Performance and cost evaluation of a new double-effect integration of multicomponent bioethanol distillation

Energy 63 (2013) 1e9

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Performance and cost evaluation of a new double-effect integration of multicomponent bioethanol distillation Larissa C.B.A. Bessa, M.C. Ferreira, Eduardo A.C. 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 17 October 2012 Received in revised form 3 May 2013 Accepted 3 October 2013 Available online 26 October 2013

Bioethanol derived from sugarcane is the most advanced alternative to fossil fuels and part of the solution in the efforts to achieve a low-carbon emissions world. Since distillation accounts for a large part of total energy consumption by industry, the need to reduce energy requirements serves as motivation for the study of this process. Many energy efficient schemes have been developed with this aim. However, most of them focus on columns working under pressure. Due to the organic nature of sugarcane and also to the liming process to which its juice is submitted, significant fouling can result from the insolubility of calcium salts, which is intensified at higher temperatures. In this work, Aspen PlusÒ was used to investigate the energy requirement of a configuration of double-effect forward-integrated columns, considering an extra stripping section to reduce the risk of fouling. The wine was considered a multicomponent mixture. Response surface methodology was applied to optimize the process and analyze some operating parameters. The configurations studied were adequate, but involved higher investments. The total annual costs were observed to be lower than those of a conventional process. Finally, congeners tended to have a negative effect on the specific steam consumption of the process. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Distillation Heat integration Minor components Bioethanol Fouling

1. Introduction Due to depletion of the world’s petroleum reserve, increasing petroleum prices and the threat of global warming, the use of biomass, particularly biofuels, for energy purposes is becoming increasingly attractive [1,2]. In general, biofuels such as biodiesel and bioethanol are products that can be used for powering internal combustion engines. They are renewable and can recycle the carbon dioxide from their combustion by a photosynthetic process [3]. Brazil has used ethanol as a motor fuel since the 1930s, when the government introduced its use as a 5% blend with gasoline [4], reaching current blend rates of up to 25% [5]. It is predicted that ethanol production will double in less than a decade [6]. Bioethanol is produced by fermentation of the sugar found in biomass in the form of sucrose, starch or lignocellulose [7]. In Brazil, it is produced from sugarcane juice, which is referred to as wine after the fermentation step. Wine is a complex mixture containing water,

* Corresponding author. Tel.: þ55 19 3521 4037; fax: þ55 19 3521 4027. E-mail addresses: [email protected] (L.C.B.A. Bessa), celacra@ fea.unicamp.br (M.C. Ferreira), [email protected] (E.A.C. Batista), tomze@ fea.unicamp.br (A.J.A. Meirelles). 0360-5442/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.energy.2013.10.006

ethanol and several minor compounds (also called congeners) that must be separated by distillation. Although the distillation process is one of the major energy consumers in industry, it is the most widely used technique for separation of liquid mixtures [2,8]. Thus, any small improvement in distillation processes can provide huge energy savings [9]. In order to reduce the energy requirement in distillation, many energy efficient schemes, such as multi-effect distillation and heat pump distillation, have been developed [10]. The simplest case of multieffect distillation is the split-feed in two-column configuration. In this design, the feed stream is divided and processed independently in two columns. The only link between the two columns is via heat exchange [11]. To provide the temperatures necessary to serve as the driving force for heat transfer between the columns, one of them must be operated at a higher pressure than the other [12]. Nevertheless, in the case of distillation in the sugarcane industry, the high pressure might be problematic. After extraction, the raw sugarcane juice contains about 4% suspended solids, which are decanted with lime, obtaining a clarified juice that contains 0.5% suspended solids at most [13]. The conventional liming process does not completely precipitate out the impurities; on the other hand, the treatment enhances the calcium concentration [14]. Significant fouling during distillation can result from the deposition of these remaining suspended solids.

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Fouling in distillation columns can result in the following: (i) greater energy consumption due to heat transfer and efficiency problems; (ii) reduced column capacity, which may lead to production losses, and (iii) increased downtime for cleaning the column and disposing of fouling material [15]. The main salts responsible for the fouling in distillation columns are calcium crystals such as calcium sulfate and calcium carbonate. The solubility of these salts in aqueous solutions decreases with temperature [15,16]; therefore, the risk of fouling increases in environments at high temperatures. In addition, the solubility of calcium salts also decreases with alcohol concentration [17e19]. Double-effect distillation had already been studied to investigate the ethanol/water separation considering its multicomponent character [20]. In that work, the configuration studied was composed of two columns, each containing sections for wine stripping and phlegm rectifying (columns A and B, respectively). The first column operates at a pressure greater than atmospheric pressure and the second column, under vacuum. The warm stream that leaves the bottom of column B has a relatively high ethanol content and feeds in at the top of column A. Hence, the ideal conditions for the insolubility of calcium salts were established at the top of column A. The ABB1 configuration studied by Marquini [21] offers an alternative. In this scheme, an extra stripping section, called column B1, is used in order to exhaust the ethanol contained in the phlegm. In this case, the liquid stream leaving the bottom of column B does not return to column A, but is exhausted in column B1. Since the calcium salts tend to remain in column A, the development of fouling is reduced, as are the harmful effects caused by it. In that work, this configuration was studied in order to separate the ethanol/water binary mixture. RSM (Response surface methodology) is a useful statistical technique that has been applied in research to determine the effect of independent variables (factors) on the dependent variables (responses) of a particular process [22]. RSM helps to minimize the number of experiments needed to detect local maximums and minimums [23]. Chen et al. [24] applied RSM in order to evaluate and optimize the conditions for the maximum conversion to biodiesel, using soybean oil as feedstock. Rashid et al. [1] studied biodiesel production from a non-food oil source, muskmelon seed oil, and they employed RSM to analyze the influence of four variables on the process. Although RSM is commonly implemented in physical processes (i.e., laboratory experiments), it can also be successfully applied to computer simulation of physical systems. The assumption is that if the computer simulation model is a faithful representation of the real system, then the RSM optimization will result in adequate determination of the optimum conditions for the real system [22]. For instance, Ceriani et al. [25] studied the deacidification step in the physical refining of sunflower oil using computational simulation, factorial design and RSM in order to minimize final levels of total and individual trans-fatty acids, keeping neutral oil loss and final oil acidity at low values. Furthermore, Batista et al. [26] investigated the possibility of optimizing the current bioethanol production using Aspen PlusÒ; he compared two different methods, RSM and SQP (sequential quadratic programming), and concluded that the approaches provided similar optimal conditions. Lefevre et al. [27] applied RSM to compare and optimize two reactors in the wet air oxidation process, used for wastewater treatment. The process was simulated using ProSimPlus software. Long and Lee [28] also used a RSM-based model to design and optimize a dividing wall column for acetic acid purification, running simulations on the commercial Aspen HysysÒ simulator.

Chang et al. [29] studied a direct contact membrane distillation for desalination built in Aspen PlusÒ, applying RSM to develop a performance-variables quadratic model, followed by multivariable optimization. The present work studied the ABB1 configuration proposed by Marquini [21], taking into account the multicomponent character of the wine and also the fusel oil extraction and degassing process in order to improve the representation of the real industrial process. The Aspen PlusÒ software was used to simulate this process, evaluating its energy demand and final product quality. In the double-effect configuration, the energy advantage is the reduction in reflux ratio, enabling a specific steam consumption that is lower than that of the conventional process. Response surface methodology based on central composite design was implemented to optimize the process and analyze some operating and configuration parameters. In addition, a cost analysis was conducted considering capital and operating costs in order to verify the economic feasibility of this configuration. 2. Wine composition and phase equilibrium In this work, the wine composition was the same as that used in previous work [20], containing ethanol, water and 18 minor components. Among the minor components, there are the isoamyl alcohol, active amyl alcohol, isobutanol, butanol and other relevant alcohols (called higher alcohols) that can have an impact on the operation of the distillation column and the obtaining of a product with the required ethanol content. The processes were simulated using the Aspen PlusÒ V7.1 commercial simulator. The phase equilibrium of the components involved was described using the NRTL (non-random two-liquid) thermodynamic model for calculating the activity coefficients of the liquid phase and the Virial Equation in association with the Hayden-O’Connell model for estimating the fugacity coefficients of the vapor phase. The interaction parameters used in these equations were those obtained in the previous work, in which the phase equilibrium description was improved by testing the Aspen PlusÒ databank parameters against experimental data and readjusting the NRTL interaction parameters when necessary. The reliability of these parameters and of the results generated by the simulator had been rigorously validated [20,26] by comparing the results with samples and information obtained from industrial mills in the State of São Paulo, Brazil and concluding that the commercial simulator was able to reproduce satisfactorily the industrial process of bioethanol distillation. 3. Heat-integrated configuration Marquini [21] proposed an adaptation of the traditional doubleeffect thermal integration model to separate the ethanol/water binary mixture by which the wine that feeds the distillation process is divided into two parts: w1 and w2. Stream w1 exchanges heat with the vinasse (bottom product from column A), reaching 94  C, and is fed in at the top of the stripping section (column A) to be exhausted. In this column, an alcoholic phlegm is generated and removed at the top in the vapor phase and then condensed in the reboiler of column B1, recovering part of the energy available in the phlegm. The condensed phlegm incorporated into an additional quantity of wine, referred to as w2, is fed in at the top of column B1. This stream is called rich wine, since its alcohol content is high. Column B receives the vapor from column B1 in order to produce hydrous ethanol, which is then withdrawn from the top of the column. Columns B and B1 operate under vacuum so the bottom temperature is lower than the temperature of the phlegm from column

L.C.B.A. Bessa et al. / Energy 63 (2013) 1e9

A, allowing its use as heat source for columns B and B1. Saturated steam at 1.667 bar was used as heat source for column A and as complementary heat for column B1. This configuration has two main benefits: (i) the utilization of an extra stripping section (column B1), preventing the return of the warm and ethanol-rich stream from column B to column A, and (ii) the possibility of adopting a lower difference in pressure between columns A and BB1; both reduce the risk of fouling in column A. In the present work, this configuration was studied considering the wine as a multicomponent mixture composed of the 20 aforementioned components at their respective concentrations [20]. Wine at 35  C and 1.177 bar feeds the process at a flow rate of 200 ton/h. The top of column A operates at 1.177 bar, with a pressure drop of 0.336 bar and a Murphree efficiency of 0.65. The pressure at the top of block BB1 is 0.288 bar with a pressure drop of 0.225 bar and Murphree efficiencies of 0.5 in column B and 0.6 in column B1. Higher alcohols are alcohols with more than two carbon atoms. They have intermediate relative volatilities, reaching maximum concentrations in the region between the bottom of column B and the top of column B1, so they can impair and eventually prevent the bioethanol from reaching a high concentration at the top of column B. For this reason, it is necessary to withdraw a side stream called fusel oil in trays near the bottom of column B. Since this stream contains a large amount of ethanol, it is commonly employed a decanter to separate the ethanol present in fusel oil. For this purpose, cold water is added at a ratio of 4 : 6 (water : fusel oil) at 25  C and 1.013 bar. Two streams leave the decanter: an organic phase that is rich in isoamyl alcohol and other higher alcohols, which can be commercialized by the mill, and an aqueous phase with a significant bioethanol content, which returns to the feed tank to be fed

3

back into the process with the wine. The simulated configuration is shown in Fig. 1. The distillate rate can be estimated through an ethanol mass balance, considering that approximately 99.5% of the ethanol fed into the process is recovered in the hydrous ethanol with a 93%wt composition. Thus, the distillate rate should be around 17,332 kg/h. The presence of carbon dioxide, a non-condensable gas, in the column causes a sharp drop in temperature at the top of the column. Thus, in order to allow the use of water at room temperature to condense the distillate, a degassing stream is required at the top, releasing a large amount of CO2 into the atmosphere. This degassing is performed by means of partial condensation of the distillate. Industrially, the condensation is carried out by controlling the flow of cooling water, so when a partial condensation is desired, the water flow is reduced. On the other hand, in the simulator, it is possible to control condensation by defining the desired vapor fraction in the distillate. However, the increase in temperature of the condenser (obtained by a larger degassing stream) would result in a greater loss of ethanol through that stream. Therefore, to ensure a feasible distillate temperature and an acceptable loss of ethanol, it was determined that the non-condensed fraction is 1% of the total distillate, which generates a flow rate of hydrous ethanol of approximately 17,300 kg/h and a degassing stream of approximately175 kg/h at 35  C. As an alternative, it is possible to increase the vapor fraction in the distillate and adopt an additional system for recovering ethanol. Thus, a second type of simulation was performed, increasing the non-condensed fraction of the total distillate and implementing a wash column in order to recover as much of the ethanol lost in the degassing stream as possible.

Fig. 1. Heat-integrated configuration.

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Fusel oil mass flow was set at 166 kg/h in both configurations in order to obtain a mass flow of the organic phase from the decanter corresponding to 0.3% of the hydrous ethanol mass flow, the value usually adopted by Brazilian mills. The results obtained from the simulator were analyzed in terms of SSC (specific steam consumption), ER (ethanol recovery), PF (purification factor) and TAC (total annual cost) savings. The purification factor determines how much the distillate was purified from minor components in relation to the wine and is obtained through the ratio of ethanol to minor components in the wine and bioethanol. These results were compared to those on the conventional distillation columns studied previously [20].

sum of operating costs and equipment costs. Operating costs are assumed to consist only of utility costs (steam and cooling water) with low-pressure steam at $7.78 per GJ [30] and cooling water at $0.067 per 1000 kg [31]. The following economic calculations are based on Douglas [32]. The construction differences between the configurations are the column diameters and the extra heat exchanger needed in the heatintegrated configurations, so the costs of all other equipment are assumed to be the same in all configurations. The cost of the distillation columns (stainless steel construction) can be estimated with Eqs. (1) and (2). The total column cost is the sum of the cost of column shell and the cost of column trays.

4. Response surface methodology

Ccolumn ;shell ¼

The effect of some process variables e NTA (number of trays in column A), NTB (number of trays in column B) and NTB1 (number of trays in column B1); TFOR (tray for fusel oil removal) and proportion of w1 to w2 (PW) e on specific steam consumption, ethanol recovery, purification factor and ECP (ethanol content in phlegmasse) was studied using the fractional factorial design, selecting the statistically significant variables. The factors and levels used in this study are shown in Table 1. With the results obtained in these simulations, the CCD (central composite design) was performed to fit the experimental responses to a second-order model and to define the optimized process conditions using RSM. Response surface methodology explores the relationship between several explanatory (or independent) variables and one or more response variables [29]. It is a collection of statistical and mathematical techniques that are useful for mapping a response surface over a particular region of interest, optimizing a response and/or selecting operating conditions to achieve specifications or customer requirements. For a problem involving k factors, CCD data points consist of 2k factorial points, 2k axial points (also called star points) and several center points. The locations of axial points in terms of coded values are  a, which can be determined by the rotatability condition, where a ¼ (2k)1/4 [22]. All the experimental data were statistically analyzed using Statistica software (StatsoftÒ, v. 10). 5. Cost analysis Despite the reduction in specific steam consumption, the heatintegrated configurations require higher investments, since columns with larger diameters are needed for operation under vacuum. In addition, in these configurations there is an extra heat exchanger responsible for the heat integration between the columns. In order to verify the economic feasibility of the configurations, a cost analysis was performed to calculate the total annual cost of the heat-integrated configurations and the conventional configuration. In this study the cost of each scheme is assumed to be the Table 1 Factors’ coded levels. Factor

x1 x2 x3 x4 x5

Levels

Number of trays in column A (NTA) Number of trays in column B (NTB) Number of trays in column B1 (NTB1) Tray for fusel oil removal (TFOR)a Proportion of w1  w2 (PW)

 M&S 698,D1:066 ,H0:802 280

(1)

 M&S 14:57,D1:55 ,h 280

(2)




1

0

þ1

18 33 14 x2 40

22 38 18 x22 63

26 43 22 x24 86

a The variable TFOR was counted according to the number of trays in column B (x2). Thus, the tray for fusel oil removal in the conditions 1, 0 and þ1 are in the first, third and fifth trays from the base of column B to the top, respectively.


Ccolumn;trays ¼

where Ccolumn, shell and Ccolumn, trays are the cost of column shell and the cost of column trays ($), respectively; M&S is the Marshall & Swift index (1536.5 [33]); D is the column diameter (ft), obtained using the Aspen tray sizing tool; H is the column height (ft), given by the number of trays with 0.61 m spacing plus 20% extra length, and h is the tray stack height (ft). The cost of the extra heat exchanger can be correlated as a function of the surface area assuming shell and tube, floating head and stainless steel construction, as shown in Eq. (3)


CHE ¼

 M&S 611:85,A0:65 280

(3)

where A is the surface area (ft2), 200 < A < 5000. In addition, due to the drop in temperature at the top of the column in the heat-integrated configuration, on warmer days cooling water must be at a lower temperature. The cost of a watercooling system was obtained by price quote from a Brazilian producer (Körper Equipamentos Industriais Ltda). It is important to mention that for the second heat-integrated configuration, which includes a wash column, the cost of this wash column, obtained using Eqs. (1) and (2), must be taken into account. The capital cost (equipment cost) is annualized over a period often referred to as plant lifetime [34], and the total annual cost is then calculated by summing the annual operating cost and the annual capital cost. In this work, plant lifetime was considered to be 10 years and 190 days of operation/year was used for the calculation [35]. 6. Results and discussion 6.1. Experimental design A fractional design was implemented in order to verify the effect of some process variables. All combinations of variables studied in the statistical analysis and the corresponding responses for the factorial design are shown in Table 2. As can be seen in the results obtained for the various tests, the simulations showed practically constant values of ethanol recovery and purification factor, which means that these variables are independent of the factors (number of trays in columns A, B and B1; tray for fusel oil removal and proportion of w1 to w2) within the range of values studied. In relation to the ethanol content in phlegmasse, expressed in mg/kg, it is important to note that the values obtained show two

L.C.B.A. Bessa et al. / Energy 63 (2013) 1e9

ranges of variation: one between 2.45 and 0.031  108 mg/kg and the other between 112.99 and 130.39 mg/kg, which differ according to the proportion of w1 to w2, the only statistically significant variable. It can be observed that when the variable PW (x5) is 40%, the values of ECP are greater than in the case when PW ¼ 86%. This occurs because the greater the flow of wine going to column BB1, the more ethanol is fed into that column and therefore the more ethanol is lost in phlegmasse, since the other output streams were not changed. However, it is noteworthy that in all simulations, the ECP values were lower than the threshold required by the mills, 200 mg/kg. Hence, the central composite design was performed only for the statistically significant independent variables for specific consumption steam, which were, at a 90% significance level, the number of trays in column B (NTB) and the proportion of w1 to w2 (PW). The CCD was then employed and the total number of runs was 2k þ 2k þ 1 ¼ 9, where k is the number of independent variables (k ¼ 2). Four factorial and four axial runs were added with one simulation at the central point. It is worth mentioning that the replications at the center point were used to evaluate the pure error [36]. Since the experiments in this work were performed using computational tools, it is useless to replicate the simulation with the same input data. Thus, it was necessary to determine the new levels for the variables to be studied and also define the values adopted for the variables NTA, NTB1 and TFOR. The first two remained at the central point of the fractional factorial design. For TFOR, it could be seen that in most cases the higher the tray from which this product is removed, the greater the amount of steam required by the process, as shown in Table 2. For this reason it was decided that fusel oil should be removed from the tray (x21), where x2 is the number of trays in column B. The levels used in this study are shown in Table 3, in which the a value is  (22)1/4 ¼ 1.41, according to the rotatability conditions. It is worth noting that, since the number of trays in column B is an integer variable, the locations of axial points in terms of coded values (a) for this variable are  1.5. The matrix used for the CCD as well as the responses obtained in each simulation are shown in Table 4. A polynomial equation (Eq. (4)) was obtained to correlate the two independent variables with the response acquired in Table 4. Thus, using the values of the independent variables in coded form, the representative mathematical model of the response SSC was as follows:

Table 2 Fractional factorial design: 2(51) þ central point. Run

NTA

NTB

NTB1

TFORa

PW

SSC

PF

ER

ECP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

18 26 18 26 18 26 18 26 18 26 18 26 18 26 18 26 22

33 33 43 43 33 33 43 43 33 33 43 43 33 33 43 43 38

14 14 14 14 22 22 22 22 14 14 14 14 22 22 22 22 18

x2 x2 x2 x2 x2 x2 x2 x2 x24 x24 x24 x24 x24 x24 x24 x24 x22

86 40 40 86 40 86 86 40 40 86 86 40 86 40 40 86 63

1.863 1.775 1.719 1.749 1.688 1.831 1.757 1.573 1.911 2.031 1.770 1.632 2.028 1.858 1.560 1.741 1.598

2.316 2.317 2.314 2.314 2.319 2.316 2.314 2.313 2.320 2.318 2.315 2.316 2.318 2.320 2.315 2.315 2.317

99.2629 99.2629 99.2629 99.2629 99.2629 99.2629 99.2629 99.2630 99.2630 99.2629 99.2629 99.2629 99.2629 99.2633 99.2631 99.2629 99.2629

1.85 128 129 3.10 119 4.78 1.10 130 113 1.34 5.52 122 2.45 116 125 1.66 82

a

x2 ¼ NTB.

 102

 102  107  106

 103  103  10

8

 107

5

SSC ¼ 1:4453  0:022,NTB þ 0:0133,NTB2 þ 0:0174,PW þ 0:0694,PW2 (4) It is important to highlight that the model described above is not phenomenological; the dependence expressed in this equation is a consequence of the statistical analysis. To assess the reliability of fit, the ANOVA (analysis of variance) was applied to the results. The significance of the model was tested at a 95% confidence level. The ANOVA results are presented in Table 5. Since Fcalculated is greater than Ftabulated [37] with the low probability value of model (p-value < 0.05), the fitted model can be considered adequate to describe the response surface of in the range studied. Furthermore, the high value of the determination coefficient R2 (0.9569) indicates that the model fits the observed data properly. Since the model proved to be feasible, it is possible to construct contour plots for the response SSC, as shown in Fig. 2. 6.2. Analysis of the process under optimum conditions Analysis of the contour plots allows determination of the optimum conditions of the system studied. Thus, by observing Fig. 2, it was possible to determine that the lowest SSC is reached when column B contains 43 to 47 trays and when w1 represents 57e66% of the wine fed in the process. Since the fitted model shown in Eq. (4) provides a good approximation to the simulation results, the given model was employed to find values of SSC for all possible combinations between NTB and PW (considering only integers of PW), obtaining results between 1.435 and 1.443 kg steam/L hydrous ethanol. Thus, investing in a large number of trays in the column is not justified by the slight reduction in SSC. Therefore, the use of 44 trays in column B was adopted, as this is the usual number of trays used in Brazilian industrial distilleries. In order to determine the best proportion of wine, a series of simulations with NTB ¼ 44 was carried out, varying PW from 57 to 66% (again, only integers). Additionally, to confirm the evaluation done with Statistica software, an extra simulation was done with PW ¼ 67%. The results are shown in Fig. 3. Thus, it was determined that the (NTB, PW) pair that produces the best result is (44, 66). A simulation was carried out at the optimum point in order to confirm the result generated by the experimental design as well as to analyze the responses obtained under these conditions, such as the quality of the alcohol produced. Table 6 contains the results obtained in this simulation. As expected, the ethanol recovery and purification factor were the same in all simulations of the fractional factorial design, as shown in Table 2. Furthermore, it can be seen that the simulation under optimum conditions showed SSC as predicted by the experimental design (SSC < 1.44), which represents a steam consumption approximately 33% lower than that in the conventional process [20]. However, it is noteworthy that this value is 17% higher than that found by Marquini [21], who studied the same configuration in order to separate the ethanol/water binary mixture,

Table 3 Factors’ coded levels used in the CCD. Factor

Levels a

1

0

þ1

þa

x2 x5

36 40

38 46.7

42 63

46 79.3

48 86

Number of trays in column B (NTB) Proportion of wine1  wine2 (PW)

6

L.C.B.A. Bessa et al. / Energy 63 (2013) 1e9

Table 4 Experimental design of CCD runs for the specific steam consumption. Run

NTB

PW

SSC

1 2 3 4 5 6 7 8 9

38 46 38 46 42 36 48 42 42

46.7 46.7 79.3 79.3 63 63 63 40 86

1.5345 1.5058 1.5512 1.5225 1.4452 1.5179 1.4316 1.5453 1.6200

indicating that the minor components have a negative effect on steam consumption. Kiss et al. [38] provide a comprehensive review of the literature on energy efficient technology schemes for multi-component and binary distillation columns. According to them, the technology should provide an energy savings of about 20e50% to be considered for future application. Thus, the configuration studied in the present work is in good agreement with the literature. The temperature profiles in columns A (Fig. 4a) and BB1 (Fig. 4b) are shown in Fig. 4. It can be seen that the temperature at the top of column A reaches about 96  C and the temperature at the bottom of column B1 reaches 82  C. Therefore, the integration of the column B1 reboiler and the column A condenser is possible, assuring a reduction in steam consumption in distillation. In addition, it can be observed a significant decrease in temperature in the first stage of the column due to the presence of carbon dioxide, as already mentioned, obtaining hydrous ethanol at approximately 35  C, as shown in Fig. 4b. Hence, on warmer days cooling water must be available at a lower temperature. The top temperature can be increased by means of a larger degassing stream. Thus, an alternative simulation was carried out where the vapor fraction in the distillate (partial condensation) was increased. The uncondensed distillate with a relatively large ethanol content is conducted into a wash column, which has 10 trays, in order to recover the ethanol. The vapor distillate is fed into the bottom of this column and moves up the column in countercurrent flow with cold water (25  C, 1 atm) fed into the first stage of the column. Two products are obtained from this operation: the degassing stream, composed mainly of carbon dioxide, which is released into the atmosphere, and the bottom product, containing the recovered ethanol, which returns to column BB1 along with the rich wine. The greater the vapor fraction in the condenser, the more water is needed in the wash column in order to minimize the ethanol loss in the degassing stream. Since most of the cold water goes to the bottom of the column and returns to the distillation column, the more water added to the process, the more steam is necessary to concentrate the ethanol up to 93 wt%. Thus, a vapor fraction of 9% of the total distillate was adopted, and the results of this simulation are shown in Table 7. It is worth noting that in this case the total distillate rate had to be increased to 18,988 kg/h, as the rich wine mass flow was higher, explaining why the SSC value was the same

Fig. 2. Contour plots of specific steam consumption.

even though more steam was introduced into the column. In addition, a purification factor that was almost four times higher can be observed, since more CO2 was released into the atmosphere. The new temperature profile in column BB1 is shown in Fig. 5. It can be seen that the temperature at the top of column B1 was significantly higher than in the first heat-integrated configuration, reaching almost 47  C. 6.3. Total annual cost The economic results for the three arrangements are presented in Table 8. The calculations for the conventional configuration were done from data taken from Bessa et al. [20]. It is interesting to note that, although the column BB1 diameter must be larger in the heatintegrated configurations, the opposite occurs with column A. Since the wine stream in those configurations is divided into two parts, the mass flow of wine feeding column A is lower than in the conventional scheme, resulting in smaller diameters. It can be seen that both heat-integrated configurations require higher capital investments than the conventional process, but this increase is only 19.2% in the scheme using the wash column, while it is 24.9% in the case without the wash column. On the other hand, due to the reduction in steam consumption, the heat-integrated schemes showed savings of 30.9 and 32.4% in operating costs for the designs with and without the wash column, respectively. Furthermore, it can be observed that the configuration with the wash column requires more water, since in addition to the cooling

Table 5 ANOVA for the response SSC. Source of variance Sum of squares Degrees Mean square Fcalc of freedom Model Residual Total Ftab

(5;3)

¼ 5.41 [37].

0.0244 0.0011 0.0255

5 3 8

0.004871 0.000366

p-value

13.314 0.02923

Fig. 3. Steam consumption for NTB ¼ 44.

L.C.B.A. Bessa et al. / Energy 63 (2013) 1e9 Table 6 Results at the optimum condition. Response

Value

Steam mass flow e Column A (kg/h) 19,909 Steam mass flow e Column B1 (kg/h) 11,090 Specific steam consumption (SSC) (kg steam/L hydrous ethanol) 1.431 Ethanol content e distillate (%wt) 0.930 Ethanol content e phlegmasse (mg/kg) 59.06 Ethanol recovery (ER) (%) 99.26 P wm,Pa 9.27  103 Purification factor (PF) 2.32 a P wm,P is the sum of mass fractions of all minor compounds in the final product (hydrous ethanol).

water necessary in the top condenser of column BB1, the water fed into the wash column was also considered. However, this scheme has the lowest total annual cost. The TAC of the double-effect integration with wash column is 7.9% less than that of the conventional design, while the configuration without wash column has a TAC savings of 6.1%. Quintero et al. [39] compared two processes for fuel ethanol production utilizing different feedstocks: sugarcane and corn. They aimed to determine which offers the best performance under Colombian conditions from an economic and environmental point of view. The economic analysis was carried out using the Aspen Icarus Process Evaluator package. In the case of ethanol production from sugarcane, a total capital cost of around $7.6  107 and total operating costs of approximately $3.6  107/year were obtained. It is important to note that those authors studied the whole ethanol production process, taking into account not only the distillation step, but also the feedstock pretreatment, fermentation and cogeneration system. Emtir et al. [34] performed a comparative design of energyintegrated distillation schemes in order to separate a ternary mixture of ethanol, n-propanol and n-butanol, using HYSYS software. They investigated two conventional and five energyintegrated schemes, including a forward heat integration scheme similar to the one studied in the present work. They achieved a total annual cost of $5.47  105/year for the integrated scheme, which was 16% less than that for the conventional one. Nevertheless, if the capital and operating costs are analyzed separately, those authors found savings of 3% and 18% for capital and operating costs, respectively. Since they kept the lower pressure column at atmospheric pressure and employed a feed rate of 100 kmol/h, the column diameters were much smaller than those found in this work, which can explain the lower values of TAC as well as the small increase in capital cost. In terms of operating cost, the present work obtained a higher saving.

7

According to von Sivers et al. [40], the cost of distillation constitutes almost 30% of the total operating cost in ethanol production. In addition, Bohlmann [41] presented some economic considerations for the process of ethanol production, in which it can be observed that distillation accounts for 13% of the total capital investment. However, both of the aforementioned studies addressed the production of ethanol from lignocellulosic biomass feedstocks. The production of ethanol by hydrolysis is more difficult and costly than the conventional techniques based on the usual feedstocks [42]. One of the main reasons for the high cost of biomass conversion is the biomass conditioning and pretreatment requirement [41]. Dias et al. [43] studied second generation ethanol production, comparing it with conventional first generation ethanol production from sugarcane in five different scenarios. They verified that the investment required in the current hydrolysis technology scenario is about 28% larger than that in the first generation plant. If this were taken into account, the cost of the distillation step would be roughly 17% of the total capital cost. In this case, the total annual cost of a complete bioethanol plant using the distillation configuration studied in the present work would be around $3.63  107/ year. Considering a production of 21.7 m3/h of hydrous ethanol, the ethanol production costs could be estimated as $0.37/L, $0.34/L and $0.33/L for the conventional, double-effect without wash column and double-effect with wash column designs, respectively, all of which are within the range of $0.25 to 0.40/L found in the literature [4,42,44].

7. Conclusions An alternative double-effect integration of bioethanol distillation was investigated by simulation, taking into account 20 minor components of the sugarcane wine as well as the fusel oil extraction and degassing process. In this suggested configuration, the development of fouling and its harmful effects are reduced owing to the mitigation of the insolubility of calcium salts in column A. The reliability of the interaction parameters used in the phase equilibrium description has already been evaluated against experimental equilibrium data and also through experimental validation with samples from industrial mills in operation. The effects of the number of trays in column A, column B and column B1; the tray for fusel oil removal and the proportion of w1 to w2 were appraised using RSM based on a CCD. The variables number of trays in column B and the proportion of wine showed the greatest influence on specific steam consumption. The response evaluated from the quadratic model shows that the predicted values were in good agreement with the simulated ones.

Fig. 4. Temperature profile in (a) column A and in (b) column BB1.

8

L.C.B.A. Bessa et al. / Energy 63 (2013) 1e9

latter. Finally, the results suggest that the scheme using the wash column may be more attractive in terms of total annual costs.

Table 7 Results of the double-effect integration with wash column. Response

Value

Steam mass flow e Column A (kg/h) 19,909 Steam mass flow e Column B1 (kg/h) 11,601 Specific steam consumption (SSC) (kg steam/L hydrous ethanol) 1.431 Ethanol content e distillate (%wt) 0.930 Ethanol content e phlegmasse (mg/kg) 198.49 Ethanol recovery (ER) (%) 99.15 P wm,Pa 2.34  103 Purification factor (PF) 9.19 a P wm,P is the sum of mass fractions of all minor compounds in the final product (hydrous ethanol).

A second simulation was carried out in order to increase the temperature of the top product by controlling the partial condensation of the distillate. An economic evaluation of these column structures was performed to ensure their advantage over conventional schemes. Both heat-integrated configurations promoted significant energy savings, producing hydrous ethanol within the specifications. Furthermore, they have an impact on both capital and operating costs. Capital costs were 19.2% and 24.9% higher in the configurations with and without wash column, respectively. However, these increases were lower than the reductions in utility costs, which were 30.9% in the former design and 32.4% in the

Fig. 5. Temperature profile in column BB1 in the configuration using a wash column.

Table 8 Calculation of capital and operating costs for each scheme.

Diameter (m) Column A Column BB1 Wash column Capital cost ($/year) Column A Column BB1 Wash column Cooling water system Heat exchanger Operating cost ($/year) Steam Cooling water TAC ($/year) Capital cost saving (%) Operating cost saving (%) TAC saving (%)

Conventional

Double-effect

Doubleeffect þ wash column

2.8 3.8 e

2.2 4.2 e

2.2 4.1 0.7

3.45  105 3.38  106 e e e

2.43 3.99 e 1.66 4.03

3.84  106 5.52  105 8.11  106 0 0 0

2.44  106 5.31  105 7.62  106 24.9 32.4 6.1

 105  106  104  105

2.43 3.78 1.64 e 4.03

 105  106  104  105

2.48  106 5.59  105 7.47  106 19.2 30.9 7.9

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