Minimizing Costs in Near-Critical Bioethanol Extraction and

Article pubs.acs.org/EF

Minimizing Costs in Near-Critical Bioethanol Extraction and Dehydration Processes Cecilia I. Paulo, M. Soledad Diaz, and Esteban A. Brignole* Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur-CONICET, Camino de La Carrindanga Km 7, Bahía Blanca 8000, Argentina ABSTRACT: In the present work, we propose a model-based cost optimization for three capacities of bioethanol extraction− dehydration plants with supercritical propane. We consider alternative integration schemes between process streams as different nonlinear programming problems (NLP) for energy minimization. In particular, we analyze integration between a heat pump option for the top vapor of the first distillation column with the beer column reboiler, which can provide reduction in total energy consumption, as well as lower final product cost. Capital and operating costs were evaluated for the analyzed schemes. Thermodynamic predictions are performed with an upgraded group contribution with association equation of state (GCA-EOS). We demonstrate that ethanol dehydration with supercritical propane can be a sustainable alternative that is energetically and economically competitive with other technologies.

1. INTRODUCTION Different alternatives to fossil fuels are currently being studied to reduce the dependence on nonrenewable resources. Biofuels constitute relevant sustainable complements and/or substitutes to petroleum fuels due to energy security reasons, environmental concerns, foreign exchange savings, and socioeconomic issues related to the rural sector. The reduction of greenhouse gases emission is another advantage of using biomass energy. The most common renewable fuel is ethanol, mainly derived from corn grain (starch) and sugar cane (sucrose). During the last years, there has been increasing interest on biomass derived ethanol due to the rapid increase in crude oil prices and the perceived strength of the global demand of petroleum. The production of ethanol from biomass is an option to reduce both consumption of nonrenewable fuels and environmental contamination. Furthermore, ethanol is appropriate for gasoline blends due to its high octane number, its low cetane number, and its high heat of vaporization that impedes self-ignition in the engine.1 The availability of raw material or feedstock for ethanol production, as well as its geographic location, is currently one of the biggest scientific concerns. Nevertheless, wood, straw, agricultural residues, and even household wastes are currently being investigated to be economically converted to ethanol. Much research is being pursued on the use of lignocellulosic biomass as an attractive feedstock for future supplies of ethanol, as it does not compete for land and agricultural market resources with food crops.2,3 However, large-scale commercial production of ethanol from lignocellulosic materials has still not been implemented. Furthermore, there is also considerable effort devoted to obtaining third generation ethanol from algae.4,5 The processes downstream the fermentation step, including ethanol separation and dehydration, are being thoroughly studied as well. As the product of the fermentation process, there is usually a dilute aqueous solution, the so-called “beer”, containing about 5−12 wt % ethanol. Separation of ethanol from beer is an energy-intensive process, which usually takes up © 2012 American Chemical Society

a large fraction of the total energy requirement for the whole biorefinery. Furthermore, there is still a need for decreasing energy consumption in the entire ethanol supply chain to make it economically competitive with fossil fuels. The use of pervaporation membranes has been proposed as an alternative to extractive distillation.6 Hoch and Espinosa compared operating and investment costs, as well as energy consumptions, for two designs of the whole purification plant using different technologies to break the azeotrope between ethanol and water. More recently, Cheng-Lee Lai et al.7 studied asymmetric ionexchange membranes for dehydrating a water/ethanol mixture by pervaporation. Karuppiah et al.8 have proposed different design alternatives for the transformation of corn kernels to fuel-grade ethanol, using distillation together with molecular sieves as a hybrid process. Furthermore, Huang et al.9 suggested a hybrid separation process combining distillation (with vapor compression) and membrane vapor permeation as an alternative to conventional distillation. The potential of near-critical fluid extraction and high pressure distillation for separating alcohols from water has been discussed by several authors.10−16 Basically, this process consists of two steps, the extraction of ethanol from an aqueous solution with a near-critical solvent and the final dehydration and separation from the solvent in a distillation train. A distinct feature of this process is that the high-pressure distillation column is used not only for solvent recovering but also to completely remove water from the alcohol. In this way, anhydrous ethanol can be obtained without additional columns to remove water. The solvents that are suitable for this type of separation process (dehydration−extraction) are called dual effect solvents. They should offer good selectivity for alcohol recovery and also exhibit the water entrainment effect (water−solvent relative volatility greater than one at the solvent recovery column). Received: February 17, 2012 Revised: May 15, 2012 Published: May 22, 2012 3785

dx.doi.org/10.1021/ef3002907 | Energy Fuels 2012, 26, 3785−3795

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The water entrainment effect has been verified experimentally by Zabaloy et al.11 and Horizoe et al.,12 even at pilot plant scale. Moreover, no azeotrope formation between solvent and ethanol is required. Propane (Tc = 369.8 K and Pc= 42.8 bar) is the only dual effect solvent for ethanol extraction and dehydration; propane exhibits the entrainment effect and does not form an azeotrope with ethanol. In the present work, the inclusion of different process schemes has been formulated as alternate nonlinear programming problems (NLP), whose solutions have provided improved energy consumption options, as well as lower economic capital and operating costs. In particular, we analyze energy integration between a heat pump option for the distillation column top vapor with the beer column reboiler, which can provide reduction in total energy consumption, as well as lower operating costs. Section 2 of this work provides a brief description of the thermodynamic model, as well as the most important thermodynamic properties for this process. A ternary graph for propane−ethanol−water under the highpressure extractor conditions is also discussed. Section 3 describes the conventional processes and the different process alternatives studied. In Section 4, the mathematical model is presented, together with the optimization algorithm description. Finally, results and conclusions for the extraction−dehydration process studied are discussed.

Figure 1. Distribution coefficients of ethanol in the system water− ethanol−propane.12  GCA-EOS predictions.34 Pressure = 99 bar. x*ethanol = 0.05.

2. THERMODYNAMIC MODEL Thermodynamic predictions are performed with an upgraded group contribution with association equation of state (GCAEOS) model17,18 that provides reliable phase equilibrium predictions at high pressure in mixtures with association. Group contribution equations of state have been widely used for predicting thermodynamic equilibrium in supercritical fluid processes, as a great variety of natural products can be represented with a limited number of functional groups. The group contribution equation of state (GC-EOS) model was proposed by Skjold-Jorgensen19 to study gas solubilities in non ideal mixtures at high pressures. This model was applied for the prediction and correlation of solubilities of solvents in supercritical fluids by Brignole et al.20 The original model takes into account only repulsive and dispersive interactions. Gros et al.21 extended its capability to treat associating compounds (GCA-EOS) in mixtures of water and alcohols with nonpolar gases, such as propane or carbon dioxide. Three types of energetic contributions are taken into account in this equation: repulsive, attractive, and associative. The repulsive hard sphere and attractive dispersive terms are the same as those in the original GC-EOS model. The third term, takes into account association effects due to hydrogen bonding between the O and H sites of an unique associating OH group. The process extraction and dehydration of ethanol with nearcritical propane is based on the phase equilibrium behavior of the system water−ethanol−propane. In particular, the dominant phase equilibrium properties that determine the conceptual design of the process are the following: • the ethanol distribution coefficient between aqueous and solvent phases at the extractor operating conditions. The distribution coefficients (Figure 1) increase with temperature; therefore, higher extraction temperatures result in lower solvent requirements and equipment size. • the water concentration in the extract; this value limits the maximum extraction temperature.

Figure 2. Water−propane relative volatility in the presence of ethanol. x* is the ethanol concentration in water free of propane.12  GCAEOS predictions.34 Temperature = 363 K. x*ethanol = 0.88.

• the water relative volatility with respect to propane (Figure 2) under the propane recovery column conditions (it should be greater than one to obtain the water entrainment effect, that is, to obtain water and solvent as the top product in the first distillation column and dehydrate ethanol as the bottom product). • the ethanol relative volatility with respect to the propane smaller than one, in the propane recovery column (which determines the feasibility of the separation, no azeotrope formation between solvent and ethanol is required) and, in most cases, this unit energy consumption. • the boiling point of ethanol−propane mixtures. The boiling point increase with ethanol concentration is rather small up to an alcohol concentration of near 50 wt %. This offers several advantages: it gives a low reboiler temperature in the solvent recovery column, it facilitates the heat integration or heat pump alternatives, and it allows the operation of this column at higher pressure to avoid water condensation. 3786


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Figure 3. GCA-EOS ternary equilibrium predictions for the ethanol−water−propane system at 360 K and 55 bar.

propane content, can be recycled to the fermentation plant after water treatment. The extract after pressure reduction is fed to a distillation column. The propane−ethanol separation takes place in this column, and the solvent is recovered. In addition, the complete dehydration of ethanol is obtained by the water entrainment effect of propane. The distillate is returned to the extractor as the supercritical solvent and anhydrous ethanol is obtained as bottoms in the distillation unit. Numerical results of the process simulation strongly depend on the accuracy of the thermodynamic model predictions of propane−ethanol and water−propane relative volatilities and ethanol distribution coefficient in the high-pressure extractor. 3.2. Alternative Process Schemes. Based on the previously described extraction−dehydration scheme, different process alternatives can lead to more efficient and economic designs. Figure 5 shows the proposed superstructure for extraction−dehydration processes, which embeds several schemes with low energy consumption. The most important contribution to minimize process energy consumption is the near-critical solvent recirculation rate. Furthermore, the solvent requirement at given operating conditions is proportional to the aqueous solution feed flow. In this way, a reduction in the feed flow rate to the extractor unit (HPE) is desirable, which is obtained through the inclusion of a beer column. The high ethanol−water relative volatility for dilute mixtures allows the increase of ethanol concentration using a stripping column. So, a preconcentration unit, namely the beer column (BC), reduces the flow rate of the aqueous solution to the extractor and, therefore, supercritical solvent requirements. This column increases ethanol concentration approximately 7× from 4 mol % to 27 mol % for all the schemes analyzed in this work. The addition of column C2 provides complete recovery of solvent (propane) as top stream, with anhydrous ethanol as the bottom product. In the proposed alternative schemes, energy integration is performed between the beer column (BC) and the first distillation column (C1), matching the BC top stream with the reboiler of C1 (Figure 6). However, the stream matches depend on the pre-concentrator unit operating pressure and ethanol concentration in C1 column bottoms. If ethanol concentration is less than 50% molar, the propane−ethanol mixture boiling point increase is small and the integration between the beer column and C1 bottoms is feasible. In the first column (C1), water and propane are removed as top products,

Figure 3 shows GCA-EOS ternary equilibrium predictions obtained for the ethanol−water−propane system at 360 K and 55 bar, which represents optimal temperature and pressure conditions for the case study presented in this work.

3. PROCESS SCHEMES FOR NEAR-CRITICAL EXTRACTION−DEHYDRATION 3.1. Basic Process. Basically, the ethanol extraction− dehydration process with supercritical light hydrocarbons13 is comprised of a high-pressure extractor and a solvent recovery column. The process mainly consists in the extraction of ethanol from a typical fermentation aqueous solution (the so-called beer, containing about 5−12 wt % ethanol), using propane as a nearcritical solvent. The solvent stream enters the extraction column at the bottom, while the ethanol−water mixture is fed countercurrently to the top of the column (Figure 4).

Figure 4. Conventional extraction−dehydration scheme. HPE: highpressure extractor. C1: dehydration column. F: aqueous feed. R: raffinate. S: supercritical solvent. B: anhydrous ethanol. W: condensed water.

The high-pressure extractor is operated at conditions near the critical temperature of propane and at pressure above its critical value. The favorable effect of temperature on the ethanol distribution coefficient is thus obtained. The extract (mainly ethanol and solvent) contains a small amount of water. The raffinate, an aqueous solution with low ethanol and 3787


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Figure 5. Superstructure for extraction−dehydration process. HPE: high-pressure extractor. C1: first distillation column. C2: second solvent recovery column. E: ethanol extract. F: aqueous feed. R: raffinate. BC1−2: anhydrous ethanol. S: solvent. Aq: aqueous stream. BC: pre-concentrator (beer column). C: compressor. T: turbine. M: motor. HP: heat pump. RC: compressor. VC1: recompressed vapor. W: condensed water. DC1−2: solvent feedback. RBBC: beer column reboiler. HE: solvent heater. RBC1: first distillation column reboiler. PF: feed pump. PS1: first solvent pump. PS2: second solvent pump.

Figure 6. Basic energy integration scheme: beer column top stream energetically integrated with the first distillation column reboiler (RBC1) and extract flow is used to preheat the solvent feed to the extractor.

operation of this column at higher pressure gives higher water concentration at saturation, avoiding the formation of a third aqueous phase on the top section of C1. In addition, the second column (C2) can operate at lower pressure with lower reboiler

while an equimolar alcohol−solvent mixture is obtained as bottom product. Ethanol−propane separation is obtained in column C2. This two-column design has numerous advantages: low reboiler temperature in C1 enables heat integration; the 3788


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columns (C1 and C2) is justified. Vapor recompression with heat pump (HP) represents a different alternative scheme for the process. Energy consumption is mainly determined by mechanical work provided to compress C1 overhead vapor, which is, in turn, used as heat source for the column reboiler (RBC1). The required energy in the column condenser is supplied by the condensation of the recompressed vapor (VC1). The use of a middle pressure turbine (T) or an electrical motor (M) as drivers for the compressor (C) has been considered. In the case of a turbine driver scheme, the exhaust steam (1−2 bar) is used to preheat the process feed stream, as well as energy integration between the first distillation column (C1) and the beer column reboiler (RBBC). No external heating services are thus required for the column. The use of vapor recompression is justified from the low temperature difference between top and bottom streams in column C1. This scheme gives low energy consumption for vapor recompression at the expense of additional energy use by conventional heating in the second distillation column (C2).

temperature, which increases energy savings. This two-column scheme also allows the use of a heat pump (HP) in the first distillation column, which provides significant reduction in total energy consumption. In that sense, the use of two distillation Table 1. Design Variables Upper Bounds (UB) and Lower Bound (LB) for Different Ethanol Production 8000 (ton/year) extractor temp. (K) solvent (kmol/h) reflux ratio, RC1 extractor pressure (bar)

80 000 (ton/year)

120 000 (ton/year)

LB

UB

LB

UB

LB

UB

360 50 0.10 50

380 200 0.95 180

360 500 0.40 50

380 1935 0.99 180

356 500 0.60 50

362 3000 0.99 180

Table 2. Inequality Constraints constraint

description

HPE

unit

r1

C1

r2

C1

r3

C1

r4

C1

r5

C1

r6

BC−C1

r7

BC−C1

r8

BC

r9

ethanol loss in reffinate (% molar) water composition in top vapor phase of C1 ethanol composition in top vapor phase of C1 (% molar) ethanol composition in bottom of C1 (solvent free basic, % molar) ethanol recovery in C1 (% molar) temp. in bottom of C1, extractor selectivity (K) beer column top vapor−bottom of C1 temp. difference energy available from beer column top vapor ethanol loss in beer column (% molar)

bound ≤1.5 ≤YH2O(sat)

4. MATHEMATICAL MODEL FOR EXTRACTION−DEHYDRATION PROCESS We have formulated rigorous mathematical models for the extraction−dehydration processes.14 Process units include highpressure multistage extractors,22 low- and high-pressure distillation columns,23 and a multiphase flash.24 Thermodynamic predictions are provided by the GCA-EOS. The analyzed process alternatives have been formulated as a series of nonlinear programming problems (NLP) for the minimization of energy consumption, so as to provide an economically attractive clean technology. Nonlinear programming problems have been implemented in a Fortran 90 environment and solved with a successive quadratic programming algorithm.25 Main optimization variables are the

≤0.1 ≥99.10 ≥98.00 ≤470.15 ≥15.00 ≥0.9 QreboilerC1 ≤0.10

Figure 7. Energy integration scheme HP-M. First distillation column top stream recompressed with heat pump, driver electrical motor, to provide complete RBC1 heat duty. 3789


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Figure 8. Energy integration scheme HP-T. First distillation column top stream is recompressed through heat pump with turbine driver to provide complete RBC1 heat duty. Exhausted steam from turbine integrated with beer column reboiler RBBC.

extraction temperature (TE) and the pressure (PE), solvent flow rate (S), and reflux ratio in first dehydration column (RC1). Variable bounds are shown in Table 1. Operating bounds and process specifications are shown in Table 2. Distillation column C1 is the process unit on which more constraints are imposed: • the composition of water in the top vapor phase of the column must be less than the value corresponding to saturation to avoid condensation at that stage; • two quality constraints are added to obtain fuel-grade ethanol as a bottom product, with high ethanol recovery (the loss in the high pressure extractor raffinate should not exceed 1.5%); • to achieve energy integration between the top stream of the beer column and the reboiler of C1, a temperature difference greater than 15 °C is imposed. • avoidance of three phase formation (L−L−V) in the top of the first distillation column. An additional constrain is imposed on ethanol loss in the beer column, which must be lower than 0.1%. The propane lost in the water stream leaving the extractor is very low, considering that the equilibrium propane concentration in water at the operating conditions is around 0.05 wt %, which has been imposed as a nonlinear constraint for the optimization problem. Energy consumption has been selected as the performance criterion. Therefore, the objective function (eq 1) is composed of several terms corresponding to heating requirement in distillation columns reboilers (HRBC, HRC1, HRC2), pumping energy consumption for the aqueous feed (Hpfed) and solvent recycle (Hpsolv) and energy consumption for the compressor driver (HHP), as well as their integration in the different proposed schemes. Electricity cost is higher than steam cost. To make the summation over the different energy types on a similar cost basis, mechanical energy (kJ/kg) has been affected

Table 3. Optimal Operating Conditions and Energy Consumption for Alternative Processes ethanol production 8000 ton/year HP-M TE (K) 360.0 S (kmol/h) 200.0 reflux C1 0.925 PE (bar) 50.3 integrated energy consumption (kJ/kg) 4063.5 ethanol production 80 000 ton/year HP-M

HP-T

basic

361.0 199.0 0.950 50.6 3738.6

360.0 200.0 0.919 50.5 4224.3

HP-T

basic

TE (K) 360.0 360.0 S (kmol/h) 1935.0 1935.0 reflux C1 0.990 0.990 PE (bar) 56.6 56.5 integrated energy consumption (kJ/kg) 4127.7 3893.6 ethanol production 120 000 ton/year TE (K) S (kmol/h) reflux C1 PE (bar) integrated energy consumption (kJ/kg)

360.0 1935.0 0.990 56.6 4260.9

HP-M

HP-T

basic

358.7 2880.0 0.990 59.8 4132.0

358.7 2880.0 0.990 59.8 3923.7

356.0 2880.0 0.949 65.5 4229.4

by a factor of 3.0,26 so as to convert it in an amount of thermal energy of equivalent cost. f = HRBC + HRC1 + HRC2 + Hpfed + Hpsolv + HHP

(1)

For cost evaluation, we have considered a 10-year project life, a 10% interest rate, 1.49 × 10−5 US$/kJ for the media pressure steam (MPS, 10 bar) for turbine and reboilers, and 0.20 US $/kWh for electricity cost. Capital cost correlations have been taken from Ulrich,27 Peters and Timmerhaus,28 and the 3790


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Figure 9. Energy consumption variation with ethanol production, for HP-T, HP-M, and basic schemes.

Figure 10. Capital cost variation with ethanol production, for HP-T, HP-M, and basic schemes.

Institut Français du Pétrole.29 For cost estimation, we used the Chemical Engineering index (CE = 529.9), updated to 2010.30

integration scheme and the two most attractive process schemes determined in previous work,15,31 which are described as follows: In the basic integration scheme (Figure 6), the beer column top stream is integrated with the first distillation column reboiler (RBC1), providing 90% of the required heat, while the extract stream preheats the solvent feed to the high pressure extractor (HPE). In the second scheme, a heat pump device is considered (Figure 7). An electrical motor is proposed as a driver for the heat pump (HP-M). In this scheme, the first distillation column top vapor is recompressed to provide complete RBC1 heat duty. Figure 8 shows the highly integrated scheme proposed, considering a heat pump with a turbine driver (HP-T). In this layout, the first

5. RESULTS AND DISCUSSION We have carried out process optimization for three different plant capacities: 8000, 80 000, and 120 000 ton/year of anhydrous ethanol with a 10 wt % aqueous solution as feed. The proposed extraction−dehydration process is best suited for large scale continuous operations. Therefore, the design of larger plant capacities is completely feasible. We assumed that the plants run all year round as in corn-based ethanol plants. For each plant capacity, we have solved three nonlinear programming problems (NLPs) corresponding to the basic Table 4. Economic Variables

ethanol production 8000 ton/year HP-M capital cost (US$/h) capital cost (US$/ton anhydrous ethanol) operating cost (US$/h) operating cost (US$/ton anhydrous ethanol) dehydration plant cost (US$) total product cost (US$/ton anhydrous ethanol)

41.1 41.1 63.1 63.1 2 005 169 107.4 ethanol production 80 000 ton/year

capital cost (US$/h) capital cost (US$/ ton anhydrous ethanol) operating cost (US$/h) operating cost (US$/ton anhydrous ethanol) dehydration plant cost (US$) total product cost (US$/ton anhydrous ethanol)

209.7 20.97 628.7 62.87 10 229 292 86.1 ethanol production 120 000 ton/year

HP-M

capital cost (US$/h) capital cost (US$/ton anhydrous ethanol) operating cost (US$/h) operating cost (US$/ton anhydrous ethanol) dehydration plant cost (US$) total product cost (US$/ton anhydrous ethanol)

HP-T

basic

42.8 42.8 15.6 15.6 2 087 403 59.8

38.9 38.9 60.1 60.1 1 896 039 101.8

HP-T

basic

210.0 21.0 126.9 12.69 10 242 995 34.6

198.1 19.81 606.5 60.65 9 661 862 82.6

HP-M

HP-T

basic

304.5 20.30 937.5 62.5 14 853 609 84.9

305.4 20.36 181.2 12.08 14 897 116 33.3

289.4 19.29 900.8 60.05 14 118 395 81.4

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remaining alternatives, for each plant production level. The differences in energy consumption can be seen in Figure 9. The HP-T scheme reduces energy consumption around 11.8%, as compared to the basic scheme for the lower capacity ethanol production plant, and 7.2% for 120 000 ton/year ethanol production. This reduction can be partially explained by the fact that for the basic scheme it is necessary to buy part of the steam required in RBC1, while for the HP-T scheme complete energy integration is attained. For the HP-M scheme, no external energy is required for RBC1, and additional steam is needed for the RBBC, while for the HP-T scheme part of this energy is provided by the turbine exhaust steam. Regarding design variables, the variations in solvent requirements in the different ethanol production levels are important: the lowest plant capacity solvent requirement per unit of product, 200 kmol propane/ton anhydrous ethanol, is 4% greater than the required for the largest ethanol plant capacity (192 kmol propane/ton anhydrous ethanol), and a similar difference is found with the medium plant capacity, 193.5 kmol propane/ton anhydrous ethanol. The optimal temperature was around 360 K in all cases. Table 4 shows economic variables. The heat pump−turbine driver scheme (HP-T), renders lower operating costs, in comparison to the basic and heat pump−motor driver schemes, for the three analyzed plant capacities. For 120 000 ton/year, the savings in operating cost between HP-T and the alternative schemes is more than 80% per ton of anhydrous product. The HP-T scheme renders considerably the lowest energy consumption, mainly due to energy integration between beer column reboiler and the exhaust steam from the turbine. So, the energy integration proposed in the HP-T scheme, results in an important reduction of operating costs. Capital cost differences for the analyzed schemes and ethanol production capacities can be seen in Figure 10. For the lowest ethanol production plant capacity, the capital cost per ton of

Figure 11. Final product cost variation with ethanol production, for HP-T, HP-M, and basic schemes.

distillation column top vapor is recompressed to provide complete RBC1 heat duty, while the exhaust steam from the turbine is integrated with the beer column reboiler RBBC. In the basic scheme, the proposed energy integration between the top vapor of beer column and the column reboiler RBC1 is not enough to provide the heat required for RBC1, so the incorporation of a heat pump (HP) in the remaining schemes is necessary to achieve complete energy integration. Table 3 shows optimal conditions for design variables and integrated energy consumption for each analyzed scheme. It can be noted that the heat pump−turbine driver scheme (HP-T) provides lower energy consumption than the Table 5. Equipment Cost and Description for HP-T Schemea

ethanol production 8000 ton/year BC diam. (m) height (m) pressure (bar) operating cost (US$/h) total cost (US$)

0.90 19.35 2.0 4.62 225 318

diam. (m) height (m) pressure (bar) operating cost (US$/h) total cost (US$)

2.85 27.38 2.0 21.10 1 028 918

BC

diam. (m) height (m) pressure (bar) operating cost (US$/h) total cost (US$)

HPE

C1

C2

0.56 1.27 0.35 8.40 37.64 18.30 design variable 25.0 12.5 1.53 10.39 1.99 74 849 506 961 97 293 ethanol production 80 000 ton/year HPE

C1

C2

1.76 4.02 1.12 11.84 53.14 25.84 design variable 25.0 12.5 4.51 65.23 6.85 219 767 3 181 955 334 125 ethanol production 120 000 ton/year

RBBC

RBC1

RBC2

2.0 6.84 320 907

25.0 7.11 346 817

12.5 1.76 85 705

RBBC

RBC1

RBC2

2.0 39.20 1 941 763

25.0 40.74 1 987 496

12.5 7.75 377 912

BC

HPE

C1

C2

RBBC

RBC1

RBC2

3.49 29.10 2.0 28.47 1 388 850

2.14 12.57 design variable 5.62 273 940

4.90 56.39 25.0 89.19 4 350 758

1.37 27.46 12.5 8.79 428 604

2.0 77.45 2 667 345

25.0 55.59 2 711 908

12.5 10.47 510 790

BC: beer column. HPE: high pressure extractor. C1: first distillation column. C2: second solvent recovery column. RBBC: reboiler of BC. RBC1: reboiler of C1. RBC2: reboiler of C2.

a

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Table 6. Solvent−Feed Pumps, Compressor and Drivers Costs, and Powera ethanol production 8000 ton/year CT

T

power (kW) total cost (US$) operating cost (US$/h) operating cost (US$/ton)

51.68 99 889 3.71 3.71

98.19 81 167

CT

T


354.7 367 080 11.89 1.19

673.9 213 102

CT

T

CM

M

PF

PS1

PS2


473.8 445 043 14.13 0.94

900.2 244 386

473.8 445 043 13.24 0.88

500.1 200 880

35.91 146 337 3.00 0.20

305.8 610 074 12,51 0.83

10.76 120 919 2.48 0.17

CM

M

45.21 47.73 91 059 13 385 2.14 2.14 ethanol production 80 000 ton/year CM

M

353.8 373.5 366 517 200 880 11.63 1.16 ethanol production 120 000 ton/year

PF

PS1

PS2

2.00 48 798 1.00 1.00

15.40 170 544 3.50 3.50

0.72 47 081 0.97 0.97

PF

PS1

PS2

22.60 121 503 2.49 0.25

186.2 487 187 9.99 1.00

7.16 103 595 2.12 0.21

a

CT: compressor associated to turbine driver. CM: compressor associated to electrical motor driver. T: turbine. M: electrical motor. PF: feed pump. PS1: first solvent pump. PS2: second solvent pump.

product for the basic scheme is 10% lower than HP-T scheme; however, for the largest one, this difference is reduced to 5.5%. Evaluating the final product cost for 8000 ton/year of anhydrous ethanol production, the HP-T scheme renders a final product cost more than 55% lower than the alternative schemes. For the largest production capacity (120 000 ton/ year), this difference reaches 40% (Figure 11). Total plant costs for each ethanol production are shown in Figure 12. Table 5 shows cost estimation results and design parameters for main units in the HP-T scheme, for the three analyzed plant capacities. Equipment capital costs vary for the different plant capacities from 75 000 US$ to 4 350 000 US$ for column C1. Meanwhile, operating costs for this column varies from 10.39 US$/h at the lowest ethanol production plant to 89 US$/h for the largest production. Table 6 shows costs and power results for HP (compressor + driver) options and solvent and feed pumps. Column dimensions were calculated taking into account 80% of flooding conditions. Total costs for each unit include final installation cost, contingency and fee, and a factor for new plants. Operating costs are calculated over an operating period of 8000.0 h/year. Analyzing the fixed cost for the heat pump (compressor + driver), the variation is from 104 445 US$ for the lowest production capacity to 689 400 US$ for the largest one. Referring to cost for feed and solvent pumps, the first solvent pump (PS1) is the most important one, with costs ranging from 16 800 US$ to 610 074 US$, depending on ethanol production level. Total plant dehydration cost varies from 2 000 000 to 14 900 000 US$ for the analyzed range of capacities, as shown in Figure 12. O’Brien et al.32 studied dehydration of ethanol through a pervaporation−distillation process for a commercial-scale fuel ethanol plant (149 335 ton/year) reporting 64 US$/ton of anhydrous ethanol against 33 US$/ton in this work in a 120 000 ton/year plant. Moreover, Hoch and Espinosa6 carried out cost analysis for two different processes in a fuel plant, producing 18 936 ton/year of anhydrous ethanol. For an alternative distillation plus extractive distillation option, they obtained 115 US$/ton, and using alternative distillation

Figure 12. Plant cost variation with ethanol production, for HP-T, HP-M, and basic schemes.

plus pervaporation (membrane-based technology), costs were 87 US$/ton. Table 7 shows a comparison of capital, operating, and final product costs for competing dehydration processes. Regarding energy consumption, Kumar et al.33 analyzed a fractional distillation process to obtain ethanol 92−94 wt % and reported energy consumption of 11.720 kJ/kg ethanol. To further dehydrate ethanol to more than 99.9 wt %, these authors analyze several processes of anhydrous ethanol production such as azeotropic distillation, extractive distillation, and membrane pervaporation, among others. In these processes, they report energy consumption varying between 5020 kJ/kg ethanol (extractive distillation process with salt calcium chloride) to 18 840 kJ/kg ethanol (extractive distillation process with ethylene glycol) against 3739 kJ//kg ethanol achieved in this work for the entire dehydration process with the HP-T scheme. Consequently, it is important to note that the optimized scheme proposed in this work results in 3793


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Table 7. Cost Comparison between Different Technologies for Ethanol Dehydration operating cost (US$/ton anhydrous ethanol)

final product cost (US$/ton)

149 335 149 335

O’Brien et al.32 10.1 26.7

24.8 37.3

34.9 64.0

alternative distillation + extractive distillation alternative distillation + pervaporation

18 936

Hoch and Espinosa6 20.5

94.8

115.3

52.9

86.6

basic dehydration scheme heat pump with turbine driver (HP-T) integrated scheme

120 000 120 000

60.1 12.1

79.4 32.5

dehydration technol. basic process dehydration + distillation pervaporation + dehydration + distillation

plant capacities (ton anhydrous ethanol/year)

18 936

capital cost (US$/ton anhydrous ethanol)

33.7 Paulo et al. (this work) 19.3 20.4

(2) Dunnett, A. J.; Adjiman, C. S.; Shah, N. A spatially explicit wholesystem model of the lignocellulosic bioethanol supply chain: An assessment of decentralized processing potential. Biotechnol. Biofuels 2008, 1, 13. (3) Martín, M.; Grossmann, I. E. Superstructure optimization of lignocellulosic bioethanol plants. Comput.-Aided Chem. Eng. 2010, 28, 943−948. (4) Woods, R. P.; Coleman, J. R.; De Deng, M. (Enol Energy Inc., Toronto, CA) Genetically modified cyanobacteria for the production of ethanol, the constructs and method thereof. United States Patent No. 6699696, Mar. 2, 2004. (5) Dexter, J.; Fu, P. Metabolic engineering of cyanobacteria for ethanol production. Energy Environ. Sci. 2009, 2, 857−864. (6) Hoch, M. P.; Espinosa, J. Conceptual design and simulation tools Aapplied to the evolutionary optimization of a bioethanol purification plant. Ind. Eng. Chem. Res. 2008, 47, 7381−7389. (7) Lai, C.-L.; Liou, R.-M.; Chen, S.-H.; Shih, C.-Y.; Chang, J. S.; Huang, C.-H.; Hung, M.-Y.; Lee, K.-R. Dehydration of ethanol/water mixture by asymmetric ion-exchange membranes. Desalination 2011, 266, 17−24. (8) Karuppiah, R.; Peschel, A.; Grossmann, I. E.; Martín, M.; Martinson, W.; Zullo, L. Energy optimization for the design of cornbased ethanol plants. AIChE J. 2008, 54, 1499−1525. (9) Huang, Y.; Baker, R. W.; Vane, L. M. Low-energy distillation− membrane separation process. Ind. Eng. Chem. Res. 2010, 49, 3760− 3768. (10) Brignole, E. A.; Andersen, P.; Fredenslund, Aa. Supercritical fluid extraction of alcohol from water. Ind. Eng. Chem. Res. 1987, 26, 254. (11) Zabaloy, M.; Mabe, G.; Bottini, S. B.; Brignole, E. A. The application of high water-volatilities over some liquefied near-critical solvents as a means of dehydrating oxychemicals. J. Supercrit. Fluids 1992, 5, 186−191. (12) Horizoe, H.; Tanimoto, T.; Yamamoto, I.; Kano, Y. Phase equilibrium study for the separation of ethanol−water solution using subcritical and supercritical hydrocarbon solvent extraction. Fluid Phase Equilib. 1993, 84, 297. (13) Gros, H. P.; Diaz, M. S.; Brignole, E. A. Near-critical separation of aqueous azeotropic mixtures: process synthesis and optimization. J. Supercrit. Fluids 1998, 12, 69. (14) Diaz, S.; Gros, H.; Brignole, E. A. Thermodynamic modeling, synthesis and optimization of extraction−dehydration processes. Comput. Chem. Eng. 2000, 24, 2069−2080. (15) Paulo, C. I.; Diaz, M. S.; Brignole, E. A. Energy consumption minimization in bioethanol dehydration with supercritical fluids. Comput.-Aided Chem. Eng. 2009, 27, 1749−1754. (16) Paulo, C. I.; Diaz, M. S.; Brignole, E. A., Cost evaluation for bioethanol extraction and dehydration plant. In Proceedings of 9th Conference on Supercritical Fluids and Their Applications; Department of Chemical and Food Engineering University of Palermo: Sorrento, 2010; p 105.

important energy and cost savings for the entire ethanol dehydration process.

6. CONCLUSIONS A low-energy and cost-effective process combining extraction and dehydration with a supercritical solvent is described in this paper for separation of ethanol−water mixtures obtained by fermentation. Capital, operating, and product costs have been determined for a nonconventional extraction−dehydration process for plant capacities ranging between 8000 and 120 000 ton/year. Numerical results show that a heat pump−turbine driver scheme (HP-T) provides the lowest energy consumption, as well as the lowest operating and total product costs for the three plant production levels analyzed. Energy savings between the optimized scheme (HP-T) and the basic one are around 12%. In turn, total final cost savings per ton of product reached 40% for the larger capacity ethanol production plant. Furthermore, the GCA-EOS gives reliable thermodynamics properties predictions to analyze different designs for anhydrous ethanol extraction−dehydration plant. The integration of this thermodynamic model with nonlinear programming techniques provides optimal design and scale-up of supercritical dehydration processes. The inclusion of an economic model has allowed optimization of process operating conditions to achieve maximum net profit for each analyzed scheme. Optimization results indicate that employing extraction− dehydration of ethanol with supercritical propane could be a profitable dehydrating process, as well as environmentally friendly and economically competitive with current commercial technologies.

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AUTHOR INFORMATION

Corresponding Author

* E-mail: [email protected]. Phone: +54-02914861700. Fax: +54-0291-4861600. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors gratefully acknowledge financial support from the National Research Council (CONICET), Universidad Nacional del Sur, and ANPCYT, Argentina.

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REFERENCES

(1) Balat, M.; Balat, H.; Oz, C. Progress in bioethanol processing. Prog. Energy Combust. Sci. 2008, 34, 551−573. 3794


Energy & Fuels

Article

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Minimizing Costs in Near-Critical Bioethanol Extraction and

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