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Simulation of Distillation Process in the Bioethanol Production Using Nonequilibrium Stage Model
10th International Symposium on Process Systems Engineering - PSE2009 Rita Maria de Brito Alves, Claudio Augusto Oller do Nascimento and Evaristo Chalbaud Biscaia Jr. (Editors) © 2009 Elsevier B.V. All rights reserved.
735
Simulation of Distillation Process in the Bioethanol Production Using Nonequilibrium Stage Model Tassia L. Junqueira, a Rubens Maciel Filho, a Maria R. W. Maciel a a
School of Chemical Engineering, State University of Campinas – UNICAMP, P.O. Box 6066, Campinas, SP 13083-970, Brazil
Abstract Most models available for simulation of multicomponent separation processes are based on the idealized concept of equilibrium or theoretical stages. However, accuracy of the predictions can be highly enhanced if a nonequilibrium stage model is taken into account. In this work, hydrous bioethanol production process was simulated using Aspen Plus® considering three calculation methods: equilibrium, equilibrium with constant plate efficiency and nonequilibrium stage models. Comparison of nonequilibrium and equilibrium model simulations, assuming the same number of stages, showed that energy consumption calculated is much larger when nonequilibrium model is considered and that column specifications have to be adapted in order to achieve bioethanol specification. Equilibrium model with efficiency of 70% presented a satisfactory agreement with nonequilibrium model. Keywords: simulation, bioethanol, nonequilibrium model, distillation process
1. Introduction Bioethanol has been increasingly used as substitute or additive to gasoline, since it is a renewable fuel and its combustion discharges less greenhouse gases when compared to fossil-derived fuels. In Brazil, second largest producer in the world, it is typically produced through fermentation of sugars derived from sugarcane. In order to be used as a fuel, wine obtained from fermentation must be concentrated to about 93 wt% of ethanol (hydrous bioethanol), which requires distillation process. Besides, for bioethanol being used as gasoline additive, further processes are necessary, since water and ethanol form an azeotrope with 95.6 wt% ethanol at 1 atm. Thus, conventional distillation can not achieve anhydrous bioethanol specification (approximately 99.5 wt%). Azeotropic distillation with cyclohexane as entrainer, extractive distillation with monoethyleneglycol as solvent and adsorption onto molecular sieves are possible and usual alternatives for bioethanol dehydration. Usually, simulations of distillation process consider the equilibrium stage model, although, in practice, columns rarely operate under thermodynamic equilibrium conditions. In 1985, Krishnamurty and Taylor described the nonequilibrium stage model of multicomponent separation processes and observed that equilibrium and nonequilibrium model provided results quite different from each other. In the nonequilibrium model, conservation equations are written for each phase independently and solved together with transport equations that describe mass and energy transfers in multicomponent mixtures; also it is assumed that equilibrium occurs only in the vaporliquid interface. Besides, in this way, the empirical correcting factors, such as efficiencies used in the equilibrium model, are no longer necessary (Pescarini, 1996).
736
T.L. Junqueira et al.
Several works have been developed regarding comparison between nonequilibrium stage model and experiments. For instance, Springer et al. (2002) realized experiments in a bubble cap distillation column with the system water – ethanol – methylacetate and also developed the nonequilibrium model, their comparison showed excellent agreement between the results. In adition, Eckert & VanƟk (2001) modelled three-phase distillation columns for ethanol dehydration with cyclohexane and observed that nonequilibrium model provided results in good agreement with experiments. Repke et al. (2004) carried out the validation of the developed nonequilibrium model in a pilot packed column and concluded that nonequilibrium model describes the experimental data with a good accuracy. In view of the fact that distillation operations require a significant amount of energy and have a great importance in bioethanol production, the simulation of this unit operation has to be as representative as possible. In this work, simulations of distillation process in bioethanol production were carried out in Aspen Plus®, considering equilibrium model, equilibrium model with constant plate efficiency and nonequilibrium model.
2. Traditional distillation process The distillation process simulated was based on the traditional Brazilian biorefineries configuration employed to produce hydrous bioethanol. Distillation takes place in a set of five columns, divided into distillation (A, A1 and D, each one at the top of each other) and rectification columns (B and B1). Columns B and B1 are located one above the other and have the same diameter, so they were simulated as a single column (46 stages). Since columns A (19 stages), A1 (8 stages) and D (6 stages) have different diameters, they were simulated detached. This configuration is depicted in Figure 1.
Figure 1. Configuration of the distillation process.
Wine, produced in the fermentation stage, is fed to column A1. Column D is responsible for removing volatile contaminants at the top, while a large amount of water (vinasse) is obtained in the bottom of column A. Vapour phlegm produced near to the top of column A and liquid phlegm obtained at the bottom of column D are sent to rectification columns. The task of the rectification consists on the concentration of the phlegms to 93 wt% ethanol (hydrous bioethanol). Phlegmasse, which has high contents of water, is produced in the bottom of column B-B1. Pressure drop in the columns was considered,
Simulation of Distillation Process in the Bioethanol Production Using Nonequilibrium Stage Model
737
top/bottom pressures (kPa) adopted were 139.3/152.5, 136.3/139.3, 133.8/136.3 and 116.0/135.7 in columns A, A1, D and B, respectively.
3. Simulation procedure Thermodynamic models adequacy to represent the system was evaluated by comparison between vapor-liquid equilibrium results given by Aspen Plus® and available experimental data (Gmehling and Onken, 1977). Aspen Plus® simulator supplies binary parameters from its databank and uses them automatically. This study analyzed NRTL and UNIQUAC activity coefficient models, PSRK equation of state and the UNIFAC method. NRTL was the model that provided better predictions, for this reason it was used to calculate the activity coefficients for the liquid phase. Wine compositions, flow rate and temperature, given in Table 1, were based on biorefinery data. This data showed that other components, such as acetaldehyde, acetone, acetal, n-propanol, isobutanol, n-butanol and acetic acid, are also present in the wine, however they represent less than 0.1% in the composition and were disregarded in this work. Table 1. Specification of wine fed to distillation process. Variable
Value
Water (wt%)
92.0
Ethanol (wt%)
7.3
Glycerol (wt%)
0.4
Isoamyl alcohol (wt%)
0.2
Glucose (wt%)
0.1
Total mass flow rate (t/h)
206.9
Temperature (°C)
81.0
PHLEGMA-L and PHLEGMA-V inlet stages were the only feed positions that could be varied, since the other streams are fed to the first or last stage to substitute a missing condenser or reboiler, respectively. Therefore, optimization process was carried out in order to determine the best inlet stages concerning energy consumption. Three different approaches were used in the simulation of this distillation process regarding to column calculations: equilibrium model (EQ), which assumes as efficiency of 100%, equilibrium model with constant plate efficiency (EQ55, EQ70 and EQ85) and nonequilibrium model (NEQ). All simulations considered condenser and reboiler as an equilibrium stage. In order to perform equilibrium model simulations, RadFrac model in Aspen Plus® simulator is used and its specification is quite simple, since number of stages and feed inlet positions are the only column design parameters required. In the equilibrium model with constant plate efficiency, RadFrac model is also utilized and a additional especification is introduced: plate efficiency. Therefore, it was specified that all stages have the same Murphree efficiency of 55, 70 and 85% in simulations EQ55, EQ70 and EQ85, respectively. In the nonequilibrium model, which is a more rigorous calculation, software Aspen Plus® makes available RateFrac model. This model calculates the product of the binary mass transfer coefficients and interfacial areas using the correlations developed by Grester et al.; the vapor phase and liquid phase heat transfer coefficients are determined
738
T.L. Junqueira et al.
using the Chilton-Colburn analogy. In general, these quantities depend on column diameter and operating parameters. Tray type was defined as bubble cap and column diameter was calculated by the simulator, which sizes the column based on the approach to flooding on the stage where it is most critical. Parameters used in calculations, such as vapor and liquid flows, densities, viscosities, surface tension of liquid, vapor and liquid phase binary diffusion coefficients, are all estimated by the simulator.
4. Simulation results and discussion In order to obtain hydrous bioethanol (93 wt% ethanol), distillate rate on column A and vapor fraction on column B reboiler were manipulated. Equilibrium and nonequilibrium models had different values on these variables, since results using same specification are significantly different. Equilibrium with plate efficiency and nonequilibrium models assumed same values, since the purpose was to determine the efficiency value that most approximates nonequilibrium model predicitions. Temperature profile in column B-B1 was analyzed aiming comparison between results given by the models. This column was chosen for this study because it has larger number of stages, thus it is the most influenced column in this process. 105
Temperature (°C)
100
95
EQ EQ85 EQ70 EQ55 NEQ - interface NEQ - liquid NEQ - vapor
90
85
80 1
6
11
16
21 26 Stage
31
36
41
46
Figure 2. Temperature profiles for column B-B1.
Temperature profiles, depicted in Figure 2, showed that EQ results deviate considerably from the curves given by NEQ. Liquid and interface temperature profiles obtained in the NEQ model were coincident, indicating that there is no resistance to energy transfer between interface and liquid phase. However, vapor temperatures calculated by NEQ slightly diverged from the other temperatures, which means that vapor phase controls energy transfer in the system. Besides, analysing temperatures given by equilibrium model with constant plate efficiency (EQ55, EQ70 and EQ85), it can be observed that temperatures are higher for the superior efficiency (85%), since it means that system behavior is closer to ideal. Among simulations considering plate efficiency, taking NEQ simulation as base, EQ70 predicted better results since its curve is between vapor and liquid temperature profiles calculated by NEQ, which is an indicative that the value 70% is reasonable for efficiency in this process.
Simulation of Distillation Process in the Bioethanol Production Using Nonequilibrium Stage Model
739
Main stream results for EQ, NEQ and EQ70 are displayed on Tables 2-4. It was verified that, in all simulations, hydrous bioethanol production was 16,100 kg/h and its concentration was around 93 wt% ethanol. Other streams also showed similar flow and composition in the simulations. It was also observed that ethanol losses on vinasse and phlegmasse are not significant in all simulations. Table 2. Stream results for equilibrium stage model. Stream
VINASSE PHLEGMAS ALCOHOL2 PRODUCT
Temperature (°C)
111.9
106.0
30.0
81.7
Mass Flow (kg/hr)
176,931
13,110
0.133
16,100
Water (wt%)
99.4
98.8
8.1
7.0
Glucose (wt%)
0.1
0.0
0.0
0.0
Ethanol (wt%)
0.0
0.1
91.9
93.0
Glycerol (wt%)
0.5
0.0
0.0
0.0
Isoamyl alcohol (wt%)
0.0
1.1
0.0
0.0
Table 3. Stream results for equilibrium stage model with efficiency. Stream
VINASSE PHLEGMAS ALCOHOL2 PRODUCT
Temperature (°C)
111.9
106.3
30.0
81.7
Mass Flow (kg/hr)
175,419
14,622
0.133
16,100
Water (wt%)
99.4
99.0
10.6
6.9
Glucose (wt%)
0.1
0.0
0.0
0.0
Ethanol (wt%)
0.0
0.0
89.4
93.1
Glycerol (wt%)
0.5
0.0
0.0
0.0
Isoamyl alcohol (wt%)
0.0
1.0
0.0
0.0
Table 4. Stream results for nonequilibrium stage model. Stream
VINASSE PHLEGMAS ALCOHOL2 PRODUCT
Temperature (°C)
111.9
106.0
30.0
81.7
Mass Flow (kg/hr)
175,347
14,693
0.133
16,100
Water (wt%)
99.4
98.8
14.9
7.0
Glucose (wt%)
0.1
0.0
0.0
0.0
Ethanol (wt%)
0.0
0.0
84.6
93.0
Glycerol (wt%)
0.5
0.0
0.0
0.0
Isoamyl alcohol (wt%)
0.0
1.1
0.5
0.0
Energy demand on columns reboilers for each simulation is shown in Table 5. It can be seen that equilibrium model calculates lower energy consumption when compared with
740
T.L. Junqueira et al.
other calculation methods, because it assumes that vapor and liquid phases leave stages in equilibrium. Once mass and energy transfers or efficiency are considered, as in NEQ and EQ70 simulations, more stages and energy are required to achieve the same separation. It was also observed that energy demand was similar in EQ70 and NEQ, so 70% may be a good estimate for total efficiency in this process. In addition, values displayed in Table 5 indicate that the reboiler of column A required larger amount of energy than column B-B1, since column A operates with larger flows. Table 5. Energy demand on hydrous bioethanol production. Model
EQ
EQ70
NEQ
Column A reboiler (kJ/kg Product)
4503
4712
4723
Column B-B1 reboiler (kJ/kg Product)
1150
2252
2259
Total energy consumption (kJ/kg Product) 5653
6964
6982
5. Conclusions Simulations of bioethanol production process were carried out considering equilibrium, equilibrium with plate efficiency and nonequilibrium models. Regarding to energy consumption and temperature profiles, equilibrium model gave different predictions from the other models studied. Equilibrium model with efficiency of 70% showed a satisfactory agreement with results obtained with nonequilibrium model, which is a rigorous calculation. It was also observed, from temperature profiles, that in the nonequilibrium model, liquid phase presents no resistance to energy transfer and vapor phase controls this process. In conclusion, nonequilibrium model simulation revealed to be a useful tool to predict column performance and to provide more accurate results. Literature review showed that nonequilibrium model predicts results in good agreement with experimental results, however, further studies including experiments are recommended to validate nonequilibrium model for bioethanol production process.
6. Acknowledgments The authors acknowledge FAPESP and CNPq for the financial support.
References E. Eckert, T. VanƟk, 2001, Some aspects of rate-based modelling and simulation of three-phase distillation columns, Computers & Chemical Engineering, 25, 603–612 J. Gmehling, U. Onken, 1977, Vapor-Liquid Equilibrium Data Collection – DECHEMA Chemistry Data Series R. Krishnamurthy, R. Taylor, 1985, A nonequilibrium stage model of multicomponent separation process – Part I: Model description and method of solution, AIChE Journal, 31, 3, 449-456 M. H. Pescarini, A. A. C. Barros, M. R. Wolf-Maciel, 1996, Development of a software for simulating separation processes using a nonequilibrium stage model, Computers & chemical engineering, 20, SUPA, 279-284 J. U. Repke, O. Villain, G. Wozny, 2004, A nonequilibrium model for three-phase distillation in a packed column: modelling and experiments, Computers & Chemical Engineering,28, 775–780 P.A.M. Springer, S. van der Molen, R. Krishna, 2002, The need for using rigorous rate-based models for simulations of ternary azeotropic distillation, Computers & Chemical Engineering, 26, 1265–1279
735
Simulation of Distillation Process in the Bioethanol Production Using Nonequilibrium Stage Model Tassia L. Junqueira, a Rubens Maciel Filho, a Maria R. W. Maciel a a
School of Chemical Engineering, State University of Campinas – UNICAMP, P.O. Box 6066, Campinas, SP 13083-970, Brazil
Abstract Most models available for simulation of multicomponent separation processes are based on the idealized concept of equilibrium or theoretical stages. However, accuracy of the predictions can be highly enhanced if a nonequilibrium stage model is taken into account. In this work, hydrous bioethanol production process was simulated using Aspen Plus® considering three calculation methods: equilibrium, equilibrium with constant plate efficiency and nonequilibrium stage models. Comparison of nonequilibrium and equilibrium model simulations, assuming the same number of stages, showed that energy consumption calculated is much larger when nonequilibrium model is considered and that column specifications have to be adapted in order to achieve bioethanol specification. Equilibrium model with efficiency of 70% presented a satisfactory agreement with nonequilibrium model. Keywords: simulation, bioethanol, nonequilibrium model, distillation process
1. Introduction Bioethanol has been increasingly used as substitute or additive to gasoline, since it is a renewable fuel and its combustion discharges less greenhouse gases when compared to fossil-derived fuels. In Brazil, second largest producer in the world, it is typically produced through fermentation of sugars derived from sugarcane. In order to be used as a fuel, wine obtained from fermentation must be concentrated to about 93 wt% of ethanol (hydrous bioethanol), which requires distillation process. Besides, for bioethanol being used as gasoline additive, further processes are necessary, since water and ethanol form an azeotrope with 95.6 wt% ethanol at 1 atm. Thus, conventional distillation can not achieve anhydrous bioethanol specification (approximately 99.5 wt%). Azeotropic distillation with cyclohexane as entrainer, extractive distillation with monoethyleneglycol as solvent and adsorption onto molecular sieves are possible and usual alternatives for bioethanol dehydration. Usually, simulations of distillation process consider the equilibrium stage model, although, in practice, columns rarely operate under thermodynamic equilibrium conditions. In 1985, Krishnamurty and Taylor described the nonequilibrium stage model of multicomponent separation processes and observed that equilibrium and nonequilibrium model provided results quite different from each other. In the nonequilibrium model, conservation equations are written for each phase independently and solved together with transport equations that describe mass and energy transfers in multicomponent mixtures; also it is assumed that equilibrium occurs only in the vaporliquid interface. Besides, in this way, the empirical correcting factors, such as efficiencies used in the equilibrium model, are no longer necessary (Pescarini, 1996).
736
T.L. Junqueira et al.
Several works have been developed regarding comparison between nonequilibrium stage model and experiments. For instance, Springer et al. (2002) realized experiments in a bubble cap distillation column with the system water – ethanol – methylacetate and also developed the nonequilibrium model, their comparison showed excellent agreement between the results. In adition, Eckert & VanƟk (2001) modelled three-phase distillation columns for ethanol dehydration with cyclohexane and observed that nonequilibrium model provided results in good agreement with experiments. Repke et al. (2004) carried out the validation of the developed nonequilibrium model in a pilot packed column and concluded that nonequilibrium model describes the experimental data with a good accuracy. In view of the fact that distillation operations require a significant amount of energy and have a great importance in bioethanol production, the simulation of this unit operation has to be as representative as possible. In this work, simulations of distillation process in bioethanol production were carried out in Aspen Plus®, considering equilibrium model, equilibrium model with constant plate efficiency and nonequilibrium model.
2. Traditional distillation process The distillation process simulated was based on the traditional Brazilian biorefineries configuration employed to produce hydrous bioethanol. Distillation takes place in a set of five columns, divided into distillation (A, A1 and D, each one at the top of each other) and rectification columns (B and B1). Columns B and B1 are located one above the other and have the same diameter, so they were simulated as a single column (46 stages). Since columns A (19 stages), A1 (8 stages) and D (6 stages) have different diameters, they were simulated detached. This configuration is depicted in Figure 1.
Figure 1. Configuration of the distillation process.
Wine, produced in the fermentation stage, is fed to column A1. Column D is responsible for removing volatile contaminants at the top, while a large amount of water (vinasse) is obtained in the bottom of column A. Vapour phlegm produced near to the top of column A and liquid phlegm obtained at the bottom of column D are sent to rectification columns. The task of the rectification consists on the concentration of the phlegms to 93 wt% ethanol (hydrous bioethanol). Phlegmasse, which has high contents of water, is produced in the bottom of column B-B1. Pressure drop in the columns was considered,
Simulation of Distillation Process in the Bioethanol Production Using Nonequilibrium Stage Model
737
top/bottom pressures (kPa) adopted were 139.3/152.5, 136.3/139.3, 133.8/136.3 and 116.0/135.7 in columns A, A1, D and B, respectively.
3. Simulation procedure Thermodynamic models adequacy to represent the system was evaluated by comparison between vapor-liquid equilibrium results given by Aspen Plus® and available experimental data (Gmehling and Onken, 1977). Aspen Plus® simulator supplies binary parameters from its databank and uses them automatically. This study analyzed NRTL and UNIQUAC activity coefficient models, PSRK equation of state and the UNIFAC method. NRTL was the model that provided better predictions, for this reason it was used to calculate the activity coefficients for the liquid phase. Wine compositions, flow rate and temperature, given in Table 1, were based on biorefinery data. This data showed that other components, such as acetaldehyde, acetone, acetal, n-propanol, isobutanol, n-butanol and acetic acid, are also present in the wine, however they represent less than 0.1% in the composition and were disregarded in this work. Table 1. Specification of wine fed to distillation process. Variable
Value
Water (wt%)
92.0
Ethanol (wt%)
7.3
Glycerol (wt%)
0.4
Isoamyl alcohol (wt%)
0.2
Glucose (wt%)
0.1
Total mass flow rate (t/h)
206.9
Temperature (°C)
81.0
PHLEGMA-L and PHLEGMA-V inlet stages were the only feed positions that could be varied, since the other streams are fed to the first or last stage to substitute a missing condenser or reboiler, respectively. Therefore, optimization process was carried out in order to determine the best inlet stages concerning energy consumption. Three different approaches were used in the simulation of this distillation process regarding to column calculations: equilibrium model (EQ), which assumes as efficiency of 100%, equilibrium model with constant plate efficiency (EQ55, EQ70 and EQ85) and nonequilibrium model (NEQ). All simulations considered condenser and reboiler as an equilibrium stage. In order to perform equilibrium model simulations, RadFrac model in Aspen Plus® simulator is used and its specification is quite simple, since number of stages and feed inlet positions are the only column design parameters required. In the equilibrium model with constant plate efficiency, RadFrac model is also utilized and a additional especification is introduced: plate efficiency. Therefore, it was specified that all stages have the same Murphree efficiency of 55, 70 and 85% in simulations EQ55, EQ70 and EQ85, respectively. In the nonequilibrium model, which is a more rigorous calculation, software Aspen Plus® makes available RateFrac model. This model calculates the product of the binary mass transfer coefficients and interfacial areas using the correlations developed by Grester et al.; the vapor phase and liquid phase heat transfer coefficients are determined
738
T.L. Junqueira et al.
using the Chilton-Colburn analogy. In general, these quantities depend on column diameter and operating parameters. Tray type was defined as bubble cap and column diameter was calculated by the simulator, which sizes the column based on the approach to flooding on the stage where it is most critical. Parameters used in calculations, such as vapor and liquid flows, densities, viscosities, surface tension of liquid, vapor and liquid phase binary diffusion coefficients, are all estimated by the simulator.
4. Simulation results and discussion In order to obtain hydrous bioethanol (93 wt% ethanol), distillate rate on column A and vapor fraction on column B reboiler were manipulated. Equilibrium and nonequilibrium models had different values on these variables, since results using same specification are significantly different. Equilibrium with plate efficiency and nonequilibrium models assumed same values, since the purpose was to determine the efficiency value that most approximates nonequilibrium model predicitions. Temperature profile in column B-B1 was analyzed aiming comparison between results given by the models. This column was chosen for this study because it has larger number of stages, thus it is the most influenced column in this process. 105
Temperature (°C)
100
95
EQ EQ85 EQ70 EQ55 NEQ - interface NEQ - liquid NEQ - vapor
90
85
80 1
6
11
16
21 26 Stage
31
36
41
46
Figure 2. Temperature profiles for column B-B1.
Temperature profiles, depicted in Figure 2, showed that EQ results deviate considerably from the curves given by NEQ. Liquid and interface temperature profiles obtained in the NEQ model were coincident, indicating that there is no resistance to energy transfer between interface and liquid phase. However, vapor temperatures calculated by NEQ slightly diverged from the other temperatures, which means that vapor phase controls energy transfer in the system. Besides, analysing temperatures given by equilibrium model with constant plate efficiency (EQ55, EQ70 and EQ85), it can be observed that temperatures are higher for the superior efficiency (85%), since it means that system behavior is closer to ideal. Among simulations considering plate efficiency, taking NEQ simulation as base, EQ70 predicted better results since its curve is between vapor and liquid temperature profiles calculated by NEQ, which is an indicative that the value 70% is reasonable for efficiency in this process.
Simulation of Distillation Process in the Bioethanol Production Using Nonequilibrium Stage Model
739
Main stream results for EQ, NEQ and EQ70 are displayed on Tables 2-4. It was verified that, in all simulations, hydrous bioethanol production was 16,100 kg/h and its concentration was around 93 wt% ethanol. Other streams also showed similar flow and composition in the simulations. It was also observed that ethanol losses on vinasse and phlegmasse are not significant in all simulations. Table 2. Stream results for equilibrium stage model. Stream
VINASSE PHLEGMAS ALCOHOL2 PRODUCT
Temperature (°C)
111.9
106.0
30.0
81.7
Mass Flow (kg/hr)
176,931
13,110
0.133
16,100
Water (wt%)
99.4
98.8
8.1
7.0
Glucose (wt%)
0.1
0.0
0.0
0.0
Ethanol (wt%)
0.0
0.1
91.9
93.0
Glycerol (wt%)
0.5
0.0
0.0
0.0
Isoamyl alcohol (wt%)
0.0
1.1
0.0
0.0
Table 3. Stream results for equilibrium stage model with efficiency. Stream
VINASSE PHLEGMAS ALCOHOL2 PRODUCT
Temperature (°C)
111.9
106.3
30.0
81.7
Mass Flow (kg/hr)
175,419
14,622
0.133
16,100
Water (wt%)
99.4
99.0
10.6
6.9
Glucose (wt%)
0.1
0.0
0.0
0.0
Ethanol (wt%)
0.0
0.0
89.4
93.1
Glycerol (wt%)
0.5
0.0
0.0
0.0
Isoamyl alcohol (wt%)
0.0
1.0
0.0
0.0
Table 4. Stream results for nonequilibrium stage model. Stream
VINASSE PHLEGMAS ALCOHOL2 PRODUCT
Temperature (°C)
111.9
106.0
30.0
81.7
Mass Flow (kg/hr)
175,347
14,693
0.133
16,100
Water (wt%)
99.4
98.8
14.9
7.0
Glucose (wt%)
0.1
0.0
0.0
0.0
Ethanol (wt%)
0.0
0.0
84.6
93.0
Glycerol (wt%)
0.5
0.0
0.0
0.0
Isoamyl alcohol (wt%)
0.0
1.1
0.5
0.0
Energy demand on columns reboilers for each simulation is shown in Table 5. It can be seen that equilibrium model calculates lower energy consumption when compared with
740
T.L. Junqueira et al.
other calculation methods, because it assumes that vapor and liquid phases leave stages in equilibrium. Once mass and energy transfers or efficiency are considered, as in NEQ and EQ70 simulations, more stages and energy are required to achieve the same separation. It was also observed that energy demand was similar in EQ70 and NEQ, so 70% may be a good estimate for total efficiency in this process. In addition, values displayed in Table 5 indicate that the reboiler of column A required larger amount of energy than column B-B1, since column A operates with larger flows. Table 5. Energy demand on hydrous bioethanol production. Model
EQ
EQ70
NEQ
Column A reboiler (kJ/kg Product)
4503
4712
4723
Column B-B1 reboiler (kJ/kg Product)
1150
2252
2259
Total energy consumption (kJ/kg Product) 5653
6964
6982
5. Conclusions Simulations of bioethanol production process were carried out considering equilibrium, equilibrium with plate efficiency and nonequilibrium models. Regarding to energy consumption and temperature profiles, equilibrium model gave different predictions from the other models studied. Equilibrium model with efficiency of 70% showed a satisfactory agreement with results obtained with nonequilibrium model, which is a rigorous calculation. It was also observed, from temperature profiles, that in the nonequilibrium model, liquid phase presents no resistance to energy transfer and vapor phase controls this process. In conclusion, nonequilibrium model simulation revealed to be a useful tool to predict column performance and to provide more accurate results. Literature review showed that nonequilibrium model predicts results in good agreement with experimental results, however, further studies including experiments are recommended to validate nonequilibrium model for bioethanol production process.
6. Acknowledgments The authors acknowledge FAPESP and CNPq for the financial support.
References E. Eckert, T. VanƟk, 2001, Some aspects of rate-based modelling and simulation of three-phase distillation columns, Computers & Chemical Engineering, 25, 603–612 J. Gmehling, U. Onken, 1977, Vapor-Liquid Equilibrium Data Collection – DECHEMA Chemistry Data Series R. Krishnamurthy, R. Taylor, 1985, A nonequilibrium stage model of multicomponent separation process – Part I: Model description and method of solution, AIChE Journal, 31, 3, 449-456 M. H. Pescarini, A. A. C. Barros, M. R. Wolf-Maciel, 1996, Development of a software for simulating separation processes using a nonequilibrium stage model, Computers & chemical engineering, 20, SUPA, 279-284 J. U. Repke, O. Villain, G. Wozny, 2004, A nonequilibrium model for three-phase distillation in a packed column: modelling and experiments, Computers & Chemical Engineering,28, 775–780 P.A.M. Springer, S. van der Molen, R. Krishna, 2002, The need for using rigorous rate-based models for simulations of ternary azeotropic distillation, Computers & Chemical Engineering, 26, 1265–1279