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Modelling and simulation of vegetable oil processes
food and bioproducts processing 8 6 ( 2 0 0 8 ) 87–95
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Food and Bioproducts Processing journal homepage: www.elsevier.com/locate/fbp
Modelling and simulation of vegetable oil processes Ana Martinho a,b , Henrique A. Matos a,∗ , Rafiqul Gani b , Bent Sarup c , William Youngreen c a b c
CPQ, Department of Chemical & Biological Engineering, Instituto Superior Tecnico, 1049-001 Lisboa, Portugal CAPEC, Department of Chemical Engineering, Denmark Technical University, DK-2800 Kgs, Lyngby, Denmark Alfa Laval Copenhagen A/S, Vegetable Oil Technology, Process Engineering Department, DK-2860 Søborg, Denmark
a b s t r a c t A solvent-based extraction process for the production of vegetable oil from soybean has been studied with special emphasis on the solvent recovery section of the process. This solvent recovery section includes four parts: an oil recovery, a condensation system, a mineral oil system and a water–solvent separation. The main compounds representing the vegetable oil (soybean oil) usually consist of triglycerides, free fatty acids, tocopherols and sterols. The ICAS-ProPred software, an Integrated Computer Aided System developed by CAPEC, has been used to generate the pure compound data and insert them into the database of a commercial simulator (PRO-II). A process model has been developed and validated by matching steady state simulation results from this model with available industrial data. The validated process model has been used to optimize the efficiency of solvent recovery by adjusting operational variables such as pressure and temperature. The paper highlights the modelling and simulation steps together with a sensitivity analysis for the search for an optimal solution of the process in terms of solvent recovery. © 2008 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. Keywords: Modelling; Simulation; Validation; Sensitivity analysis; Solvent-based extraction; Vegetable oil
1.
Introduction
The global fats and oil consumption is forecasted to reach about 160 million tons/year by 2008 (Oil World Stat., June 2007) and will continue to grow as world population increases. Furthermore, there are changes towards higher quality fats and oil, including more attention to health effects. Also, demand for fats and oils for biofuels production has in recent years become a significant demand factor, with projected production capacity of 33 million tons by the end of 2008 (Oil World, 2007). Clearly, the demand for biofuels is significantly affecting the fats and oil market which was traditionally directed towards production of edible oils. The changes in demand are driving a need, for new technology to adapt to such changes. Soybean oil is one of the world’s leading vegetable oils, in terms of production and consumption, with a global production of about 39 million tons/year. Products derived from soybeans find a wide range of applications like human diet, animal feed, pharmaceutical, cosmetic and
∗
oil-chemical industries, pesticides formulations, and many more. The objective of this work has been to model a solventbased extraction process for the production of soybean oil, to validate the model with available industrial plant data and then to use the validated model to identify the main operational variables (OV) to obtain an optimized process in terms of solvent recovery. The modelling steps included generation of property data for compounds found in the solvent-based extraction process, which are not available in the steady state process simulator used in this work. The needed properties (such as normal boiling point, normal melting point, critical temperature, vapour pressure as a function of temperature, etc.) for the compounds (tri-glycerides, fatty acids, tocopherols, etc.) were generated through the Marrero and Gani (2001) group contribution model available in the ICASProPred software (ICAS, 2001). The simulation and sensitivity analysis was carried out in PRO-II (PRO-II, 2006). The industrial plant data were obtained from Alfa Laval and are proprietary and therefore, not all the information can be disclosed.
Corresponding author. E-mail address: [email protected] (H.A. Matos). Received 12 November 2007; Accepted 14 March 2008 0960-3085/$ – see front matter © 2008 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.fbp.2008.03.009
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Nomenclature AP advantageous procedure ARD average relative deviation CI composition indicator Desolventisation operation to remove solvent (hexane) from meal DTDC desolventiser–toaster–cooler–dryer FA further analysis ICAS Integrated Computer Aided System Meal mixture of oil seeds flakes with vestiges of oil and hexane Miscella mixture of oil and solvent (hexane) MOS solvent air separation system, also known as the mineral oil system (MOS), removes solvent from vent gases NAP not advantageous procedure OV operational variables p reference value of the simulation output CI value influenced by the variation of v p deviation of simulation output CI value between nominal value and the new simulation ProPred pure compound properties prediction software Pro-II steady-state simulator of SIMSCI-ESSCOR RS relative sensitivity ((p/p)/(v/v)) v nominal value of the OV in study v variation inputted on the OV in study exp xi industrial oil mass composition of stream i ximodel calculated oil mass composition value of stream i
2. Description of soybean oil production process The soybean oil is almost exclusively produced by solventbased extraction. This is an efficient process to obtain vegetable oil from seeds, when they contain approximately 20% of oil. The most widely used solvent for extraction of oil from oilseeds is hexane, because it has a high solubility for fats and oils, chemical stability and a suitable vapour pressure allowing separation by flash and stripping operations. However due to its toxicity, hexane must be recovered to the greatest extent
possible, for reasons of operating economics, environmental responsibility and general safety. It can be recovered at low temperature and its low solubility in water, leads to a nearly total recovery. Therefore, the solvent recovery section of the soybean oil production process is a crucial unit operation, which is studied in this work, with industrial data being made available by Alfa Laval. The process of soybean oil production, as shown in Fig. 1, by a solvent-based extraction consists of the following steps: preparation, extraction, desolventisation, oil and solvent recovery, and oil refining. During preparation, the seeds are cracked, cooked and then turned into flakes that are dried and fed to the solvent-based extraction section. The solventbased extraction process consists of “washing” the oil from soybean flakes with a solvent in a counter-current extractor. Once the oil is removed from the flakes, the miscella (mixture of oil and solvent) and the white flakes (extracted flakes wet with solvent) are heated separately to remove the solvent. The flakes go to a desolventiser–toaster–drier–cooler (DTDC) unit and the miscella enters the oil recovery system, where the oil is concentrated. Here the solvent is removed from the oil that is subsequently concentrated nearly to 100% (crude oil). The combination of a drier and a cooler with the desolventiser unit and the toaster produces a meal with the desired characteristics for sale and storage. This oil is then refined through a series of processes, designed to remove the undesirable non-triglyceride compounds that influence flavour, colour and stability of the oil. In the degumming and neutralization steps, gums and free fatty acids are removed and neutral oil is obtained. After bleaching, the oil is then deodorized to remove volatile substances that give the oil undesirable flavour (see Fig. 2). All the recovered solvent vapours mixed with steam, including those coming from the DTDC facility, are retrieved by means of three sub-systems that make part of the solvent recovery system: the condensation, the MOS (mineral oil system), and the water–solvent separation. Therefore, vapours are condensed and the residual vapours are captured in the mineral oil system. The condensates from the condensation system enter a water–solvent separation section, where the solvent is recovered and after mixing with a make-up stream, is redirected to the extractor. The major compounds of soybean oil are: triglycerides, sterols, tocopherols, di- and monoglycerides as well as free fatty acids and phosphatides. These are complex compounds
Fig. 1 – Flow diagram of soybean oil production through a solvent-based extraction process.
food and bioproducts processing 8 6 ( 2 0 0 8 ) 87–95
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Fig. 2 – Sub-sections of the oil refining treatment.
and many of their key properties are currently not available in the open literature or in properties databases for chemicals. Usually, only free fatty acids are available in the databases of commercial process simulation tools. Therefore, one of the main objectives of this work has been to provide a high quality model of a soybean oil process including a comprehensive database of soybean oil compounds. This involved the estimation and validation of proprieties for these compounds and their mixtures representing soybean oil behaviour. Since many of these compounds are also available in other vegetable oils, unless specifically stated, the term “vegetable oils” will be used in this paper to mean soybean oil as well as other vegetable oils (Fig. 3).
3.
Model development
3.1.
Methodology
The development of the model consisted of the following steps:
• the creation of a new database with vegetable oil compounds; • representation of the soybean production process with a network of available process models; • steady state simulations using the new database; • model validation with industrial data; • sensitivity analysis of the main operational variables for the search for an optimal solution in terms of the solvent recovery efficiency, using the validated solvent recovery model.
3.2.
Property models and database
Creation of the new database, containing the needed chemical and physical properties of the vegetable oil compounds has been achieved through the ICAS-ProPred software that allows the prediction of primary properties (such as the normal boiling point, which depend only on the molecular structure of the chemicals) of pure organic chemicals through the Marrero and Gani (2001) group contribution method and the estimation of secondary properties (such as the vapour pressure as
Fig. 3 – Scheme of the methodology adopted.
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a function of temperature, which depend on other properties like the critical pressure, normal boiling point, etc.) through equations of state. Additional data on vapour pressure of sterols and tocopherols not available through ProPred, was found in Erickson et al. (1980). In this work, the group contribution based methods were chosen for two reasons. They are predictive in nature and since hardly any data is available for the vegetable oil chemicals except for the molecular structure, they offer a fast reliable estimation of the necessary data. Another reason is that since the vegetable oil is represented by a model mixture of vegetable oil compounds, the use of other methods such as the boiling point fractions (pseudo-components), extensively used to model petroleum oil mixtures, is not necessary.
3.3.
Process models
Process models from specific sections of the solvent-based extraction process of vegetable oils have been developed. The necessary process models were selected from the model libraries of PRO-II and ICAS, based on the actual available information of the industrial process (such as, detailed process flow diagram, condition of operation, equipment design, etc.). The selected process models were configured to match the vegetable oil process and the simulations were made by taking into account the actual operational scenarios. The simulation results for each operational scenario were then compared with the corresponding available plant data.
3.4.
Software tools
Two software, ICAS and PRO-II, have been used in this work. ICAS is an Integrated Computer Aided System developed at CAPEC (ICAS, 2001). This software combines computer-aided tools for modelling, simulation (including property prediction), synthesis/design, control and analysis into a single integrated system. In order to help solve process engineering problems more efficiently, these tools permit the selection and use of consistent methods and data for process and product development. PRO-II is a software from SIMSCI-ESSCOR, which is used in this work for steady state process simulation. The possibility to integrate PRO-II and ICAS was a key factor in
Table 1 – Average composition for crude and refined soybean oil (Erickson et al., 1980)
Triglycerides (%) Phosphatides (%) Plant sterols (%) Tocopherols (%) Hydrocarbons (%) Free fatty acids (%) Iron (ppm) Copper (ppm)
Crude oil
Refined oil
95–97 1.5–2.5 0.33 0.15–0.21 0.0014 0.3–0.7 1–3 0.03–0.05
99 0.003–0.0045 0.13 0.11–0.18 0.01 <0.05 0.1–0.3 0.02–0.06
the fast and reliable development of the simulation models. Through an established interface between ICAS and PRO-II, the generated data for the vegetable oil chemicals were passed to the PRO-II database for its simulation models, while the process models from ICAS (after testing) were passed into PRO-II for steady state simulation according to the specified operational scenarios.
3.5.
Database creation and validation
Table 1 lists some of the important vegetable oil chemicals found in crude and refined soybean oil. In this work, the phospholipids, also found in vegetable oils, have not been considered. In Erickson et al. (1980), it was possible to find some vapour pressure data for sterol, tocopherols and free fatty acids. Fig. 4 shows the fitting vapour pressures curves obtained for these compounds. It can be noted that the vapour pressures for these vegetable oil chemicals were accurately modelled. In Fig. 5, the predicted and reported data for boiling points of miscella as a function of composition of the solvent (hexane) at 760 mmHg are highlighted. It can be seen that the relative deviation is quite small (less than 6%) when the solvent mass fraction is 0.65 or higher (which is the usual miscella concentrations found in solvent-based extractors). When the mass fraction of the solvent is smaller, the deviation is larger, but this is not affecting the simulation results since the mass fraction of the solvent considered in this work is higher than 0.65.
Fig. 4 – Vapour pressure curves for (a) free fatty acids; (b) tocopherols; (c) sterols (- - -, ICAS prediction; —, Erickson et al., 1980).
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stream, because it corresponds to the solids, and was represented by glutamic acid (one of the major compounds in the meal).
3.6.2.
Extraction
The extractor receives the flakes coming from the preparation block (extract feed) and the recovered hexane (hexane feed) and releases two streams: the miscella (oil–hexane mixture) and the meal (deflected flakes). The meal leaving the extractor, which are often wet flakes saturated with hexane are sent to a desolventiser– toster–dryer–cooler, where this hexane is removed before the solids to be sold as meal or reused.
Fig. 5 – Representation of boiling point as a function of the molar fraction of hexane from literature data and from PRO-II at 760 mmHg.
3.6.
The solvent recovery model
The oil seeds fed to the process are composed of: 19% oil, 11% moisture and 70% hulls. The composition of the soybean oil was assumed (based on available data) to be: triglycerides (95.8%), free fatty acids (1.3%), sterols (1.5%) and tocopherols (1.4%). The operation of the solvent recovery process was modelled through Pro-II. This model includes the essential parts of a vegetable oil extraction plant: preparation, extraction, desolventisation, oil and solvent recovery (Fig. 6). With this model it was possible to analyze the various operational scenarios necessary to validate the developed models and to optimize the solvent recovery process operation.
3.6.1.
Preparation
The preparation block includes the crack, cook, flakes maker and dryer. This block is fed with the stream FEED SEEDS and with MEAL. The feed (FEED SEEDS) contains the oil compounds (one compound representing each group of molecular types) and water. The meal (MEAL) was introduced as a solid
3.6.3.
Desolventisation
The DTDC gases (HEX REC MEAL) are directed to the solvent–water separator which is part of the solvent recovery section, after condensation. To concentrate the oil in the miscella, a system of multiple effect evaporators were used. Two PT-flash operations were used to represent the two evaporators and one distillation column with no reboiler or condenser to represent the stripping column (T3). The vapour streams from the two evaporators (HEX REC1 and HEX REC2) as well as the vapour stream that overflows from the top of the stripping column contains the evaporated hexane are re-directed back to be recovered. The crude oil leaves from the bottom of the column as a stream denominated CRUDE OIL and is further refined in a section not shown in Fig. 6.
3.6.4.
Condensation section
This section consists of a sequence of chilled water condensers, where almost all the hexane vapour mixed with steam is condensed. In this model the rich hexane vapour streams are mixed in a mixer (M1) and then sent to the condensation section. The section that includes two sequential condensers, which are represented by two heat exchangers (PC1 and PC2) followed by two TP-flash separators (COND1 and COND2).
Fig. 6 – Solvent recovery PRO-II model.
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food and bioproducts processing 8 6 ( 2 0 0 8 ) 87–95
The not condensed part of the mixed vapour (REC1C IN) entering in the first condenser (PC1 + COND1) is then sent to the second condenser via the stream REC2C IN. A top stream (REC2C NCOND) containing no condensable compounds leaves from the second condenser (PC2 + COND2) while from the bottom section the residual liquid hexane (REC2C OUT) leaves. Both the liquid streams (REC1C OUT and REC2C OUT) are sent to the water–hexane separator.
3.6.5.
MOS system
In the mineral oil scrubbing system the vapour from the top of the second condenser is treated with mineral oil. Mineral oil is a paraffinic oil (in this model it was represented by a compound named NC15 in PRO-II) which corresponds to an alkaline compound with a long linear chain of 15 carbon atoms. This section comprises an absorption column (T1) followed by a stripping column (T2). Both columns are represented in PRO-II by distillation column block without re-boiler or condenser and between them a heat exchangers network is used to control the temperature of the mineral oil. The absorption column (T1) receives at the bottom the no condensable vapour stream (NCOND) which includes the air leakage from the vacuum system and at the column top the stream of mineral oil at a temperature between 23 and 26 ◦ C (MO PR IN). From the column top a stream is released to the atmosphere with the non-absorbed compounds (AIR OUT) while from the column bottom the rich mineral oil containing hexane (MO RCH OUT) is recovered. This mineral oil is heated in the heat exchanger network and is then fed to the top of the stripper column, which operates from 100 ◦ C. This column is also fed with steam (STEAM CD), which is used to remove hexane from the oil. The lead mineral oil leaving the column from the bottom (MO PR OUT) is cooled in sequence by a rich mineral oil and then by cold water, to reach 23–26 ◦ C required to enter in the absorption column. The mineral oil circulates in a closed loop with no make-up, because it was considered that no significant losses occur. From the top of the stripping column overflows the recovered hexane (HEX REC MO) that will be redirected to the condensation section described above.
Table 2 – Absolute deviation between simulation results and industrial data () for selected measured variables (Quality) of some process outlet streams Stream
Quality
Crude Oil
Hexane composition (ppm) Oil mass composition (%)
3.43 0.20
Air Out
Oil composition (ppm) Hexane composition (ppm)
17.0 0.51
Water Waste
Oil composition (ppm)
2.40
3.6.6.
Water–solvent separation
A decantation is used to achieve the separation of hexane from the aqueous solution due to the two liquid-phases immiscibility and density difference. Therefore the condensate liquid from the condensers (HEX REC DEST and HEX REC MEAL), is sent to a work-tank (DECANT), where the water insoluble hexane is separated from the aqueous phase. This work-tank is represented in PRO-II by a flash block with no added duty (Duty = 0) and from it leaves two streams: the water waste (WASTE WATER) and the recovered hexane (HEX REC TOT). Decanted hexane is subsequently mixed with a make-up stream of about 0.12 tons/h of fresh hexane for re-use in the extractor.
4.
Model validation
The model performance has been compared with data from a full-scale industrial plant under the same operational scenarios (see Fig. 7). Since the properties of the vegetable oil chemicals and their model mixtures have already been verified, in this section, the validation of the steady state simulation results are only discussed. The process simulation validation scheme used is highlighted in Fig. 7.
4.1.
Solvent recovery model validation results
Table 2 lists the differenced between the process simulation results obtained with PRO-II and the industrial data for some of the measured outlet stream variables. The results in Table 2 are given in terms of differences because of rea-
Fig. 7 – Model validation.
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food and bioproducts processing 8 6 ( 2 0 0 8 ) 87–95
sons of confidentiality. The crude oil is composed of hexane and the vegetable oil. The stream that comes out from the MOS section, ‘Air out’, contains mostly air and traces of hexane and oil (<5 ppm), which has been predicted quite accurately. The ‘Water Waste’ is the outlet stream from the decantation facility, where the waste of oil is low <10 ppm, which is also predicted quite accurately. Generally, as can be noted, a good match has been obtained without any adjustment of the model parameters with respect to the process data. Since data related to the operation of the oil recovery section was also available, it was possible to make a detailed analysis of the performance of the process simulation model. The average relative deviation (ARD) between the industrial data and the simulation results for the miscella concentration (leaving both evaporators) and the crude oil (leaving the column T3) was calculated in order to analyse the performance of the hexane–oil equilibrium in this section.
ARD =
exp
|(xi
exp
− ximodel )/xi N
|
× 100
(1)
exp
is the industrial oil mass composition of stream where xi i and ximodel is the calculated oil mass composition value of stream i, for N different experimental runs. The ARD obtained for the first and second evaporators were 3.5% and 5.4%, respectively, and it was 0.3% for the crude oil stream. These results indicate that the composition and proprieties used in the simulation model for the oil corresponds to a similar behaviour as that of the same oil in the full-scale
industrial process. Also, the equilibrium between hexane and oil is predicted with acceptable accuracy.
5.
Process sensitivity analysis
The validated solvent recovery simulation model was used to perform a sensitivity analysis, by adjusting operational variables (OV) and determining their influence on the solvent recovery efficiency. For sensitivity analysis, variations in controlled OV are in the range of 5–30%, with respect to their nominal values, since this is an acceptable range within the usual industrial practice. The chosen OV in this study are three variables: temperature, pressure and number of stages in the column, in different sections of the process. The results of this sensitivity analysis are presented in Table 3. In Table 3, the terminology used for the OV is as follows: the first capital letter indicates the variable being perturbed and the remaining letters indicate the process operation. For example, the first letters indicate temperature (T), pressure (P) and number of column stages (N); while the other letters indicate the condensing zone (COND); the decanter (DECANT); the evaporation (EVAPi) and the crude oil stripping (STP1). The nominal value (v) of the OV and the relative variation (v) of each OV are presented in the second and third columns. Six composition indicators (CIs) divided into two groups have been chosen as parameters to measure the OV variation effect on the final solvent recovery efficiency. The selected CIs are based on the process model flowsheet presented in Fig. 6: Air out(Hex), hexane composition in the MOS’s exhausted air
Table 3 – Effect of operation variables (OV) changes in the compositions indicators (CIs) and relative sensitivity (RS) Operational Variable (OV)
Nominal value (v)
Variation (v/v)
−15% TCOND2
◦
40 C 15% −15%
PCOND
0.6 bar 15%
TDECANT
39.7 ◦ C
PDECANT
0.5 bar
−5% 5% −40% 40% −15%
TEVAP1
◦
69 C 15%
TEVAP2
105 ◦ C
−20%
TSTP1
110 ◦ C
−5%
NSTP1
5
−20% 20% −30% PEVAP
0.6 bar 30%
a
Final effect Composition indicator (CI)
Effect on the CI (p/p)
RS
Air out (Hex) Water Waste (Oil) Air out (Hex) Water Waste (Oil) Air out (Hex) Recycle (Oil) Air out (Hex) Recycle (Oil) Does not converge
Decrease 47% Decrease 30% Increases 245% Increase 164% Decrease 188% Decrease 123% Increase 47% Increase 29%
+3.0 +2.0 +16.3 +10.9 +12.5 +8.2 +3.0 +2.0
AP: advantageous procedure; NAP: not advantageous procedure; FA: further analysis.
AP NAP AP/FA NAP/FA –
Does not converge Crude Oil (Hex) Recycle (Oil) Crude Oil (Hex) Recycle (Oil) Crude Oil (Hex) Total Lost (Hex) Recover (Hex) Crude Oil (Hex) Crude Oil (Hex) Total Lost (Hex) Crude Oil (Hex) Total Lost (Hex) Recycle (Hex) Crude Oil (Hex) Recycle (Hex) Crude Oil (Hex)
Statusa
– Increase 7% Increase 16% Decrease 7% Decrease 5% Increase 321% Increase 64% Decrease 6% Increase 115% Increase 578% Increase 5% Decrease 86% Decrease 0.9% Increase 4% Increase 55% Decrease 0.5% Decrease 43%
−0.5 −1.0 −0.5 −0.3 −16.0 −3.2 +1.2 −23.0 −28.9 −0.3 −4.3 −0.1 −0.2 −1.8 −0.0 −1.4
NAP/AP FA/NAP NAP NAP NAP FA AP/NAP NAP/FA
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outlet stream; Water Waste (Oil), oil composition in the outlet stream from de decantation operation; Crude Oil (Hex), hexane composition in the crude oil outlet stream from the evaporation; Total lost (Hex), hexane amount lost in the four outlet streams of the system; Recycle (Oil), oil composition in the stream that returns to preparation zone, containing mainly hexane and only traces of oil and water; Recover (Hex), hexane composition in the vapour stream obtained after the evaporation, which contains vapour of hexane (major compound), water and oil (traces). The Group I involves the first four CIs (Air out(Hex), Water Waste (Oil), Crude Oil (Hex) and Total lost (Hex)) a decrease in their value is a desirable operational condition, since these indicators are located at outlet streams where a oil or hexane emissions should be avoided. However, for the last two CIs (Group II) an increase in their value means a enhancement of the process performance, since this are related with internal streams and thus more oil or hexane are recovered. Based on these indicators a measure of the influence of the variations to the OV in solvent recovery system efficiency is obtained, and defined as a generic relative sensitivity (RS), given by RS =
p/p v/v
(2)
where p is the reference value of the simulation output for each CI at the nominal OV value and p is the deviation of simulation output between reference value and the new simulation. A negative value for v means in general a reduction in energy consumption (lower temperature or pressure) or capital costs (reduction on the number of striping stages). Therefore, in this situation for the CIs of Group I, a positive value of RS means an advantageous procedure (AP). For the CIs of Group II the negative RS value is required for it to be classified as AP, since a positive p is expected. For scenarios with a positive value of v, the negative RS values should be further analysed (FA) since it is obvious that there is a trade off between a increase of energy consumption (higher temperature or pressure) or capital costs and the benefits obtained by the improvement of the CI values. The status designated as “not advantageous procedure” (NAP) occurred when the variation in OV is not economically acceptable and/or the effect in the CI is against to the expected increase on solvent recovery. In Table 3 the main variables are listed together with their variations. The effect in the specific composition indicators are also shown, as well the RS values. In the last column (Status) the classification of the RS is highlighted in terms of AP, NAP and FA. From Table 3 it is possible to note that changes in the chosen OV, involving mainly the solvent recovery section (evaporation, condensation and mineral oil system), have a strong effect on the solvent recovery efficiency of the system. In the Condensation zone the RS values are positive, meaning that a direct relation between the variation of the OV and the CI output values, and four procedures are adequate to improve the solvent recovery system efficiency (entries with AP label). In the Evaporation section it is possible to obtain only negative values of RS in the range of −28.9 to −0.05. This means that an inverse behaviour was obtained between the variation OV and the CI observable parameters. Also, only two scenarios could be labelled with the AP status. Finally, the changes in decantation operation conditions are not reported, since the
model is not able to converge for the proposed OV changes, as these changes lead to an infeasible operation. The highest negative RS value was obtained by decreasing one stage of stripping column (NSTP1), where crude oil is obtained. This means an increase in 578% in the loss of hexane with the crude oil. Thus, the global efficiency of the solvent recovery, as well as oil recovery, is strongly affected by decreasing the NSTP. In an opposite way, by increasing a stage, a decrease in the loss of hexane with crude oil in 86%, which represents a gain of 0.9% over total hexane lost, is observed. The second highest negative RS value was obtained by the decrease of 5% of the stripping column temperature. This has also a strong effect on the loss of hexane in the crude oil, indicating a lower recovery in the condensation unit. By decreasing the temperature in the first evaporator by 15%, the recycled hexane increases by 16%, but the quantity that comes out with crude oil becomes 7% higher. This means that the increase of solvent efficiency is achieved by a decrease of crude oil purity. A similar effect can be observed when the pressure of this zone is changed. The allowable change in the temperature of the second evaporator is limited because of the stripping column temperature. But a decrease of this temperature (20%) has an undesirable effect in the solvent recovery: the quantity of hexane lost with the crude oil increases by 321% and also the hexane total lost increases 64%. The condensation temperature or the pressure in the 2nd condenser causes a direct variation in air outlet stream and in oil quantity of the decantation wastewater. The influence of these OV are the most important to achieve a better performance of the whole solvent recovery system. This sensitivity analysis shows that some operational variables, namely in the condensation and evaporation sections, are good candidates to be included in a optimization model for the searching of a configuration with a optimal solution in terms of solvent recovery efficiency.
6.
Conclusions
A model for soybean oil extraction process has been developed for process simulation and sensitivity analysis. The process simulation models use the property databases, which was necessary since very few compounds could be found in the process simulation databases. The good comparison between the data from a full-scale industrial plant and the results obtained from simulations demonstrate the applicability of the developed models. The results also show that for soybean oil extraction, it has been possible to match the industrial data because of a good fit of the pure component and mixture properties. Therefore, the process simulation models provide a good basis for analysis of the future process-product alternatives involving economic improvements and/or structural changes in the process. In this work the objective was to study the effect of operating conditions such as temperatures, pressures as well as equipment design features such as the number of stages of the solvent recovery section. Using sensitivity analysis for these variables on the solvent recovery system efficiency, it was possible to determine that the condensing and the evaporation zones are the ones that have the great influence over the oil recovery and hexane lost of the process. In particular, the decrease of the second condensation temperature is well suited to improvements in the solvent
food and bioproducts processing 8 6 ( 2 0 0 8 ) 87–95
recovery operations. Other scenarios of operational variables changes should be further analysed using economical data, since a trade-off is generated by benefits of a greater solvent recovery but requiring an eventual increase in operational or capital costs. The paper has highlighted the need and use for different methods and tools needed to solve typical process design and engineering problems. The available methods and tools help to generate the necessary property models and their use in process models. It also shows that models from one sector of application can easily be adapted to another area of application. Other industrial food processes could also be modelled using the same methodology, the methods and tools (software)—the key step is to get the properties of the vegetable oil right. Since many of the chemicals can also be found in other oils and oil-based products, a direct extension of this work to simulation-based studies of processes for the produc-
95
tion of sunflower oil, palm kernel oil or olive oil should also be possible.
References Erickson, D.R., Pryde, E.H., Brekke, O.L., et al., 1980, Handbook of Soy Oil Processing and Utilization (AOCS Monograph 8, AOCS, Champaign, IL), pp. 14–17. ICAS Documentation, PEC01-XX, CAPEC report, Technical University of Denmark, Department of Chemical Engineering, CAPEC, 2001. Marrero, J. and Gani, R., 2001, Group contribution based estimation of pure component properties. Fluid Phase Equilibr., 183/184: 183–208. Oil World, No. 35, vol. 50, p. 449, August 31, 2007, http://www.oilworld.biz/. Oil World Statistics Update, June 8, 2007, http://www.oilworld.biz/. PRO-II User’s Guide, 2006, Simulation Sciences Inc., Brea, USA.
Contents lists available at ScienceDirect
Food and Bioproducts Processing journal homepage: www.elsevier.com/locate/fbp
Modelling and simulation of vegetable oil processes Ana Martinho a,b , Henrique A. Matos a,∗ , Rafiqul Gani b , Bent Sarup c , William Youngreen c a b c
CPQ, Department of Chemical & Biological Engineering, Instituto Superior Tecnico, 1049-001 Lisboa, Portugal CAPEC, Department of Chemical Engineering, Denmark Technical University, DK-2800 Kgs, Lyngby, Denmark Alfa Laval Copenhagen A/S, Vegetable Oil Technology, Process Engineering Department, DK-2860 Søborg, Denmark
a b s t r a c t A solvent-based extraction process for the production of vegetable oil from soybean has been studied with special emphasis on the solvent recovery section of the process. This solvent recovery section includes four parts: an oil recovery, a condensation system, a mineral oil system and a water–solvent separation. The main compounds representing the vegetable oil (soybean oil) usually consist of triglycerides, free fatty acids, tocopherols and sterols. The ICAS-ProPred software, an Integrated Computer Aided System developed by CAPEC, has been used to generate the pure compound data and insert them into the database of a commercial simulator (PRO-II). A process model has been developed and validated by matching steady state simulation results from this model with available industrial data. The validated process model has been used to optimize the efficiency of solvent recovery by adjusting operational variables such as pressure and temperature. The paper highlights the modelling and simulation steps together with a sensitivity analysis for the search for an optimal solution of the process in terms of solvent recovery. © 2008 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. Keywords: Modelling; Simulation; Validation; Sensitivity analysis; Solvent-based extraction; Vegetable oil
1.
Introduction
The global fats and oil consumption is forecasted to reach about 160 million tons/year by 2008 (Oil World Stat., June 2007) and will continue to grow as world population increases. Furthermore, there are changes towards higher quality fats and oil, including more attention to health effects. Also, demand for fats and oils for biofuels production has in recent years become a significant demand factor, with projected production capacity of 33 million tons by the end of 2008 (Oil World, 2007). Clearly, the demand for biofuels is significantly affecting the fats and oil market which was traditionally directed towards production of edible oils. The changes in demand are driving a need, for new technology to adapt to such changes. Soybean oil is one of the world’s leading vegetable oils, in terms of production and consumption, with a global production of about 39 million tons/year. Products derived from soybeans find a wide range of applications like human diet, animal feed, pharmaceutical, cosmetic and
∗
oil-chemical industries, pesticides formulations, and many more. The objective of this work has been to model a solventbased extraction process for the production of soybean oil, to validate the model with available industrial plant data and then to use the validated model to identify the main operational variables (OV) to obtain an optimized process in terms of solvent recovery. The modelling steps included generation of property data for compounds found in the solvent-based extraction process, which are not available in the steady state process simulator used in this work. The needed properties (such as normal boiling point, normal melting point, critical temperature, vapour pressure as a function of temperature, etc.) for the compounds (tri-glycerides, fatty acids, tocopherols, etc.) were generated through the Marrero and Gani (2001) group contribution model available in the ICASProPred software (ICAS, 2001). The simulation and sensitivity analysis was carried out in PRO-II (PRO-II, 2006). The industrial plant data were obtained from Alfa Laval and are proprietary and therefore, not all the information can be disclosed.
Corresponding author. E-mail address: [email protected] (H.A. Matos). Received 12 November 2007; Accepted 14 March 2008 0960-3085/$ – see front matter © 2008 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.fbp.2008.03.009
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Nomenclature AP advantageous procedure ARD average relative deviation CI composition indicator Desolventisation operation to remove solvent (hexane) from meal DTDC desolventiser–toaster–cooler–dryer FA further analysis ICAS Integrated Computer Aided System Meal mixture of oil seeds flakes with vestiges of oil and hexane Miscella mixture of oil and solvent (hexane) MOS solvent air separation system, also known as the mineral oil system (MOS), removes solvent from vent gases NAP not advantageous procedure OV operational variables p reference value of the simulation output CI value influenced by the variation of v p deviation of simulation output CI value between nominal value and the new simulation ProPred pure compound properties prediction software Pro-II steady-state simulator of SIMSCI-ESSCOR RS relative sensitivity ((p/p)/(v/v)) v nominal value of the OV in study v variation inputted on the OV in study exp xi industrial oil mass composition of stream i ximodel calculated oil mass composition value of stream i
2. Description of soybean oil production process The soybean oil is almost exclusively produced by solventbased extraction. This is an efficient process to obtain vegetable oil from seeds, when they contain approximately 20% of oil. The most widely used solvent for extraction of oil from oilseeds is hexane, because it has a high solubility for fats and oils, chemical stability and a suitable vapour pressure allowing separation by flash and stripping operations. However due to its toxicity, hexane must be recovered to the greatest extent
possible, for reasons of operating economics, environmental responsibility and general safety. It can be recovered at low temperature and its low solubility in water, leads to a nearly total recovery. Therefore, the solvent recovery section of the soybean oil production process is a crucial unit operation, which is studied in this work, with industrial data being made available by Alfa Laval. The process of soybean oil production, as shown in Fig. 1, by a solvent-based extraction consists of the following steps: preparation, extraction, desolventisation, oil and solvent recovery, and oil refining. During preparation, the seeds are cracked, cooked and then turned into flakes that are dried and fed to the solvent-based extraction section. The solventbased extraction process consists of “washing” the oil from soybean flakes with a solvent in a counter-current extractor. Once the oil is removed from the flakes, the miscella (mixture of oil and solvent) and the white flakes (extracted flakes wet with solvent) are heated separately to remove the solvent. The flakes go to a desolventiser–toaster–drier–cooler (DTDC) unit and the miscella enters the oil recovery system, where the oil is concentrated. Here the solvent is removed from the oil that is subsequently concentrated nearly to 100% (crude oil). The combination of a drier and a cooler with the desolventiser unit and the toaster produces a meal with the desired characteristics for sale and storage. This oil is then refined through a series of processes, designed to remove the undesirable non-triglyceride compounds that influence flavour, colour and stability of the oil. In the degumming and neutralization steps, gums and free fatty acids are removed and neutral oil is obtained. After bleaching, the oil is then deodorized to remove volatile substances that give the oil undesirable flavour (see Fig. 2). All the recovered solvent vapours mixed with steam, including those coming from the DTDC facility, are retrieved by means of three sub-systems that make part of the solvent recovery system: the condensation, the MOS (mineral oil system), and the water–solvent separation. Therefore, vapours are condensed and the residual vapours are captured in the mineral oil system. The condensates from the condensation system enter a water–solvent separation section, where the solvent is recovered and after mixing with a make-up stream, is redirected to the extractor. The major compounds of soybean oil are: triglycerides, sterols, tocopherols, di- and monoglycerides as well as free fatty acids and phosphatides. These are complex compounds
Fig. 1 – Flow diagram of soybean oil production through a solvent-based extraction process.
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Fig. 2 – Sub-sections of the oil refining treatment.
and many of their key properties are currently not available in the open literature or in properties databases for chemicals. Usually, only free fatty acids are available in the databases of commercial process simulation tools. Therefore, one of the main objectives of this work has been to provide a high quality model of a soybean oil process including a comprehensive database of soybean oil compounds. This involved the estimation and validation of proprieties for these compounds and their mixtures representing soybean oil behaviour. Since many of these compounds are also available in other vegetable oils, unless specifically stated, the term “vegetable oils” will be used in this paper to mean soybean oil as well as other vegetable oils (Fig. 3).
3.
Model development
3.1.
Methodology
The development of the model consisted of the following steps:
• the creation of a new database with vegetable oil compounds; • representation of the soybean production process with a network of available process models; • steady state simulations using the new database; • model validation with industrial data; • sensitivity analysis of the main operational variables for the search for an optimal solution in terms of the solvent recovery efficiency, using the validated solvent recovery model.
3.2.
Property models and database
Creation of the new database, containing the needed chemical and physical properties of the vegetable oil compounds has been achieved through the ICAS-ProPred software that allows the prediction of primary properties (such as the normal boiling point, which depend only on the molecular structure of the chemicals) of pure organic chemicals through the Marrero and Gani (2001) group contribution method and the estimation of secondary properties (such as the vapour pressure as
Fig. 3 – Scheme of the methodology adopted.
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a function of temperature, which depend on other properties like the critical pressure, normal boiling point, etc.) through equations of state. Additional data on vapour pressure of sterols and tocopherols not available through ProPred, was found in Erickson et al. (1980). In this work, the group contribution based methods were chosen for two reasons. They are predictive in nature and since hardly any data is available for the vegetable oil chemicals except for the molecular structure, they offer a fast reliable estimation of the necessary data. Another reason is that since the vegetable oil is represented by a model mixture of vegetable oil compounds, the use of other methods such as the boiling point fractions (pseudo-components), extensively used to model petroleum oil mixtures, is not necessary.
3.3.
Process models
Process models from specific sections of the solvent-based extraction process of vegetable oils have been developed. The necessary process models were selected from the model libraries of PRO-II and ICAS, based on the actual available information of the industrial process (such as, detailed process flow diagram, condition of operation, equipment design, etc.). The selected process models were configured to match the vegetable oil process and the simulations were made by taking into account the actual operational scenarios. The simulation results for each operational scenario were then compared with the corresponding available plant data.
3.4.
Software tools
Two software, ICAS and PRO-II, have been used in this work. ICAS is an Integrated Computer Aided System developed at CAPEC (ICAS, 2001). This software combines computer-aided tools for modelling, simulation (including property prediction), synthesis/design, control and analysis into a single integrated system. In order to help solve process engineering problems more efficiently, these tools permit the selection and use of consistent methods and data for process and product development. PRO-II is a software from SIMSCI-ESSCOR, which is used in this work for steady state process simulation. The possibility to integrate PRO-II and ICAS was a key factor in
Table 1 – Average composition for crude and refined soybean oil (Erickson et al., 1980)
Triglycerides (%) Phosphatides (%) Plant sterols (%) Tocopherols (%) Hydrocarbons (%) Free fatty acids (%) Iron (ppm) Copper (ppm)
Crude oil
Refined oil
95–97 1.5–2.5 0.33 0.15–0.21 0.0014 0.3–0.7 1–3 0.03–0.05
99 0.003–0.0045 0.13 0.11–0.18 0.01 <0.05 0.1–0.3 0.02–0.06
the fast and reliable development of the simulation models. Through an established interface between ICAS and PRO-II, the generated data for the vegetable oil chemicals were passed to the PRO-II database for its simulation models, while the process models from ICAS (after testing) were passed into PRO-II for steady state simulation according to the specified operational scenarios.
3.5.
Database creation and validation
Table 1 lists some of the important vegetable oil chemicals found in crude and refined soybean oil. In this work, the phospholipids, also found in vegetable oils, have not been considered. In Erickson et al. (1980), it was possible to find some vapour pressure data for sterol, tocopherols and free fatty acids. Fig. 4 shows the fitting vapour pressures curves obtained for these compounds. It can be noted that the vapour pressures for these vegetable oil chemicals were accurately modelled. In Fig. 5, the predicted and reported data for boiling points of miscella as a function of composition of the solvent (hexane) at 760 mmHg are highlighted. It can be seen that the relative deviation is quite small (less than 6%) when the solvent mass fraction is 0.65 or higher (which is the usual miscella concentrations found in solvent-based extractors). When the mass fraction of the solvent is smaller, the deviation is larger, but this is not affecting the simulation results since the mass fraction of the solvent considered in this work is higher than 0.65.
Fig. 4 – Vapour pressure curves for (a) free fatty acids; (b) tocopherols; (c) sterols (- - -, ICAS prediction; —, Erickson et al., 1980).
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stream, because it corresponds to the solids, and was represented by glutamic acid (one of the major compounds in the meal).
3.6.2.
Extraction
The extractor receives the flakes coming from the preparation block (extract feed) and the recovered hexane (hexane feed) and releases two streams: the miscella (oil–hexane mixture) and the meal (deflected flakes). The meal leaving the extractor, which are often wet flakes saturated with hexane are sent to a desolventiser– toster–dryer–cooler, where this hexane is removed before the solids to be sold as meal or reused.
Fig. 5 – Representation of boiling point as a function of the molar fraction of hexane from literature data and from PRO-II at 760 mmHg.
3.6.
The solvent recovery model
The oil seeds fed to the process are composed of: 19% oil, 11% moisture and 70% hulls. The composition of the soybean oil was assumed (based on available data) to be: triglycerides (95.8%), free fatty acids (1.3%), sterols (1.5%) and tocopherols (1.4%). The operation of the solvent recovery process was modelled through Pro-II. This model includes the essential parts of a vegetable oil extraction plant: preparation, extraction, desolventisation, oil and solvent recovery (Fig. 6). With this model it was possible to analyze the various operational scenarios necessary to validate the developed models and to optimize the solvent recovery process operation.
3.6.1.
Preparation
The preparation block includes the crack, cook, flakes maker and dryer. This block is fed with the stream FEED SEEDS and with MEAL. The feed (FEED SEEDS) contains the oil compounds (one compound representing each group of molecular types) and water. The meal (MEAL) was introduced as a solid
3.6.3.
Desolventisation
The DTDC gases (HEX REC MEAL) are directed to the solvent–water separator which is part of the solvent recovery section, after condensation. To concentrate the oil in the miscella, a system of multiple effect evaporators were used. Two PT-flash operations were used to represent the two evaporators and one distillation column with no reboiler or condenser to represent the stripping column (T3). The vapour streams from the two evaporators (HEX REC1 and HEX REC2) as well as the vapour stream that overflows from the top of the stripping column contains the evaporated hexane are re-directed back to be recovered. The crude oil leaves from the bottom of the column as a stream denominated CRUDE OIL and is further refined in a section not shown in Fig. 6.
3.6.4.
Condensation section
This section consists of a sequence of chilled water condensers, where almost all the hexane vapour mixed with steam is condensed. In this model the rich hexane vapour streams are mixed in a mixer (M1) and then sent to the condensation section. The section that includes two sequential condensers, which are represented by two heat exchangers (PC1 and PC2) followed by two TP-flash separators (COND1 and COND2).
Fig. 6 – Solvent recovery PRO-II model.
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The not condensed part of the mixed vapour (REC1C IN) entering in the first condenser (PC1 + COND1) is then sent to the second condenser via the stream REC2C IN. A top stream (REC2C NCOND) containing no condensable compounds leaves from the second condenser (PC2 + COND2) while from the bottom section the residual liquid hexane (REC2C OUT) leaves. Both the liquid streams (REC1C OUT and REC2C OUT) are sent to the water–hexane separator.
3.6.5.
MOS system
In the mineral oil scrubbing system the vapour from the top of the second condenser is treated with mineral oil. Mineral oil is a paraffinic oil (in this model it was represented by a compound named NC15 in PRO-II) which corresponds to an alkaline compound with a long linear chain of 15 carbon atoms. This section comprises an absorption column (T1) followed by a stripping column (T2). Both columns are represented in PRO-II by distillation column block without re-boiler or condenser and between them a heat exchangers network is used to control the temperature of the mineral oil. The absorption column (T1) receives at the bottom the no condensable vapour stream (NCOND) which includes the air leakage from the vacuum system and at the column top the stream of mineral oil at a temperature between 23 and 26 ◦ C (MO PR IN). From the column top a stream is released to the atmosphere with the non-absorbed compounds (AIR OUT) while from the column bottom the rich mineral oil containing hexane (MO RCH OUT) is recovered. This mineral oil is heated in the heat exchanger network and is then fed to the top of the stripper column, which operates from 100 ◦ C. This column is also fed with steam (STEAM CD), which is used to remove hexane from the oil. The lead mineral oil leaving the column from the bottom (MO PR OUT) is cooled in sequence by a rich mineral oil and then by cold water, to reach 23–26 ◦ C required to enter in the absorption column. The mineral oil circulates in a closed loop with no make-up, because it was considered that no significant losses occur. From the top of the stripping column overflows the recovered hexane (HEX REC MO) that will be redirected to the condensation section described above.
Table 2 – Absolute deviation between simulation results and industrial data () for selected measured variables (Quality) of some process outlet streams Stream
Quality
Crude Oil
Hexane composition (ppm) Oil mass composition (%)
3.43 0.20
Air Out
Oil composition (ppm) Hexane composition (ppm)
17.0 0.51
Water Waste
Oil composition (ppm)
2.40
3.6.6.
Water–solvent separation
A decantation is used to achieve the separation of hexane from the aqueous solution due to the two liquid-phases immiscibility and density difference. Therefore the condensate liquid from the condensers (HEX REC DEST and HEX REC MEAL), is sent to a work-tank (DECANT), where the water insoluble hexane is separated from the aqueous phase. This work-tank is represented in PRO-II by a flash block with no added duty (Duty = 0) and from it leaves two streams: the water waste (WASTE WATER) and the recovered hexane (HEX REC TOT). Decanted hexane is subsequently mixed with a make-up stream of about 0.12 tons/h of fresh hexane for re-use in the extractor.
4.
Model validation
The model performance has been compared with data from a full-scale industrial plant under the same operational scenarios (see Fig. 7). Since the properties of the vegetable oil chemicals and their model mixtures have already been verified, in this section, the validation of the steady state simulation results are only discussed. The process simulation validation scheme used is highlighted in Fig. 7.
4.1.
Solvent recovery model validation results
Table 2 lists the differenced between the process simulation results obtained with PRO-II and the industrial data for some of the measured outlet stream variables. The results in Table 2 are given in terms of differences because of rea-
Fig. 7 – Model validation.
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sons of confidentiality. The crude oil is composed of hexane and the vegetable oil. The stream that comes out from the MOS section, ‘Air out’, contains mostly air and traces of hexane and oil (<5 ppm), which has been predicted quite accurately. The ‘Water Waste’ is the outlet stream from the decantation facility, where the waste of oil is low <10 ppm, which is also predicted quite accurately. Generally, as can be noted, a good match has been obtained without any adjustment of the model parameters with respect to the process data. Since data related to the operation of the oil recovery section was also available, it was possible to make a detailed analysis of the performance of the process simulation model. The average relative deviation (ARD) between the industrial data and the simulation results for the miscella concentration (leaving both evaporators) and the crude oil (leaving the column T3) was calculated in order to analyse the performance of the hexane–oil equilibrium in this section.
ARD =
exp
|(xi
exp
− ximodel )/xi N
|
× 100
(1)
exp
is the industrial oil mass composition of stream where xi i and ximodel is the calculated oil mass composition value of stream i, for N different experimental runs. The ARD obtained for the first and second evaporators were 3.5% and 5.4%, respectively, and it was 0.3% for the crude oil stream. These results indicate that the composition and proprieties used in the simulation model for the oil corresponds to a similar behaviour as that of the same oil in the full-scale
industrial process. Also, the equilibrium between hexane and oil is predicted with acceptable accuracy.
5.
Process sensitivity analysis
The validated solvent recovery simulation model was used to perform a sensitivity analysis, by adjusting operational variables (OV) and determining their influence on the solvent recovery efficiency. For sensitivity analysis, variations in controlled OV are in the range of 5–30%, with respect to their nominal values, since this is an acceptable range within the usual industrial practice. The chosen OV in this study are three variables: temperature, pressure and number of stages in the column, in different sections of the process. The results of this sensitivity analysis are presented in Table 3. In Table 3, the terminology used for the OV is as follows: the first capital letter indicates the variable being perturbed and the remaining letters indicate the process operation. For example, the first letters indicate temperature (T), pressure (P) and number of column stages (N); while the other letters indicate the condensing zone (COND); the decanter (DECANT); the evaporation (EVAPi) and the crude oil stripping (STP1). The nominal value (v) of the OV and the relative variation (v) of each OV are presented in the second and third columns. Six composition indicators (CIs) divided into two groups have been chosen as parameters to measure the OV variation effect on the final solvent recovery efficiency. The selected CIs are based on the process model flowsheet presented in Fig. 6: Air out(Hex), hexane composition in the MOS’s exhausted air
Table 3 – Effect of operation variables (OV) changes in the compositions indicators (CIs) and relative sensitivity (RS) Operational Variable (OV)
Nominal value (v)
Variation (v/v)
−15% TCOND2
◦
40 C 15% −15%
PCOND
0.6 bar 15%
TDECANT
39.7 ◦ C
PDECANT
0.5 bar
−5% 5% −40% 40% −15%
TEVAP1
◦
69 C 15%
TEVAP2
105 ◦ C
−20%
TSTP1
110 ◦ C
−5%
NSTP1
5
−20% 20% −30% PEVAP
0.6 bar 30%
a
Final effect Composition indicator (CI)
Effect on the CI (p/p)
RS
Air out (Hex) Water Waste (Oil) Air out (Hex) Water Waste (Oil) Air out (Hex) Recycle (Oil) Air out (Hex) Recycle (Oil) Does not converge
Decrease 47% Decrease 30% Increases 245% Increase 164% Decrease 188% Decrease 123% Increase 47% Increase 29%
+3.0 +2.0 +16.3 +10.9 +12.5 +8.2 +3.0 +2.0
AP: advantageous procedure; NAP: not advantageous procedure; FA: further analysis.
AP NAP AP/FA NAP/FA –
Does not converge Crude Oil (Hex) Recycle (Oil) Crude Oil (Hex) Recycle (Oil) Crude Oil (Hex) Total Lost (Hex) Recover (Hex) Crude Oil (Hex) Crude Oil (Hex) Total Lost (Hex) Crude Oil (Hex) Total Lost (Hex) Recycle (Hex) Crude Oil (Hex) Recycle (Hex) Crude Oil (Hex)
Statusa
– Increase 7% Increase 16% Decrease 7% Decrease 5% Increase 321% Increase 64% Decrease 6% Increase 115% Increase 578% Increase 5% Decrease 86% Decrease 0.9% Increase 4% Increase 55% Decrease 0.5% Decrease 43%
−0.5 −1.0 −0.5 −0.3 −16.0 −3.2 +1.2 −23.0 −28.9 −0.3 −4.3 −0.1 −0.2 −1.8 −0.0 −1.4
NAP/AP FA/NAP NAP NAP NAP FA AP/NAP NAP/FA
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outlet stream; Water Waste (Oil), oil composition in the outlet stream from de decantation operation; Crude Oil (Hex), hexane composition in the crude oil outlet stream from the evaporation; Total lost (Hex), hexane amount lost in the four outlet streams of the system; Recycle (Oil), oil composition in the stream that returns to preparation zone, containing mainly hexane and only traces of oil and water; Recover (Hex), hexane composition in the vapour stream obtained after the evaporation, which contains vapour of hexane (major compound), water and oil (traces). The Group I involves the first four CIs (Air out(Hex), Water Waste (Oil), Crude Oil (Hex) and Total lost (Hex)) a decrease in their value is a desirable operational condition, since these indicators are located at outlet streams where a oil or hexane emissions should be avoided. However, for the last two CIs (Group II) an increase in their value means a enhancement of the process performance, since this are related with internal streams and thus more oil or hexane are recovered. Based on these indicators a measure of the influence of the variations to the OV in solvent recovery system efficiency is obtained, and defined as a generic relative sensitivity (RS), given by RS =
p/p v/v
(2)
where p is the reference value of the simulation output for each CI at the nominal OV value and p is the deviation of simulation output between reference value and the new simulation. A negative value for v means in general a reduction in energy consumption (lower temperature or pressure) or capital costs (reduction on the number of striping stages). Therefore, in this situation for the CIs of Group I, a positive value of RS means an advantageous procedure (AP). For the CIs of Group II the negative RS value is required for it to be classified as AP, since a positive p is expected. For scenarios with a positive value of v, the negative RS values should be further analysed (FA) since it is obvious that there is a trade off between a increase of energy consumption (higher temperature or pressure) or capital costs and the benefits obtained by the improvement of the CI values. The status designated as “not advantageous procedure” (NAP) occurred when the variation in OV is not economically acceptable and/or the effect in the CI is against to the expected increase on solvent recovery. In Table 3 the main variables are listed together with their variations. The effect in the specific composition indicators are also shown, as well the RS values. In the last column (Status) the classification of the RS is highlighted in terms of AP, NAP and FA. From Table 3 it is possible to note that changes in the chosen OV, involving mainly the solvent recovery section (evaporation, condensation and mineral oil system), have a strong effect on the solvent recovery efficiency of the system. In the Condensation zone the RS values are positive, meaning that a direct relation between the variation of the OV and the CI output values, and four procedures are adequate to improve the solvent recovery system efficiency (entries with AP label). In the Evaporation section it is possible to obtain only negative values of RS in the range of −28.9 to −0.05. This means that an inverse behaviour was obtained between the variation OV and the CI observable parameters. Also, only two scenarios could be labelled with the AP status. Finally, the changes in decantation operation conditions are not reported, since the
model is not able to converge for the proposed OV changes, as these changes lead to an infeasible operation. The highest negative RS value was obtained by decreasing one stage of stripping column (NSTP1), where crude oil is obtained. This means an increase in 578% in the loss of hexane with the crude oil. Thus, the global efficiency of the solvent recovery, as well as oil recovery, is strongly affected by decreasing the NSTP. In an opposite way, by increasing a stage, a decrease in the loss of hexane with crude oil in 86%, which represents a gain of 0.9% over total hexane lost, is observed. The second highest negative RS value was obtained by the decrease of 5% of the stripping column temperature. This has also a strong effect on the loss of hexane in the crude oil, indicating a lower recovery in the condensation unit. By decreasing the temperature in the first evaporator by 15%, the recycled hexane increases by 16%, but the quantity that comes out with crude oil becomes 7% higher. This means that the increase of solvent efficiency is achieved by a decrease of crude oil purity. A similar effect can be observed when the pressure of this zone is changed. The allowable change in the temperature of the second evaporator is limited because of the stripping column temperature. But a decrease of this temperature (20%) has an undesirable effect in the solvent recovery: the quantity of hexane lost with the crude oil increases by 321% and also the hexane total lost increases 64%. The condensation temperature or the pressure in the 2nd condenser causes a direct variation in air outlet stream and in oil quantity of the decantation wastewater. The influence of these OV are the most important to achieve a better performance of the whole solvent recovery system. This sensitivity analysis shows that some operational variables, namely in the condensation and evaporation sections, are good candidates to be included in a optimization model for the searching of a configuration with a optimal solution in terms of solvent recovery efficiency.
6.
Conclusions
A model for soybean oil extraction process has been developed for process simulation and sensitivity analysis. The process simulation models use the property databases, which was necessary since very few compounds could be found in the process simulation databases. The good comparison between the data from a full-scale industrial plant and the results obtained from simulations demonstrate the applicability of the developed models. The results also show that for soybean oil extraction, it has been possible to match the industrial data because of a good fit of the pure component and mixture properties. Therefore, the process simulation models provide a good basis for analysis of the future process-product alternatives involving economic improvements and/or structural changes in the process. In this work the objective was to study the effect of operating conditions such as temperatures, pressures as well as equipment design features such as the number of stages of the solvent recovery section. Using sensitivity analysis for these variables on the solvent recovery system efficiency, it was possible to determine that the condensing and the evaporation zones are the ones that have the great influence over the oil recovery and hexane lost of the process. In particular, the decrease of the second condensation temperature is well suited to improvements in the solvent
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recovery operations. Other scenarios of operational variables changes should be further analysed using economical data, since a trade-off is generated by benefits of a greater solvent recovery but requiring an eventual increase in operational or capital costs. The paper has highlighted the need and use for different methods and tools needed to solve typical process design and engineering problems. The available methods and tools help to generate the necessary property models and their use in process models. It also shows that models from one sector of application can easily be adapted to another area of application. Other industrial food processes could also be modelled using the same methodology, the methods and tools (software)—the key step is to get the properties of the vegetable oil right. Since many of the chemicals can also be found in other oils and oil-based products, a direct extension of this work to simulation-based studies of processes for the produc-
95
tion of sunflower oil, palm kernel oil or olive oil should also be possible.
References Erickson, D.R., Pryde, E.H., Brekke, O.L., et al., 1980, Handbook of Soy Oil Processing and Utilization (AOCS Monograph 8, AOCS, Champaign, IL), pp. 14–17. ICAS Documentation, PEC01-XX, CAPEC report, Technical University of Denmark, Department of Chemical Engineering, CAPEC, 2001. Marrero, J. and Gani, R., 2001, Group contribution based estimation of pure component properties. Fluid Phase Equilibr., 183/184: 183–208. Oil World, No. 35, vol. 50, p. 449, August 31, 2007, http://www.oilworld.biz/. Oil World Statistics Update, June 8, 2007, http://www.oilworld.biz/. PRO-II User’s Guide, 2006, Simulation Sciences Inc., Brea, USA.