ARTICLE pubs.acs.org/IECR
Dynamic Simulation of Rosemary Essential Oil Extraction in an Industrial Steam Distillation Unit Rafael B. Sartor,† Argimiro R. Secchi,*,‡ Rafael de P. Soares,† and Eduardo Cassel§ †
Department of Chemical Engineering, Federal University of Rio Grande do Sul Program of Chemical Engineering, COPPE, Federal University of Rio de Janeiro § Faculty of Engineering, PUCRS ‡
ABSTRACT: This work presents the dynamic simulation of rosemary essential oil extraction by steam-distillation. Experimental data acquired in an industrial steam-distillation unit were used to estimate the model parameters. Two distinct climatic conditions (summer and winter) were used to verify the model flexibility. In winter condition, two time-dependent experimental data curves were used together to verify the parameter estimation strategy, whereas in summer condition, two time-dependent curves were used for parameter estimation and a third one for model validation with respect to prediction. The mathematical model fitted satisfactorily both set of industrial data, resulting, for the summer and winter, in global mass transfer coefficient of 8.64 10-4 s-1 and 9.10 10-4 s-1, and equilibrium constant 2.72 10-4 m3/kg and 1.07 10-2 m3/kg, respectively.
1. INTRODUCTION Essential oils from rosemary (Rosmarinus officinalis L.) have a strong contribution on international market, mainly as additives in the production of food and perfume products. In aromatherapy, the rosemary essential oil can be ingested pure or diluted in alcohol, mixed in honey or drugs as it has medicinal properties.1 In addition, rosemary extracts are used in medicine as antiinflammatory agents,2 antimicrobial,3 and it has also shown antibacterial activity.4 The lack of technology is one of the reasons that hinder the accurate description of essential oil extraction process by steamdistillation on industrial scale. Some aspects are important in this regard as lack of experimental information about extraction behavior along the bed and the difficulty in getting accurate data on the total amount of oil in aromatic plants. The latter is defined as the maximum amount of essential oil that can be extracted from aromatic plants and therefore varies with the extraction process and operating conditions of the process. The difference is in the raw material, which shows significant variations depending on climatic conditions and geographic regions where they are grown, making the processes involving natural products unique in the chemical sector, unlike the vast majority of chemical processes, where the properties of the raw materials are almost constant. Thus, the development of a generalized model to describe steam-distillation process for different aromatic plants is of great interest. Steam distillation is a traditional process to obtain essential oils from aromatic plants. The industry uses this method because it is simple and inexpensive when compared to more technologically advanced methods such as supercritical fluid extraction.5 Some studies have been conducted with the aim of mathematical models to represent extractive processes behavior by steamdistillation. Cassel and Vargas5 used the Fick’s second law to represent the extraction of essential oil of citronella in laboratory scale. Benyoussef et al.6 applied steam-distillation for essential oil extraction of coriander fruits (Coriandum sativum L.). The r 2011 American Chemical Society
authors model the extraction as an irreversible desorption of essential oil in water, with first-order rate and flashing due to the formation of phase equilibrium. Intraparticle diffusion of oil in water was considered the controlling step. Sovova and Aleksovski7 developed a model for hydrodistillation and steamdistillation assuming the essential oil as pseudocomponent and the diffusion of solute to the particle surface as the controlling step. This model is applied when the aromatic plant is submerged in water and all essential oil is free and available on the particle surface. Cerpa8 and Cerpa et al.9 modeled the steam-distillation process on semicontinuous mode, where the extraction time was separated in two steps: warming time that begins with the injection of steam in the extraction vessel until obtain the first drop of essential oil; and time of obtaining the product, which ends when there is no more oil to be extracted. This mathematical model considers the fixed bed consisting of leaves and stems, with random and heterogeneous distribution, and was applied in a pilot steam-distillation unit. In the present work, a theoretical-experimental study of the dynamic of extraction by steam-distillation on an industrial scale is carried out. The essential oil extraction experiments of rosemary (R. officinallis) were performed at the company Tekton Essential Oils, in the state of Rio Grande do Sul (Southern Brazil). A mathematical model, based on literature,10 was developed and implemented in the EMSO (Environment for Modeling Simulation and Optimization) simulator,11 in order to represent the yield of essential oils as function of the extraction time.
2. EXPERIMENTAL PROCESS Plant materials were collected randomly from the Experimental Farm of the Tekton Essential Oils in the state of Rio Grande Received: July 26, 2010 Accepted: February 14, 2011 Revised: February 11, 2011 Published: March 07, 2011 3955
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Figure 1. Schematic diagram of the steam-distillation industrial unit: (1) wood-fired boiler, (2) extractor vessel, (3) coil condenser, (4) liquid-liquid separator vessel.
do Sul, Southern Brazil (latitude, 30°050 S; longitude, 50°470 W; altitude, 100 m). The extraction experimental data were obtained in an industrial steam-distillation unit of the Tekton Essential Oils (Figure 1). The steam is generated in a wood-fired boiler, with pressure of 686.5 kPa. An expansion valve reduces the pressure to 117.7 kPa, producing slightly superheated steam with an average temperature of 380 K. The raw material, composed of leaves and stems of fresh plant, was loaded into the extractor vessel, forming a packed fixed bed. The steam is injected at the bottom of the extractor vessel through a distributor, heats the raw material in the packed bed, and vaporizes the most volatile portion of the essential oil. The oil is solubilized in the vapor stream and dragged to the top of the extractor vessel. The vapor mixture leaves the extractor vessel and is condensed in the cooling coil by indirect contact with cooling water at room temperature. The condensed mixture feeds the liquid-liquid separator vessel in which the essential oil phase is separated from the aqueous phase by density difference. Data from five experiments of rosemary essential oil distillation were collected at same process operating conditions in the industrial steam-distillation unit. Two of them were carried out in winter and are called W457 and W352. The other three experiments were carried out in summer, here called S630, S580, and S430. The heating of fixed bed was performed in the same way for all experiments by injecting steam directly to the aromatic plants at the same volumetric flow rate, 0.1 m3/min. The height and weight of the dry bed for all experiments are showed in Table 1. The rosemary essential oil density is 812 kg/m3 at 298 K. The essential oil volume is determined in graduated tube, and the yield was calculated on a wet weight basis from the relationship between essential oil mass, obtained from experimental volume and density, and mass of leaves of the fresh aromatic plants. Some of these results as function of time are showed in Table 2. 2.1. Analysis. The composition of the oil was carried out by gas chromatography on Agilent 7890A GC equipped with a mass spectrometer Agilent 5975C and a HP-5MS silica capillary column (30 m 250 μm i.d.), coated with 5% phenyl methyl silox (0,25 μm phase thickness). The column temperature was 60 °C for 8 min, rising to 180 at 3 °C/min, 180-250 at 20 °C/min, then 250 °C for 10 min. The injector temperature was 250 °C;
Table 1. Steam Distillation Experiments Data experiment
rosemary mass plant (kg)
fixed bed height (m)
W457
457
1.7
W352 S630
352 630
1.3 2.3
S580
580
2.2
S430
430
1.6
detector temperature 280 °C; split injection mode with split ratio of 1:55; volume injected of 0.2 μL of oil. The carrier gas was helium, flow rate 1 mL/min; interface temperature 250 °C; acquisition mass range, m/z 40-450. Comparison of fragmentation patterns in the mass spectra with those stored on the GC-MS databases12 was also performed. The GC/MS analysis of the oil was performed in Agilent 6890 equipped with Adams12 library. Identification of the volatile compounds was based on a comparison with the spectral data.12 The majority compounds identified in the oil obtained by steam-distillation were R-pinene, camphene, sabinene, limonene, 1,8 cineole, camphor, verbenone, and β-caryophyllene.
3. MATHEMATICAL MODELING The mathematical modeling of steam-distillation is an important step in design of industrial units, used to simulate and optimize the process. Therefore, the appropriate mathematical representation of the physical phenomena that occur during essential oil extraction is the purpose of this study, aiming the reduction of experimental procedures in process design and operation. Many authors have modeled the essential oils extraction using supercritical fluid.13-15 Although the fluid phase models are similar and the differences are in terms of mass transfer in the solid phase it is possible to adapt the model structure used in the supercritical fluid extraction process to model the steam-distillation process. The steam-distillation has been widely used for several centuries, but only a few attempts have been reported by chemical engineers in search for a deeper understanding of the whole process under a phenomenological point of view.9 Since 3956
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Table 2. Experimental Data of Accumulated Volume and Yield of the Rosemary Essential Oils W457 V (cm3)
t (min)
W352
yield (%)a
V (cm3)
S630
yield (%)a
yield (%)a
V (cm3)
V (cm3)
S430
yield (%)a
V (cm3)
yield (%)a
0
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
10
195
0.037
120
0.029
625
0.090
580
0.091
309
0.058
20
359
0.102
342
0.084
1810
0.262
1748
0.275
911
0.172
30
929
0.176
558
0.137
2715
0.394
2573
0.406
1389
0.263
40
1262
0.239
728
0.179
3188
0.464
3000
0.474
1702
0.322
50
1504
0.286
843
0.208
3547
0.516
3338
0.528
1904
0.360
60
1686
0.320
939
0.231
3709
0.540
3592
0.568
2025
0.383
70 80
1824
0.347
3821 3920
0.556 0.571
3721 3824
0.589 0.605
2134 2227
0.404 0.422
3954
0.576
2270
0.430
90 a
S580
The yield is expressed by essential oil mass per total fresh mass.
the extraction curves behavior for both processes are similar, it is possible to use the knowledge associated with supercritical extraction to model the steam-distillation. A dynamic mathematical model was developed to describe the industrial steam-distillation process. The model assumptions are the following: • The oil extracted is composed of several chemical components, but it was considered as being represented by one pseudocomponent. • Steam is uniformly distributed in radial sections of the extractor and its velocity is considered constant along the bed height. • The fixed bed porosity is not affected by bed compacting that occurs during the extraction process. • The phase equilibrium is described by a linear relationship of the solute concentration. • The mass transfer between solid and fluid phases are governed by a linear driving force. • Oil concentration in the fluid and solid phases depend on the extraction time and axial coordinate. • The axial dispersion is neglected. • Temperature and pressure are considered constant during extraction. The distribution of the pseudocomponent in the fluid and solid phases is obtained through the mass balance in the extractor vessel. It is assumed that the oil concentration in aromatic plants is homogeneous and dispersed throughout the bed. Based on the above assumptions, the model is composed by a one-dimensional mass balance for the essential oil10 as presented by eqs 1 and 2. Fluid phase: DCðz, tÞ DCðz, tÞ ð1 - εÞ Dqðz, tÞ ¼ -u Fs Dt Dz ε Dt
ð1Þ
Solid phase: Dqðz, tÞ ¼ - kMT ½qðz, tÞ - qðz, tÞ Dt
ð2Þ
where C(z,t) is the oil concentration in the fluid phase (goil/ m3steam), q(z,t) is the oil content in aromatic plant (goil/kgbed), u is the interstitial velocity (m/s), ε is the bed porosity; kMT is the overall mass transfer coefficient (s-1); Fs is aromatic plant density (kgbed/m3bed). The equilibrium relationship between
Table 3. Measured and Estimated Initial Essential Oil Content q0(goil/kgbed) experiments
experimental
predicted
W457
3.5
4.6
W352
2.4
3.0
S630
5.6
5.7
S580
6.0
6.2
S430
4.2
4.2
phases, q*(z,t), is described as a linear relationship: qðz, tÞ ¼ Keq Cðz, tÞ
ð3Þ
3
where Keq is the equilibrium constant (m steam/kgbed). The initial and boundary conditions are: Cðz, 0Þ ¼ 0 and qðz, 0Þ ¼ q0 for all z
ð4Þ
Cð0, tÞ ¼ 0 for all t
ð5Þ
The accumulated mass of oil along the bed height is evaluated by integrating eq 6 together with eqs 1 and 2. Dmðz, tÞ ¼ Cðz, tÞQ Dt
ð6Þ
where Q is the steam volumetric flow rate (m3/s) and m(z,0) = 0 as initial condition. 3.1. Results. In order to solve the proposed model in the EMSO simulator, the set of partial differential equations was solved by the method of lines by discretizing the spatial variable, resulting in a system of ordinary differential equations. The fixed bed length, of height H, was divided in N equal intervals and backward finite differences were used to discretize the spatial derivative, yielding to a system of 3N ordinary differential equations. The multiple steps integrator DASSLC (Differential-Algebraic System Solver in C)16 was used to solve the resulting system of ordinary differential equations. The mass transfer coefficient, kMT, and the phase equilibrium constant, Keq, were estimated minimizing the sum of squares errors using the flexible polyhedrons optimization algorithm.17 The dynamic simulation and the estimation strategy were already implemented in the EMSO software and only the system of ordinary differential equations had to be implemented in the simulator. 3957
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Figure 2. Steam distillation yield extraction curves vs time: winter experimental data (O W457 and Δ W352) and mathematical model simulation (——).
The yield curve was obtained from ratio of extracted oil mass and the initial mass of the fixed bed. Table 3 shows the experimental and predicted amounts of oil present in aromatic plants, by measuring the amount of oil extracted up to the extraction final time and estimating this amount (parameter q0) individually for each experimental curve. The initial oil content varies with climatic conditions, being a difficult parameter to predict accurately. However, according to the obtained results, it is possible to say its estimated value represents adequately the experimental data, despite of having different values for each experiment. The experiments W457 and W352 were carried out according to the traditional operating procedure of Tekton Company. Since the extraction did not reach complete exhaustion, the experimental value cannot be used directly in the simulations. Therefore, the value of q0 was estimated for all experiments. First, two experimental extractions curves, W457 and W352, were used together in order to verify the model suitability. The estimated value of the parameter kMT was (9.10 ( 0.11) 10-4 s-1 and Keq was (1.07 ( 0.02) 10-2 m3steam/kgbed using both experimental runs. Figure 2 shows the simulation curves for these experiments, showing the good agreement with experimental data. Similarly, for the summer experiments, the parameters were estimated using two experimental runs together, S630 and S430 (Figure 3). The mass transfer coefficient, kMT, estimate was (8.64 ( 0.23) 10-4 s-1 and the equilibrium constant, Keq, estimate was (2.72 ( 0.29) 10-4 m3steam/kgbed. The simulations were performed for extraction time greater than that achieved in experiments, showing that there is small variation in extraction yield after the experiment was stopped. The third experimental extraction curve, S580, was used to test the model with respect to prediction. In this simulation, the parameters kMT and Keq estimated from S630 and S430 experimental data were used. Figure 4 shows the good performance of the model for this experiment that has the higher initial oil content. The result of Figure 4 is not a fit, but a prediction based on the parameters previously determined (results from Figure 3). In this way, it is possible to simulate all summer experiments with the same set of estimated parameters; the only experiment-dependent parameter is q0. The experiments W457 and W352 showed worse performance than summer experiments, as consequence, the equilibrium
ARTICLE
Figure 3. Steam distillation yield extraction curves vs time: summer experimental data (O S630 and Δ S430) and mathematical model simulation (——).
Figure 4. Steam distillation yield extraction curve vs time: summer experimental data (O S580) and mathematical model simulation (——).
constant had very different value than in the experiments S630 and S430. An important fact to be considered is the season when the experiments were conducted, because the weather conditions may influence the thermodynamic equilibrium resulting in a slower extraction process. The essential oil production is reduced in winter since the membrane that cover the essential oil become more rigid, decreasing the accessibility of the essential oil. This occurs because the amount of sunlight is much reduced when compared with summer season, and the temperature of the extraction process is not high enough to completely expose the essential oil. This leads to different responses between winter and summer experiments. The experimental runs clearly show a difference in the slope of extraction curves between seasons, mainly in the first stage of extraction (where the solubility is the limiting factor), resulting in the observed differences in the equilibrium constant Keq values. The change between the stage limited by solubility and by mass transfer is not well-defined in winter, while this fact is easy to observe in summer. Some authors work with the principle of easy-access (solubility) and 3958
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Industrial & Engineering Chemistry Research hard-access (mass transfer) to extract essential oil by solvent, separating the extraction curve in two stages. Benyoussef et al.6 applied this principle in steam-distillation, while Sovova and Aleksovski7 applied the same theory for a supercritical extraction process. In the present work these effects are being modeled by assuming different values of Keq.
4. CONCLUSIONS The mathematical model was capable to represent the yield curve of essential oil extraction as function of time from experimental data of an industrial steam-distillation process. This is an important result because studies in this area of knowledge are grounded in experimental data in laboratory scale and the difference in scale significantly changes the extraction process behavior.5 The mathematical model has good predictive ability regarding the extraction behavior, being hard to predict how much essential oil can be extracted, since this depends on parameters beyond the control of extraction process as the initial oil content, q0. The mass transfer coefficient presented small difference between the two sets of climatic condition, while the equilibrium constant is the cause of the difference of extraction rate in summer and winter. The sensitivity of the raw material to the climatic conditions suggests the companies to take into account the weather to define the operating conditions of the steamdistillation process. Therefore, it is necessary a database containing several experimental data, obtaining a group of parameters that can represent the extraction.
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(9) Cerpa, M. G.; Mato, R. B.; Cocero, M. J. Modeling steam distillation of essential oils: application to lavandin super oil. AIChE J. 2008, 54 (4), 909–917. (10) Reverchon, E. Mathematical modeling of supercritical extraction of sage oil. AIChE J. 1996, 42, 1765–1771. (11) Soares, R. P.; Secchi, A. R. EMSO: A new environment for modeling, simulation and optimization. In Proceedings of 13th European Symposium on Computer Aided Process Engineering; Lappeenranta, Finland, 2003, 947-952. (12) Adams, R. P. Identification of Essential Oils Components by Gas Chromatography/Mass Spectrometry, 4th ed.; Allured: Carol Stream, IL, 2007. (13) Reis-Vasco, E. M. C.; Coelho, J. A. P.; Palavra, A. M. F.; Marrone, C.; Reverchon, E. Mathematical modeling and simulation of pennyroyal essential oil supercritical extraction. Chem. Eng. Sci. 2000, 55, 2917–2922. (14) Reverchon, E.; Marrone, C. Modeling and simulation of the supercritical CO2 extraction of vegetable oils. J. Supercrit. Fluids 2001, 19, 161–175. (15) Zizovic, I.; Stamenic, M.; Orlovic, A.; Skala, D. Supercritical carbon dioxide essential oil extraction of lamiaceae family species: Mathematical modeling on the micro-scale and process optimization. Chem. Eng. Sci. 2005, 60, 6747–6756. (16) Secchi, A. R. DASSLC: user’s manual - v3.6 (DifferentialAlgebraic System Solver in C). 2009, http://www.enq.ufrgs.br/englib/ numeric (accessed April 5, 2010). (17) Nelder, J. A.; Mead, R. A simplex-method for function minimization. Comput. J. 1965, 7 (4), 308–313.
’ AUTHOR INFORMATION Corresponding Author
*Address: Program of Chemical Engineering, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68502, CEP 21941-972 Rio de Janeiro, RJ - Brazil. E-mail:
[email protected]. Phone: þ55-21-2562-8301. Fax: þ55-21-2562-8300.
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