility consumption increment. Both motive steam and cooling water for condensers were analyzed by varying the residual hexane content in the input crude oil and determining the modification in the operative conditions for minimizing the energy costs. The study evidenced the criticalities related to the management of inlet crude oil quality, in terms of residual solvent content control, for the enhancement of the global process efficiency. Finally, the simulator also allowed investigating the potential hazards due to formation of flammable mixtures inside the process vent lines, in presence of purged hexane vapors and air entrained by gaskets and/or seals of vacuum operating pipelines. The results evidenced the conditions in which flammable mixtures might potentially be formed inside the process vents, with fire and explosion hazards in presence of accidental ignition.

The authors gratefully acknowledge financial support received from Regione Toscana (Bando Unico R&S n.2009DUA/526090469/ 1). Appendix A The present section provides details on the thermodynamic model implemented in UnisimÒ Design (Honeywell, 2010a,b). The selected model is based on the Peng–Robinson equations (Peng and Robinson, 1976) corrected with the Twu Alpha function (Twu et al., 1995; Honeywell, 2010b), which takes into account the excess free energy in order to have more accurate prediction of vapor pressure. Table A1 summarizes the key parameters and equations used to predict enthalpy, entropy, the fugacity coefficients for each component of the mixture and thus the vapor/liquid equilibrium. Tables A2 and A3 report the specific parameters selected for each substance considered in the present study. Appendix B Table B1 reports the results of the process optimization and sensitivity analysis, comparing the baseline case results (BC) and the optimized cases (OCs) by varying the residual hexane content (HEX in the following, expressed in % by weight basis) up to one order of magnitude respect to the BC, which features HEX = 0.1%. The first column of the table reports the process variable of interest (EJ: ejector, MS: motive steam, CW: cooling water, see Figs. 4 and 5). The second column report the results obtained for the baseline case with HEX = 0.1%, while the third column shows the correspondent optimization of process variables aimed at

Table A2 Main parameters selected for the present analysis (Honeywell, 2010b). For parameters definition see Table A1. Parameter (see Table A1)

Equation (see Table A1)

Units (SI)

Assigned parameter for each component – UnisimÒ Design library Triolein

Oleic acid

n-Hexane

n-C29H60

Sterols

Tocopherols

Water

Tc,i Pc,i Li Mi Ni L0 M0 N0 L1 M1 N1

5 5 8 8 8 see see see see see see see

°C kPa – – – – – – – – – –

680.9 360.2 –a –a –a 0.1253 0.9118 1.9482 0.5116 0.7841 2.8125 1.6862

496.9 1390 0.7760 0.8235 0.8235 – – – – – – –

234.7 3032 0.1363 0.8620 0.8620 – – – – – – –

564.9 826 0.3688 0.8247 0.8247 – – – – – – –

668.1 999.7 –a –a –a 0.1253 0.9118 1.9482 0.5116 0.7841 2.8125 0.9863

646.7 945.9 –a –a –a 0.1253 0.9118 1.9482 0.5116 0.7841 2.8125 0.9624

374.1 22,120 0.3831 0.8701 0.8701 – – – – – – –

xi

note note note note note note note

(a) (a) (a) (a) (a) (a) (a)

a

The parameters Li, Mi and Ni depend on individual compounds and were retrieved from UniSimÒ Design library for the application of Eq. (8) of Table A1. Nevertheless, for ð0Þ ð1Þ ð0Þ ð0Þ N0=ðM01Þ non-library compounds, the Twu alpha function can be estimated by the following expressions: ai ¼ ai ðTÞ þ xi ðai ðTÞ  ai ðTÞÞ where ai ¼T r;i ð1Þ N1=ðM11Þ expðL0ð1  T N0M0 ÞÞ; ai ¼ T r;i expðL1ð1  T N1M1 ÞÞ; T r;i ¼ T=T c;i . r;i r;i In this case, Table A2 reports the relevant parameters for the estimation of the Twu alpha function (L0, M0, N0, L1, M1, N1 and xi).

Table A3 Determination of system specific binary interaction factor ki,j (i: columns; j: rows) (see Eq. (11) in Table A1). ki,j i ? j;

Triolein

Oleic acid

n-Hexane

n-C29H60

Sterols

Tocopherols

Water

Triolein Oleic acid n-Hexane n-C29H60 Sterols Tocopherols Water

– 0 0.095 0 0 0 0

0 – 0 0 0 0 0

0.095 0 – 0.031 0 0 0.48

0 0 0.031 – 0 0 0.48

0 0 0 0 – 0 0

0 0 0 0 0 – 0

0 0 0.48 0.48 0 0 –

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Table B1 Results of the sensitivity analysis. BC = base case; OC: optimized case; RHC: residual hexane content. Process variable

EJ1a/b operative pressure (kPa) EJ2a/b operative pressure (kPa) EJ3a/b operative pressure (kPa) EJ3c operative pressure (kPa) MSA for EJ1a/b flowrate (kg/h) MSB for EJ1a/b flowrate (kg/h) MSA for EJ2a/b flowrate (kg/h) MSB for EJ2a/b flowrate (kg/h) MSA for EJ3a/b flowrate (kg/h) MSB for EJ3a/b flowrate (kg/h) MSA for EJ3c flowrate (kg/h) CW1&CW2 flowrate (kg/h) CW3&CW4 flowrate (kg/h) CW5&CW6 flowrate (kg/h) a

HEX = 0.1%

HEX = 0.5%

HEX = 1.0% a

BC

OC

BC

OC

BC

OCa

16.5 16.5 2.8 20.0 70.1 53.4 15.6 27.6 1100.1 157.2 26.0 9282.2 1180.8 240,000.0

14.0 20.0 2.9 11.0 55.2 64.1 20.7 21.7 1138.3 55.0 50.3 9061.0 1316.0 240,000.0

16.5 16.5 2.8 20.0 90.4 177.1 18.8 47.5 1113.9 216.6 42.0 10,330.0 1375.0 240,000.0

25.5 22.5 3.0 12.0 165.7 66.0 28.6 32.7 1171.7 78.7 69.0 12,026.2 1664.2 240,000.0

16.5 16.5 2.8 20.0 115.2 342.3 22.2 68.7 1127.3 275.4 61.3 11,604.5 1637.4 240,000.0

26.0 24.0 3.0 12.0 223.1 63.0 38.4 42.5 1205.3 95.7 94.5 14,534.7 2099.6 240,000.0

Respect to the base case.

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