Double-effect integration of multicomponent alcoholic distillation columns

Energy 45 (2012) 603e612

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Double-effect integration of multicomponent alcoholic distillation columns Larissa C.B.A. Bessa, Fabio R.M. Batista, Antonio J.A. Meirelles* Laboratory of Extraction, Applied Thermodynamics and Equilibrium, Department of Food Engineering, Faculty of Food Engineering, University of Campinas, Campinas 13083e862, São Paulo, Brazil

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 March 2012 Received in revised form 11 June 2012 Accepted 19 July 2012 Available online 9 August 2012

The growing need to expand the use of renewable energy sources in a sustainable manner, in order to provide energy supply security and to reduce the environmental impacts associated with fossil fuels, finds in bioethanol an alternative economically feasible and with significant potential of expansion. Despite its high energetic demand, distillation is one of the most widely used techniques for separating liquid mixtures. Thus, this work aimed to study distillation columns thermally integrated to produce bioethanol, considering a large amount of minor compounds so that the actual conditions can be better represented. In order to evaluate energy requirements, steady-state simulation of the distillation process was carried out using the software Aspen Plus. As a preliminary step, the simulator results for the current distillation columns configurations were compared with industrial data through analysis of samples collected from mills in operation. Although it has presented, in the case of some minor components, significant deviations, the simulator was able to reproduce satisfactorily the industrial process of alcoholic distillation. The thermally integrated configuration showed good results, with a reduction in the specific steam consumption of 54%. It was observed that minor compounds had a great influence in the steam consumption of the process. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Bioethanol Thermal integration Multicomponent distillation Sugarcane

1. Introduction Worldwide high demand for energy, global warming associated with the gasoline use, rising oil prices and the desire for energy supply security have led to an increasing interest on alternative fuels [1,2]. Fuels have been widely produced from fossil resources, but emerging attention has been given to the potential use of biomass as the basis for production of an alternative (and renewable) motor vehicle fuel. Biofuels are an often-cited option to provide such alternative [3,4]. The main types of feedstock for the production of bioethanol are raw materials containing fermentable sugars (sugarcane, beet and sweet sorghum), polysaccharides that can be hydrolyzed for obtaining fermentable sugars (starch contained in grains) and lignocellulosic biomass [5]. In Brazil, bioethanol is used as motor fuel in neat ethanol cars (hydrous ethanol) or blended (anhydrous ethanol) with gasoline in a proportion of 25%. Also, it is still used in flexible-fuel vehicles (FFV), which can be fueled with a mixture of gasoline and ethanol allowing to the consumer a higher flexibility to respond to price changes [6,7]. The use of ethanol allows better oxidation of hydrocarbons and reduces the amount of aromatic compounds and * Corresponding author. Tel.: þ55 19 3521 4037; fax: þ55 19 3521 4027. E-mail addresses: [email protected] (L.C.B.A. Bessa), fabio06m@ fea.unicamp.br (F.R.M. Batista), [email protected] (A.J.A. Meirelles). 0360-5442/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.energy.2012.07.038

carbon monoxide released onto the atmosphere [8]. The decrease in greenhouse gas emissions due to the use of anhydrous ethanol blended with gasoline in Brazil were estimated as 78% [7]. Brazil is worldwide the second largest producer of fuel ethanol, as US surpassed Brazil in 2006 [7]. However, all processing technologies, including biofuel options, involve the use of fossil fuels in their production and/or operation, such as the fuel consumption in the mechanized harvest of corn or sugarcane. Therefore, in practice the actual benefits of biofuels displacing their fossil fuel equivalents depend on the efficiency with which it can be produced [3]. This can be calculated by an index called fossil energy ratio (FER), which is the ratio between the energy contained in ethanol and the fossil energy used to produce it. Corn has FER equal to 1.4, while that of sugarcane is 8.3. It means that sugarcane is six times more efficient than corn when it comes to reducing the consumption of fossil fuels [9]. The use of commercial softwares, such as Aspen Plus and Hysys, for simulating distillation columns is a frequent topic in the literature, especially for mixtures containing hydrocarbons and similar organic compounds [10,11]. In the case of bioethanol production, simulations are frequently used for heat integration evaluation [12e14]. Distillation columns are the major energy consumers in the chemical industry. The distillation process generally employed in Brazilian distilleries is based in the same configuration used for decades, divided into rectifying and stripping sections, on which

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atmospheric pressures are adopted. Thermal energy has to be supplied to the stripping section and removed from the rectifying section [12,15]. To improve the energy efficiency, the heat integration concept was first introduced almost 70 years ago. Its basic idea is to exchange heat between the hot and the cold process streams. Thus, resources are used more economically, reducing the external energy inputs [16]. Engelien and Skogestad [11] have studied the separation of a hydrocarbon feed using the multi-effect distillation, whose central idea is to adjust the column pressures so that the overhead vapor of one column can be the heat source to the other column. For this integrated arrangement there are two possible types of integration: a forward integration, where the heat integration is in the direction of the mass flow, and a backward integration, where the integration is in the opposite direction of the mass flow [11,16,17]. Dias et al. [12] have studied the double-effect backward-integrated columns to produce anhydrous ethanol. Although that integration might be more suitable, the forward-integrated arrangement would be easier to control and, also, in terms of start-up: as the heat input is to the first column, this can be started up first and when it is running, it would be relatively easy to start the second up [11]. Sugarcane juice coming from fermentation step contains usually water, ethanol and several minor compounds like methanol, acetaldehyde and higher alcohols. The exact composition depends on the raw material and fermentation process conditions [5]. In contrast to previous researches on the heat integration of bioethanol distillation that considered the alcoholic wine as a simple binary mixture [18e20], the present study took into account the complexity of the fermented must with its multicomponent character. Despite their very low concentrations in the feed stream, minor compounds may exhibit large concentrations in the bottom trays of rectifying section. In order to evaluate energy requirements and the influence of minor components, the double-effect forward-integrated columns are investigated in this work using the software Aspen Plus. A wine with a large number of components is considered, as well as the fusel oil extraction and degassing process, so that the actual industrial process can be better represented. 2. Wine composition In this work, the wine was considered a mixture of 20 components, and their contents are shown in Table 1. These compounds were chosen because they are the most frequently found in the literature [21e23]. In addition, there was a concern to choose at least one compound of every different chemical class, and also components whose volatilities cover the entire range of possibilities, for instance, those more volatile than ethanol, some heavier than water and components with intermediate volatilities. All components presented in Table 1 are byproducts of the fermentation process, except the sulfur dioxide, which is derived from the sugar bleaching process.

The influence of the major components’ concentration on the relative volatilities of minor compounds present in wine has already been discussed in detail in the literature [24]. It was observed that in low concentrations of ethanol and high concentrations of water (the region near the bottom of the column), the volatility of higher alcohols is high and, therefore, they concentrate in the vapor phase, rising up to the trays located in the middle part of the column. On the other hand, as the ethanol concentration increases (the region near the top of the column), the volatility of these alcohols decreases, and they tend to concentrate in the liquid phase. Hence, the alcohols that compound the fusel oil reach maximum concentrations in the region near the bottom of the rectifying section, from where they must be removed. In fact, their removal as a sidestream is essential for the correct operation of the distillation column and the obtaining of a product with the desired content of ethanol. Batista and Meirelles [24] have investigated the distillation of an alcoholic wine with 11 components, and their contents were assumed in this work, except the major component ethanol. The ethanol composition was based on Marquini et al. [18], who considered a wine with higher alcoholic content (10% v/v). Fermented musts with higher alcohol content (8e12% v/v) are increasingly used in Brazilian sugar mills, because they allow a better industrial performance and a reduction of environmental impacts [25]. Thus, considering a wine with 10% (v/v) is a suitable assumption. For 2-butanol, propionic acid, 1-pentanol, 1-hexanol and active amyl alcohol, the mass fraction was estimated on the basis of the lower detection limit of the gas chromatography methodology used in this work. The concentration of these components in the fermented must can be very low, sometimes below the detection limit of the analytic equipments. In fact, in the wines analyzed in the present work they were not detected. In the case of sulfur dioxide, the composition was that one found by Gutierrez [26]. In order to estimate the content of carbon dioxide in the wine, it was used the NRTL (Non-random, two liquid) model for ethanolewater mixtures and the Henry constants reported by Dalmolin et al. [27] for CO2 dissolved in hydroalcoholic solutions. It was considered that industrial fermentation occurs in closed vessels under slightly positive pressure and temperature close to 32  C. In these conditions, and assuming that the gas phase inside the vessel is composed of carbon dioxide saturated with water and ethanol vapors, the solubility of CO2 in a wine with 10% v/v was estimated as 1169 mg CO2/kg wine. Since the composition ought to be exact in the simulator, the water composition was obtained by difference, regarding the sum of the values set for ethanol and the minor compounds. 3. Phase equilibrium of the components involved Two phases at the same temperature and pressure are in equilibrium when the fugacity of each component is equal in both phases [28]. The Vaporeliquid equilibrium inside the distillation columns is given by Eq. (1) [28]:

fi yi P ¼ gi xi Pivap

Table 1 Wine composition. Component

Mass fraction

Component

Mass fraction

Ethanol Water Isoamyl alcohol Propanol Isopropanol Isobutanol Butanol 2-Butanol Active amyl alcohol 1-Hexanol

0.081 0.91712934 0.0001425 3.00  105 1.02  106 2.78  105 1.43  106 1.00  106 1.00  106 1.00  106

1-Pentanol Methanol Acetaldehyde Acetone Ethyl acetate Methyl acetate Acetic acid Propionic acid Sulfur dioxide Carbon dioxide

1.00  106 3.20  107 1.58  105 1.50  105 7.69  106 1.00  106 0.0004351 1.00  106 1.90  105 0.001169

(1)

where yi and xi are the molar fraction of component i in the vapor vap and liquid phases, respectively, P is the system pressure, Pi is the vapor pressure of component i at the system temperature and gi and fi are the activity and fugacity coefficients of component i, respectively. In the production of hydrous ethanol, there is a removal of a sidestream called fusel oil, rich in higher alcohols (alcohols that have more than two carbon atoms) and which goes to a decanter where, with the addition of water, it can be separated into two liquid phases. Therefore, in the complete process of ethanol

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production, it is also necessary to consider the liquideliquid equilibrium, as described in Eq. (2):

gIi xIi ¼ gIIi xIIi

(2)

where the roman numerals I and II represent each liquid phase. In this work, the NRTL model was used to calculate the activity coefficient of each component, since it was able to reproduce experimental data for the binary ethanol/water, leading to lower deviations [29]. For the vapor phase, the Virial Equation was used in association with the Hayden-O’Connell model to estimate the fugacity coefficient, due to the presence of components able to dimerize, as organic acids [30]. The large amount of substances in the must and their low concentrations represent a great challenge in the description of this system in the simulator [31]. Thus, the choice of a truthful thermodynamic model is not enough to guarantee the reliability of the simulations; the efficiency of a model is related to an accurate adjustment of the parameters used in these equations. Therefore, a study was carried out to evaluate the parameters presented in the simulator databank. With the aid of the Data Regression tool of Aspen Plus, equilibrium data was calculated for each binary sub-system of the mixture using the NRTL parameters offered in the simulator. These data were compared to experimental data found in the literature [32e36] by calculating the absolute differences in temperature and composition of vapor phase (DyA), as well as the relative difference in the composition of the vapor phase (DyR).

PN

DyA ¼

 

n ¼ 1 yn;exp

(3)

N PN

DyR ¼ 100,

   yn;calc 

n¼1

    yn;exp  yn;calc  y

n;exp

N

(4)

where N is the number of data for each binary system. When the average absolute difference was less than 0.03 or the average relative difference less than 10%, the parameters were accepted. Otherwise, new parameters were adjusted with the experimental data from literature. Since the wine is composed by 20 components, there are 190 binaries to be studied. From these, 55 have already been studied by Batista and Meirelles [24], and they were directly accepted, except for 9 binaries (water/isoamyl alcohol, ethanol/propanol, isobutanol/ isoamyl alcohol, ethyl acetate/isoamyl alcohol, isopropanol/isoamyl alcohol, acetone/propanol, acetaldehyde/isopropanol, isobutanol/ acetone and acetaldehyde/acetic acid) for which more recent experimental data were available. From the remaining 135 binaries, it was possible to find experimental data for 76, of which 30 had their parameters readjusted. For the 59 binaries with no experimental data available, 2 (ethanol/SO2 and acetic acid/SO2) had parameters present in the databank of the simulator, and those were directly accepted, and for the remaining 57, the UNIFAC (Universal quasi-chemical functional-group activity coefficients) model was used to predict vaporeliquid equilibrium data. The NRTL parameters were, then, adjusted to these data. This procedure has already been used before, presenting good results [24,31]. There are no experimental measurements of vaporeliquid equilibrium for such a complex mixture as the alcoholic wine, and even for ternary or higher mixtures, they are quite scarce. Thus, the assessment of the binary parameters predictions in a multicomponent mixture is difficult. In order to evaluate the reliability of these parameters two procedures were adopted. The first one is

605

based in a literature check of multicomponent vaporeliquid equilibrium data and their subsequent comparison with equilibrium data calculated using the NRTL parameters. The same criterion of the binary parameters evaluation (DyA < 0.03 and DyR < 10%) was used in this case. VLE (Vapor-liquid equilibrium) experimental data were found [32,37e51] for 38 ternary systems and for 4 quaternary systems involving some components (ethanol, water and 9 congeners) present in the wine. The total number of experimental data was defined as (C e 1) ∙ S, where C is the number of components in each system (ternary or quaternary) and S, the total number of systems. It was verified that, from the total of 88 data, 6.8% had average absolute difference greater than 0.03 and 17% had average relative difference greater than 10%. It is worth noticing that due to the low composition of some components, it is expected that the relative deviations were higher. This evaluation suggests that the binary parameters are reliable. The second procedure adopted to evaluate the reliability of these parameters is the experimental validations, through the comparison between samples collected in industries and the results obtained with the simulator. 4. Validation of the simulator In order to verify whether the results obtained from the simulator are reliable, experimental validations were conducted, comparing the results generated by the software with samples and information collected in two industrial mills: Santa Adélia Mill e located in Pereira Barreto, São Paulo, Brazil e and Müller Beverage Company e located in Pirassununga, São Paulo, Brazil. The collected samples were analyzed through gas chromatography. The software Aspen Plus numbers the trays of a distillation column from the top to the bottom. For this reason, the descriptions of all columns simulated in this work will follow the same order. Also, it is worth mentioning that in all simulations the RadFrac module was used, which is based on the equilibrium method that utilizes MESH (Material, equilibrium, summation and heat) equations [13]. Besides, the convergence method used in all simulation was NewtoneRaphson. 4.1. Santa Adélia mill Santa Adelia mill e Pereira Barreto is a distillery and produces and sells only anhydrous ethanol, obtained by the dehydration of hydrous ethanol. The hydrous ethanol distillation system is composed of two sections: stripping of wine (named columns A, A1 and D) and rectifying and stripping of phlegm (columns B and B1), as shown in Fig. 1. Columns A1 and D are used mainly for reducing the contamination of bioethanol with volatile compounds when a product of higher purity is desired. In the case of fuel bioethanol, the level of contamination does not have to be so low. For this reason, column D operates under total reflux without any distillate withdrawal. Columns A and B1 are employed for stripping the liquid phase from ethanol, obtaining bottom products as almost ethanol-free streams. Column B concentrates ethanol in the vapor phase, so that bioethanol can be withdrawn from a tray close to the column top with the required concentration. In this mill, column A contains 20 trays, column A1 has 4 trays and column D, 6 trays. Wine is fed into the top of column A1 at 97  C with an average flow of 276.2 m3/h. Saturated steam at 0.731 bar (gauge pressure) is fed into the bottom of column A, where vinasse is withdrawn. The top product of column A1 feeds the base of column D, from where two products are obtained: the bottom product (BPD), which feeds the column B, and the top product,

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Fig. 1. Santa Adélia process configuration.

which goes to a set of condensers; in the last one a small fraction of vapor is released to the atmosphere, allowing a reduction in the content of volatile compounds in the liquid phase, which returns to the column D as total reflux. In the top of column A, an ethanol-rich stream containing around 40 wt% ethanol (phlegm) is withdrawn in the vapor phase and fed to the bottom of column B. Column B, containing 43 trays, receives the BPD (fed into the tray 40) and phlegm to enrich them, obtaining hydrous ethanol with ethanol content around 92.5 wt%, withdrawn in the fourth tray. As in column D, the top product follows to a set of condensers, releasing part of the vapor to the atmosphere and returning the condensate to the column as total reflux. Column B1, containing 15 trays, is heated with saturated steam. Fusel oil is removed from the tray 41 of column B, and follows to a decanter, where, with addition of water, it is obtained an aqueous phase, rich in water and ethanol and which goes to a tank to return to the process, and an organic phase, rich in higher alcohols, which can be commercialized as a byproduct. However, for the purpose of validation, the return of the aqueous phase was not simulated, since the sample of wine was collected directly from the tank, i.e., the wine composition used for the validation was already taking into account the recycled aqueous phase. The pressure drop considered in the block AA1 was 6.1 mwc (meters of water column) and in the block BB1, 4.8 mwc. Samples of wine, vinasse, phlegmasse, hydrous ethanol and fusel oil (organic phase) were collected.

Column 1 produces around 4000 L/h and contains 26 trays. The pressure in its base is 1.275 bar. Column 2 contains 18 trays, bottom pressure around 1.373 bar and production of approximately 10,000 L/h. Wine is fed into Column 1 in tray 11 and into Column 2 in tray 3. The production of vinasse, removed in the bottom of the columns, is in the approximate proportion of 6 L vinasse/L cachaça. 4.3. Gas chromatography Ethanol and most minor components were quantified by gas chromatography. The capillary gas chromatograph is equipped with a flame ionization detector and the column DB-624 Agilent was used (6% cyanopropyl-phenyl, 94% dimethylpolysiloxane), with 60 m of length, 0.25 mm of internal diameter and 1.4 mm of film thickness. Calibration curves were constructed with 8 dilution points for each component using chromatographic grade standardsamples, produced by Sigma Aldrich, with purity  99.9%. All samples were analyzed in triplicate, with the methodology described in Batista and Meirelles [24]. The components sulfur dioxide and carbon dioxide were not quantified in the samples. The water mass fraction was quantified, in most samples, by difference. However, for some samples of fusel oil the chromatogram presents some non-identified peaks and, therefore, the water content was determined by Karl Fischer titration. In these cases, the sample composition was normalized to compare with the data obtained in the simulator.

4.2. Müller beverage company 5. Thermal integration analysis Müller beverage company produces cachaça, a typical Brazilian spirit with ethanol content within the range of 38e54% v/v at 20  C, obtained by distillation of sugarcane fermented juice [52]. A typical industrial plant for continuous cachaça distillation is composed of one single column, containing a small rectifying section and a slightly larger stripping section. In the case of Müller beverage company, the total production is divided into four independents columns. Samples of distillate and vinasse of two columns (Column 1 and Column 2) were collected, and also a sample of wine, which feeds the four columns.

Steam demand was first evaluated for fuel ethanol production using conventional distillation columns in order to compare with the thermally integrated process. As the hydrous ethanol studied in the present work is for fuel purposes, there is no requirement for a high quality product in relation to the content of volatile compounds. Thus, the columns A1 and D were not considered and the ABB1 configuration (Fig. 2) was studied. Fusel oil is removed from column B and goes to a decanter, returning the aqueous phase to the process.

L.C.B.A. Bessa et al. / Energy 45 (2012) 603e612

607

Fig. 2. Conventional distillation process configuration.

Wine at 35  C and 1.177 bar, with a flow rate of 200,000 kg/h, is heated to 94  C by exchanging heat with vinasse, and then is fed to the top of column A, which contains 22 trays and operates, at its top, at 1.177 bar, with pressure drop of 0.336 bar and efficiency of 0.65. Saturated steam at 1.667 bar heats this column with flow rate sufficient to exhaust ethanol from wine, losing 0.02% in vinasse and getting phlegm with 35e45 wt% ethanol, which feeds the bottom of column B. 166 kg fusel oil/h is withdrawn from tray 43. Columns B and B1 contain 44 and 18 trays, respectively. The pressure at the top of column B is 1.013 bar, lower than that of column A in order to enable the admission of phlegm in its bottom. The pressure drop is 0.225 bar. The double-effect distillation system studied in this work is composed of two columns containing, each one, stripping of wine and rectifying of phlegm sections (columns A and B, with 22 and 44 trays, respectively), with sidestream of fusel oil removed in both columns, as shown in Fig. 3. The first column operates at a pressure greater than atmospheric (1.52 bar) and the second one, under

vacuum (0.219 bar). Thus, the heat generated in the condensation of hydrous ethanol from the first column can be used as heating source for the second column. The pressure drop in both columns is 0.225 bar. Wine at 94  C and 1.177 bar is fed to the process. Firstly, the mass flow of wine was divided equally and fed into both columns. Since the pressure of wine is lower than that of the tray where it is fed in the high pressure column, it is necessary to use a pump to raise its pressure in 0.659 bar. In order to determine the distillate rate, an ethanol mass balance was carried out (Eq. (5)) considering that 99.5% of ethanol is recovered in the hydrous ethanol:

0:995:we;W :W ¼ we;H :H

(5)

where: we;W and we;H are ethanol mass fraction in the wine and in the hydrous ethanol, and W and H are mass flow of wine and hydrous ethanol, respectively. Thus, the distillate rate should be

Fig. 3. Double-effect distillation process: HP ¼ high pressure, LP ¼ low pressure.

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around 17,332 kg/h. However, in the simulator, it was necessary adjustments in this value in order to ensure the desired ethanol content in hydrous ethanol and the maximum loss allowed in the vinasse. The exact values are shown in Table 2, in which the operational conditions are shown. In the low pressure column, the distillate rate and the heat supplied in the reboiler were specified, so that the reflux ratio was not an input data supplied by the user, but calculated by the simulator. In this case, the reflux ratio assumed the value of 5.7. Nevertheless, it is known that the separation of water and ethanol is easier under vacuum due to the displacement of the azeotropic point of the mixture, requiring a smaller amount of thermal energy. For this reason the lower specific steam consumption is not achieved, necessarily, when the feed stream is equally divided between the two columns. Regarding this, a study was conducted by varying the proportion of wine that feeds each column, keeping constant all other variables of the process, except the distillate rate, whose value was adjusted in each situation in order to obtain a top product with 93 wt% of ethanol and maximum loss of 0.02% in vinasse. Then, the specific steam consumption was calculated for each case. The results obtained from the simulator (for the conventional and integrated processes) were analyzed and compared using the following criteria: specific steam consumption (SSC) e Eq. (6), ethanol recovery (ER) e Eq. (7), and purification factor (PF) e Eq. (8):

SSC ¼

ER ¼

Qr

(6)

l,V_ p _ p ,wep m ,100 _ w ,wew m

(7)

  w P e wm p  PF ¼  we P wm w

(8)

where: l is the heat of vaporization, equal to 529.2 kcal/kg for saturated steam at 1.667 bar, Qr is the heat required in the reboiler, _ is the volumetric flow of hydrous ethanol, m _ w are the _ p and m Vp mass flow of hydrous ethanol and wine, respectively, we is the mass P fraction of ethanol, wm is the sum of mass fractions of all minor compounds, and the subscripts p and w stand for the product (hydrous bioethanol) and wine streams. The purification factor measures how much the distillate was purified from the contamination with minor components in relation to the wine. Although the removal of organic contaminants is not a main concern in the production of fuel ethanol, a higher purification factor can make feasible its use as raw material in other industrial branches. Finally, in order to evaluate the influence of specific minor components on the performance of the heat-integrated columns,

Table 3 Wine composition (in mass fraction). Component

Santa Adélia

Müller

Acetaldehyde Methanol Ethanol Propanol Ethyl acetate Isobutanol Butanol Isoamyl alcohol Active amyl alcohol Water

4.1  106 3.9  106 0.0642 4.9  105 1.9  105 7.3  105 2.8  106 2.4  104 4.7  105 0.9353

4.5  106 4.9  106 0.0487 1.6  105 1.1  105 2.5  105 e 5.5  105 1.7  105 0.9512

a sequence of simulations was carried out excluding in every case components of a specific volatility class. 6. Results and discussion 6.1. Validation of the simulator As mentioned before, the samples collected in both industrial plants were analyzed by gas chromatography in triplicate. The linear calibration curves, which correlate the area under the component peak with its composition, presented values of determination coefficients (R2) always higher than 0.992. The wine composition obtained from GC (gas chromatography), used as input data in the simulator, is shown in Table 3 for both mills. For the samples of vinasse and phlegmasse only ethanol was identified in the gas chromatography analysis, and the water was obtained by difference. The comparison of the experimental samples with the simulated results is shown in Table 4. It can be noted that the values predicted by the simulator are very close to the experimental values, especially in the case of Santa Adélia mill. For the samples of Müller beverage company, the differences are larger, in particular for vinasse from Column 2. However, it is worth noting that in all cases, the maximum allowed loss in these streams was observed. Finally, the results for cachaça from Müller beverage company and for hydrous ethanol and fusel oil from Santa Adélia mill can be observed in Table 5. Regarding the results for Müller beverage company, it can be observed that the behavior of a cachaça distillation column was well represented by the simulator, since the predicted values are quite similar to the experimental ones, even for the minor components. As regards to the samples from Santa Adélia mill, it is possible to note that the simulator presented good results for the major components of each sample. Besides, the experimental and simulated results for propanol and ethyl acetate

Table 4 Experimental and simulated compositions (mass fraction) for vinasse and phlegmasse. Ethanol Santa Adélia mill Vinasse

Table 2 Operational conditions-double-effect. Number of stages Feed stage WINE Withdrawal stage FUSEL HYDROUS ETOH VINASSE Distillate rate (kg/h) Reflux ratio Flow rate FUSEL (kg/h)

HP 17,315 5.6 166

68 46 44 1 68 LP 17,365 e 166

Exp.a Sim.b Phlegmasse Exp.a Sim.b Müller beverage company Vinasse 1 Exp.a Sim.b Vinasse 2 Exp.a Sim.b

a b

Experimental. Simulated.

Water

2.9 2.9 1.3 1.1

   

105 105 105 105

0.99997 0.99997 0.99999 0.99999

4.9 1.6 1.9 6.1

   

105 105 105 106

0.99995 0.99998 0.99998 0.99999

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609

Table 5 Experimental and simulated compositions (mass fraction) for hydrous ethanol, fusel oil and cachaça. Hydrous ethanol

Acetaldehyde Methanol Ethanol Propanol Ethyl acetate Isobutanol Butanol Isoamyl alcohol Active amyl alcohol Water a b

Fusel oil

Cachaça 1

Cachaça 2

Exp.a

Sim.b

Exp.a

Sim.b

Exp.a

Sim.b

Exp.a

Sim.b

1.8  105 3.6  105 0.9349 5.9  104 6.4  105 3.4  105 e e e 0.06434

6.4  106 4.2  105 0.9346 4.3  104 8.7  105 5.5  106 e e e 0.06485

1.4  105 1.2  105 0.1911 4.6  103 1.8  105 0.0796 4.0  103 0.4174 0.0907 0.21253

4.1  107 3.8  106 0.1966 5.8  103 1.7  105 0.1336 4.4  103 0.4169 0.0886 0.15415

3.1  105 3.4  105 0.3915 9.9  105 5.2  105 2.1  104 e 4.1  104 1.3  104 0.60750

2.4  105 2.0  105 0.3904 1.3  104 8.6  105 2.0  104 e 4.4  104 1.4  104 0.60856

2.5  105 2.0  105 0.3865 9.2  105 5.4  105 1.9  104 e 4.1  104 1.2  104 0.61261

2.3  105 1.9  105 0.3815 1.3  104 8.4  105 2.0  104 e 4.3  104 1.4  104 0.61752

Experimental. Simulated.

have the same order of magnitude in both samples, while for acetaldehyde and isobutanol, the deviations were higher. As acetaldehyde is an extremely volatile compound, a large amount of it is released to the atmosphere in the degassing stream, so that this difference can be explained by the fact that there is no information concerning the mass flow of this stream, which made the validation harder. The great difference in water content of fusel oil is mainly due to the difference in composition of isobutanol. For the other components, the deviations observed are within an acceptable range. Also, it was not possible to collect information about the phlegm withdrawn from column A. Thus, only its ethanol content was evaluated, which should normally be around 35 wt% of ethanol. Since the phlegm generated in column A, according to the simulator, contains 38 wt% of ethanol, it can be concluded that column A has been properly represented by the simulator. Analyzing all the results obtained in this validation test, it is noted that the largest differences between the experimental and simulated results are related to minor components. In fact, in the case of major components the results were good in qualitative as well as in quantitative terms. For the minor compounds, however, the deviations were considerably higher, but in general the same order of magnitude was observed, indicating that the simulator should be considered able to reproduce the major trends of their distillation behavior. Considering the very low range of concentration values for the minor components, higher deviations between experimental and simulated results were almost unavoidable. Taking these aspects into account, it is possible to conclude that the commercial simulator was able to reproduce satisfactorily the industrial process of bioethanol distillation.

Furthermore, these results reinforce that the binary parameters were well adjusted, suggesting that the behavior of the multicomponent alcoholic wine can be reliably predicted using the related binary data. 6.2. Thermal integration analysis As regards to the study of varying the wine proportion, the specific steam consumptions are shown in Fig. 4. It is worth remembering that the necessary energy in the low pressure column comes from the heat generated in the hydrous ethanol condensation from high pressure column. Besides, when the wine flow rate fed in this column is lower, the generated heat in its top decreases. Thus, there is a point where the amount of heat is not sufficient to enrich the wine in the column under vacuum up to an alcohol content of 93 wt%, so that it is necessary, in these cases, to feed additional steam to this column, which was added to the global steam consumption. According to this figure, the lowest specific steam consumption is achieved when 38% of the wine is fed to the high pressure column, consuming, in this case, 0.995 kg steam/L hydrous ethanol. The results in this optimum condition are shown in Table 6, as well as the results for the conventional process. It can be seen that the specific steam consumption was reduced in almost 54%, compared to the conventional process. Nakaiwa et al. [15], Matsuda et al. [53] and Kiran et al. [54] have investigated the energy saving in distillation columns of hydrocarbons mixtures using different techniques of thermal integration (self-heat recuperation technology e SHRT and secondary reflux and vaporization e SRV, also called heat-integrated distillation column e HIDiC), reaching reduction in the energy consumption within the range of 38e60%. Although the mixture is different, it can be seen that the result obtained in the present work is in agreement with those of the aforementioned works. As regards to the bioethanol distillation, Dias et al. [12] have studied the whole process of anhydrous bioethanol production from sugarcane, including the double-effect backward-integrated columns. However, they integrated the hot and cold streams of the different distillation columns for hydrous and anhydrous Table 6 Results for the conventional and integrated processes.

Fig. 4. Specific steam consumption for different proportions of wine.

Parameter

Conventional

Integrated

Specific steam consumption (SSC) Ethanol recovery (ER)

2.151 99.21

P ( w m) P Purification factor (PF) Distillate ethanol content

1.16  102 1.844 0.930

0.995 99.26 HP 7.98  103 2.692 0.930

LP 8.28  103 2.592 0.930

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Fig. 6. Higher alcohols profile in the HP column: (C) liquid phase, (D) vapor phase. Fig. 5. Temperature profile in the LP column.

bioethanol, and they present results in terms of overall energy consumption, and not only the energy saving specifically in the hydrous ethanol distillation, so that the comparison with that work is not feasible. Back to the analysis of Table 6, the ethanol recovery was almost the same in both process, and the hydrous ethanol obtained in the integrated process was purer in terms of minor compounds, as indicated by the total concentration of minor components and the purification factors. The temperature on the top of HP (high pressure) column is 82.1  C and on the bottom of LP (low pressure) columns is 77.1  C. Hence, the integration is possible, though it requires a heat exchanger with a large heat transfer area. In addition, the temperature of the hydrous ethanol from LP column is 29.8  C, as shown in Fig. 5, in which the temperature profile of LP column is presented. It can be seen a significant decrease in temperature in the first stage of the column, which occurs due to the presence of carbon dioxide, an incondensable gas. Usually, in Brazilian sugar mills, the refrigerant used in the top condenser is water at room temperature, but in the case of the LP column a water cooling system would be required. Table 7 shows the results of the simulations in which the wine composition was varied. Each column corresponds to a simulation with the wine composed of the components marked with an “x”. It can be observed that each group of minor components has some influence on the specific steam consumption, in comparison with the value obtained for the mixture containing only ethanol plus water, but heavy and volatile components have a lower influence than that caused by higher alcohols and carbon dioxide. The components of fusel oil have partial miscibility with water,

indicating that their activity coefficients are significantly high in aqueous solutions, as well as the activity coefficient of water (significant positive deviation from ideality). Thus, in the presence of higher alcohols water becomes more volatile and a higher reflux ratio is required to concentrate ethanol up to 93 wt%. Besides, in all simulations that carbon dioxide was not present, it is observed a higher purification factor. Since CO2 is a noncondensable component, its mere presence in the system increases the concentration of minor compounds in the distillate, and therefore the purification factors are smaller. In the case of the simulations with a feed stream containing carbon dioxide, if this component is not computed in the calculation of the purification factors their values increase and become similar to the obtained in the other simulations. In addition, it is possible to note in those simulations without CO2 a larger temperature difference between the top of HP column and the bottom of LP column. Also, the temperature of the hydrous ethanol from LP column is always more than 43.5  C, which eliminates the need of a water cooling system. The presence of carbon dioxide in the column leads to a sharp drop in temperature at the top of the column. The solubility of CO2 decreases with the increase of temperature and the reduction of pressure. Thus, the reduction of the temperature at the top of the column is even more critical in columns under vacuum. The CO2 concentration in the product being condensed can be decreased by means of a larger degassing stream. This could increase the condensation temperature, but would imply either in a higher loss of ethanol through the degassing stream or in an additional system for recovering it from that stream. It is noteworthy that the equilibrium parameters between carbon dioxide and the other components were obtained using the predictive UNIFAC model, since there were no NRTL parameters in the

Table 7 Results for different wine compositions. 1 CO2 Higher alcohols Volatile compounds Heavy compounds Ethanol þ water SSC (kg steam/L hydrous ethanol) ER (%) PF HP PF LP DT ( C) TC. LP ( C)

x x x x x 0.995 99.26 2.692 2.592 5.0 29.8

2 x x x x 0.949 99.64 8.760 7.585 11.6 43.7

3

4

5

6

7

x

x x

x x x

x x

x

x 0.988 99.32 2.063 1.987 5.0 29.8

x 0.991 99.36 2.117 2.035 5.0 29.8

x 0.964 99.59 1.837 1.834 5.1 30.0

x x x 0.969 99.55 2.439 2.438 5.0 29.9

x x 0.995 99.35 2.791 2.682 5.0 29.8

8

9

x

x 0.943 99.61 9.914 6.132 11.7 43.9

x 0.925 99.85 e e 11.7 43.9

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database of Aspen Plus. Thus, it is necessary to consider that the equilibrium of the system containing CO2 has a level of uncertainty inherent to a predictive method, so that this drop in temperature may be overestimated. Finally, it can be noticed that the simulation considering only water and ethanol (simulation 9) showed the lowest steam consumption, which shows that the minor components have a significant influence on steam consumption and therefore they should not be ignored in studies and researches on the subject. In fact, their presence increases the specific steam consumption in more than 7%. Fig. 6 shows the higher alcohols composition profiles in the vapor and liquid phases in the high pressure column. The analysis of these profiles clarifies the phenomenon involving the abrupt change in volatility of higher alcohols along the column. Since the bottom of column B is a region rich in ethanol, because of the feeding of phlegm, the volatility of higher alcohols decreases, and they tend to concentrate in the liquid phase. In column A the inverse process occurs; the descending liquid has increasing concentration of water, augmenting the volatility of higher alcohols and causing them to concentrate in the vapor phase. It confirms that these components must be removed from the column by a sidestream, since they tend to concentrate in the liquid phase at the bottom of the rectifying section and in the vapor phase in the top of the stripping section. This makes clear that it is not possible to represent appropriately the whole industrial process of bioethanol distillation without taking into account the presence of such minor compounds that can attain so large a concentration in parts of the equipment. Note that from a very low mass fraction in wine (whigher alcohols ¼ 2.07 , 104) they concentrate in specific column trays to reach values within the range 0.1e0.21. 7. Conclusions Samples of products and byproducts from two Brazilian plants were collected in order to check whether the simulation results of industrial plants used for bioethanol distillation are reliable. In general, the commercial simulator Aspen Plus reproduced satisfactorily the industrial alcoholic distillation process, at least from a qualitative point of view. The double-effect thermal integration was then studied based on the simulation approach validated by comparison with industrial data. The results indicated that hydrous ethanol can be produced according to the required specifications and with a significant decrease of the steam consumption. The specific steam consumption was 0.995 kg steam/L hydrous ethanol, which corresponds to a reduction of approximately 54% compared to the conventional process. Although the simulation results indicate that the thermal integration promoted important energy savings in the bioethanol process, achieving high purity and a high ethanol recovery in the main product stream, it requires a heat exchanger with large dimensions due to the low difference in temperature between the top product of the first column and the bottom product of the second column. Also, in this configuration, the diameter of the low pressure column must be greater because of the vacuum, requiring higher investments, which was not considered in this paper. A series of simulations was conducted removing in each case components of a given group of wine contaminants. Higher alcohols and carbon dioxide were the minor components with the greatest influence on the steam consumption. It was also observed that the presence of carbon dioxide causes a sharp drop in the condenser temperature, so that cooling water must be available at a lower temperature. The results suggest that the presence of wine contaminants must be considered for a reliable simulation of the industrial bioethanol distillation process. Furthermore, the

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