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Simulation of ethanol production from sugarcane in Brazil: economic study of an autonomous distillery
20th European Symposium on Computer Aided Process Engineering – ESCAPE20 S. Pierucci and G. Buzzi Ferraris (Editors) © 2010 Elsevier B.V. All rights reserved.
Simulation of ethanol production from sugarcane in Brazil: economic study of an autonomous distillery Marina O.S. Diasa,b, Marcelo P. Cunhaa, Charles D.F. Jesusa, Mirna I.G. Scandiffioa, Carlos E.V. Rossella,b, Rubens Maciel Filhob, Antonio Bonomia a
CTBE – Bioethanol Science and Technology National Laboratory, PO Box 6170 – CEP 13083-970, Campinas – SP, Brazil, [email protected] b School of Chemical Engineering, University of Campinas, PO Box 6066 – CEP 13083-970, Campinas – SP, Brazil
Abstract Simulation of the production of ethanol from sugarcane in an autonomous distillery was carried out using software SuperPro Designer and electronic spreadsheet. Analysis of the ethanol production costs was performed for different production scenarios, considering improvements on the energy production from sugarcane bagasse and the selling of surplus electricity. It was verified that selling of surplus electricity positively influences the ethanol production costs. Keywords: ethanol, simulation, sugarcane, economic evaluation.
1. Introduction Brazil produces bioethanol from sugarcane on a large scale basis since the 1970s (Bake et al., 2009); increase on the demand for the biofuel as a substitute or complement of gasoline has motivated the search for more efficient means of production. As a consequence, the evaluation of conventional ethanol production and the identification of critical process parameters are required. Brazil and the United States, which produces ethanol from corn, are the largest ethanol producers in the world (Balat et al., 2008); however, net energy of ethanol production from sugarcane is more positive than that from corn (Leite et al., 2009): sugarcane bagasse, one of the main by-products of sugarcane processing, is used as fuel in cogeneration systems, which provide steam and electric energy to supply the bioethanol production process. Thus, an autonomous distillery may produce electric energy to sell to the grid, if there is a surplus produced during cogeneration. For the past few years, growing interest on production of electricity in ethanol production plants has been observed, which may improve revenues and competitiveness of sugarcane ethanol. In this work, simulations of ethanol production from sugarcane were carried out using SuperPro Designer 7.5 (Intelligen, Inc) in order to evaluate production costs within the industrial site. A “standard” autonomous distillery is considered, in which 500 tons of sugarcane (TC) per hour are processed, producing 1000 m³/day of anhydrous bioethanol, during 180 days (sugarcane harvest season). Data used to simulate the unit operations were obtained from industrial sites and from literature.
2. Ethanol production process from sugarcane In an autonomous distillery, all the sugarcane processed is used to provide sugars for fermentation, from which ethanol is produced.
M.O.S. Dias et al.
The main steps required for the ethanol production process from sugarcane in an autonomous distillery are illustrated in Fig. 1.
Figure 1. Simplified block flow diagram of the anhydrous bioethanol production process from sugarcane in an autonomous distillery.
3. Process simulation procedure Operating and process parameters of the autonomous distillery were obtained in the literature and from operating industries. A “standard” plant is considered, with unit operations typical of those found in the Brazilian bioethanol industry. 500 TC per hour are processed for production of around 1000 m³/day of anhydrous bioethanol (99.3 wt % ethanol). 3.1. Simulation Firstly, a mass balance of the process was carried out using a spreadsheet, in which usual industrial parameters (efficiency of unit operations, amount of raw materials and yields) were employed. Then, simulation was carried out using software SuperPro Designer 7.5 from Intelligen, Inc, considering the unit operations indicated in Fig. 1 except for the operations related to the cogeneration system. In these operations, where steam and electric energy are produced, calculation was performed using a conventional spreadsheet, since the simulation software does not have the procedures required to perform its simulation at the moment. A simplified flowsheet of the simulation of bioethanol production process is shown in Fig. 2. Several hypothetic components (sugarcane bagasse constituents, sugarcane impurities, etc.) were inserted into the simulator database, in order to represent the bioethanol production process more accurately. Energy demand of unit operations that are not available in the simulator database, such as extraction of sugars in the mills, azeotropic distillation and adsorption onto molecular sieves (processes used on ethanol dehydration), was obtained in the literature (Ensinas et al., 2007; Andrietta, 2009). The main process parameters are listed in Table 1. 3.2. Dehydration processes Two dehydration processes were considered: azeotropic distillation using cyclohexane as entrainer, which is the most common and most energy-intensive process used in ethanol dehydration, and adsorption onto molecular sieves, which presents the lowest energy consumption among the commercial dehydration processes available, but requires a larger investment.
Simulation of ethanol production from sugarcane in Brazil: economic study of an autonomous distillery
Figure 2. Simplified flowsheet of the simulation of bioethanol production process carried out using SuperPro Designer. Table 1. Main parameters adopted in the simulation of the “standard” plant.
Parameter Sugarcane crushing rate Days of operation Fibre on sugarcane Sugars on sugarcane Amount of sugarcane trash produced in the fields Dirt removal on sugarcane cleaning Sugar losses on sugarcane cleaning Sugars recovery on the mills Sugarcane bagasse water content Recovery of sugars on juice treatment Fermentation yield Ethanol recovery on distillation and dehydration
Value Unit 500 180 12 14 140 90 0.8 96 50 99.5 90 99.7
TC/h days/year wt% wt% kg/TC % kg/TC % wt% % % %
3.3. Cogeneration system Conventional plants are equipped with boilers for the production of 22 bar steam, in which sugarcane bagasse, produced in the mills, is used as fuel. The steam produced in the boilers is used to produce electricity in steam turbines and as thermal energy for the process, besides being used in mechanical drivers in the sugarcane preparation and juice extraction systems. A 90 bar cogeneration system with back pressure and condensing steam turbines for production of steam and electric energy is considered as well, in which surplus electricity is sold to the grid. The amount of sugarcane bagasse available for cogeneration and the process steam demand are obtained in the simulation and used to determine the parameters of the coproduction of heat and power (CHP) plant; as a
M.O.S. Dias et al.
result, the amount of surplus electric energy available for sale is determined. The main parameters of the CHP system are shown in Table 2. Table 2. Main parameters considered in the simulation of the CHP system (Dias et al., 2009; Ensinas, 2008; Seabra, 2008).
Value
Parameter
Unit
*
22 bar – boiler thermal efficiency 75 % 90 bar – boiler thermal efficiency* 86 % High pressure steam turbines isentropic efficiency 72 % Intermediate pressure steam turbines isentropic efficiency 81 % Condensation turbine isentropic efficiency 70 % Mechanical drivers – turbine isentropic efficiency 55 % Generator efficiency 96 % Sugarcane bagasse LHV (50 wt% water) 7565 kJ/kg Sugarcane trash LHV (15 wt% water) 12960 kJ/kg Electric power demand of the distillery 12 kWh/TC Mechanical power demand – cane preparation and juice extraction 16 kWh/TC Electric power demand of the distillery – electric drivers 18 kWh/TC Outlet pressure of high pressure steam turbine 22 bar 6 bar 1st extraction pressure 2.5 bar 2nd extraction pressure Process steam pressure 2.5 bar * Low Heating Value (LHV) base Besides the use of more efficient boilers (90 bar), two other process improvements were considered: the use of electric drives for mills and other equipments, replacing the mechanical drivers, and the use of sugarcane trash as fuel in boilers. Sugarcane trash is composed by leaves and tops, and nowadays is burned before harvest or left in the field, but a fraction of the trash generated may be recovered and used as a fuel in the plant. In all the cases where excess steam is produced, it is condensed on condensing steam turbines, increasing the amount of electricity produced. In this work, 50 % of trash is used as fuel for the production of steam and electricity; the remaining fraction is left in the fields in order to provide control of weeds and diseases (Hassuani et al., 2005).
4. Simulation results and discussion Different configurations were analysed, combining the options shown in Table 3. Table 3. Parameters considered in the studied scenarios.
Parameter st
1 generation anhydrous ethanol production 22 bar boilers Dehydration by azeotropic distillation Dehydration by adsorption onto molecular sieves Sell of surplus bagasse 90 bar boilers Burning of surplus bagasse Sell of surplus electricity Electrification of drives 50 % of trash used
I II III IV X X X X X X X X X
X
X
X
X
X X X X
X X X X X
Simulation of ethanol production from sugarcane in Brazil: economic study of an autonomous distillery
Case I presents the typical traditional autonomous distillery; cases II through IV present increasing levels of technologic improvements considered in this work, which influence the production of electricity. Simulation results for each of the studied scenarios are displayed in Table 4. Table 4. Simulation results for each of the studied scenarios.
I
Parameter
II
III
IV
Anhydrous ethanol production – L/t sugarcane 83.3 83.3 83.3 83.3 Surplus bagasse – kg/t sugarcane 16.6 0 0 0 Surplus electricity sold – kWh/t sugarcane 0 68.2 73.7 154.9
5. Economic evaluation and discussion On Table 5 the basic parameters for the economic analysis are displayed. Table 5. Basic parameters used in the economic analysis1.
Parameter
Value 25 years 2 years US$ 16.58/t US$ 12.21/t 10 years 34.0% US$ 0.40/L US$ 67.05/MWh US$ 16.58/t
Project lifetime Salvage value of equipment Construction and start-up Sugarcane price2 Trash price (15% moisture)3 Depreciation (linear) Tax rate (income and social contributions) Ethanol (producer price)4 Bioelectricity (producer price)3 Bagasse (producer price)5 1
Considered the exchange rate US$ 1.00 = R$ 2.088 (average of the past 12 months) Average of the last 12 months (UDOP, 2009) 3 Seabra, 2008 4 Average of the last 12 months (CEPEA, 2009) 5 Considered equal to the sugarcane price 2
In order to evaluate ethanol production costs, equipment costs were evaluated for each of the studied scenarios based on data provided by the industry. Firstly, economic analysis of each of the studied scenarios was carried out using the parameters presented in Table 5. Production costs were then calculated reducing ethanol, surplus bagasse and electricity prices (amount paid to the producer) simultaneously, at the same proportion, until real profits reached zero (i.e, internal rate of return per year equal to zero). Table 6 presents the investment required on equipments for each scenario and the calculated ethanol production costs. Table 6. Investment and ethanol production costs on each scenario.
I
Parameter
II
III
IV
6
Equipment costs – 10 US$ 144 185 185 205 Ethanol production costs – US$/L 0.313 0.289 0.286 0.265 Since sugarcane costs are equivalent to roughly 60 % of the final production costs of ethanol, a sensitivity analysis was carried out in order to evaluate sugarcane costs on the final ethanol cost; results are presented in Fig. 3 for the four studied scenarios.
M.O.S. Dias et al.
Figure 3. Evaluation of anhydrous ethanol production costs for different sugarcane prices.
6. Conclusions In this work simulation of a typical autonomous distillery for anhydrous bioethanol production from sugarcane was carried out. Selling of surplus electricity (cases II through IV) can improve the profitability of ethanol production from sugarcane, since it leads to a reduction on ethanol production costs. The use of sugarcane trash as a fuel on cogeneration systems can significantly reduce ethanol production costs, since large amounts of electricity are available for sale.
7. Acknowledgements The authors acknowledge Dedini Indústrias de Base S/A for supplying data for investment calculations.
References S.R. Andrietta, Optimal Industrial Fermentation, In: BIOEN Workshop on Process for ethanol production, FAPESP, 2009. Available online at http://www.fapesp.br/eventos/2009/09/ 10_bioen/Silvio_Roberto.pdf, retrieved on sept 15, 2009 (in Portuguese) M. Balat, H. Balat, C. Öz, Prog. in En. and Comb.Sci., 34 (2008) 551-573 J. Bake, M. Junginger, A. Faaij, T. Poot, A. Walter, Biom. Bioen., 33 (2009) 644-658 CEPEA - Center for Advanced Studies on Applied Economics, 2009, Available online at www.cepea.usp.br, retrieved on nov 15, 2009 M.O.S. Dias, A.V. Ensinas, S.A. Nebra, R. Maciel Filho, C.E.V. Rossell, M.R.W. Maciel, Chem. Eng. Res. Des., 87 (2009) 1206-1216 A.V. Ensinas, S.A. Nebra, M.A. Lozano, L.M. Serra, En. Conv. Manag., 48 (2007) 2978–2987 A.V. Ensinas, PhD Thesis (Mechanical Engineering), School of Mechanical Engineering, State University of Campinas (in Portuguese), 2008 R. Leite, M. Leal, L. Cortez, W. Griffin, M. Scandiffio, Energy, 34 (2009) 655-661 S.J. Hassuani, M.R.L.V. Leal, I.C. Macedo (eds.), Biomass Power Generation—Sugarcane Bagasse and Trash. Piracicaba: PNUD-CTC, 2005 J.E.A. Seabra, PhD Thesis (Energetic Systems Planning), School of Mechanical Engineering, State University of Campinas (in Portuguese), 2008 UDOP – Union of Biofuel Producers, Sugarcane prices, 2009. Available online at www.udop.com.br/index.php?item=cana. Retrieved on nov 15, 2009
Simulation of ethanol production from sugarcane in Brazil: economic study of an autonomous distillery Marina O.S. Diasa,b, Marcelo P. Cunhaa, Charles D.F. Jesusa, Mirna I.G. Scandiffioa, Carlos E.V. Rossella,b, Rubens Maciel Filhob, Antonio Bonomia a
CTBE – Bioethanol Science and Technology National Laboratory, PO Box 6170 – CEP 13083-970, Campinas – SP, Brazil, [email protected] b School of Chemical Engineering, University of Campinas, PO Box 6066 – CEP 13083-970, Campinas – SP, Brazil
Abstract Simulation of the production of ethanol from sugarcane in an autonomous distillery was carried out using software SuperPro Designer and electronic spreadsheet. Analysis of the ethanol production costs was performed for different production scenarios, considering improvements on the energy production from sugarcane bagasse and the selling of surplus electricity. It was verified that selling of surplus electricity positively influences the ethanol production costs. Keywords: ethanol, simulation, sugarcane, economic evaluation.
1. Introduction Brazil produces bioethanol from sugarcane on a large scale basis since the 1970s (Bake et al., 2009); increase on the demand for the biofuel as a substitute or complement of gasoline has motivated the search for more efficient means of production. As a consequence, the evaluation of conventional ethanol production and the identification of critical process parameters are required. Brazil and the United States, which produces ethanol from corn, are the largest ethanol producers in the world (Balat et al., 2008); however, net energy of ethanol production from sugarcane is more positive than that from corn (Leite et al., 2009): sugarcane bagasse, one of the main by-products of sugarcane processing, is used as fuel in cogeneration systems, which provide steam and electric energy to supply the bioethanol production process. Thus, an autonomous distillery may produce electric energy to sell to the grid, if there is a surplus produced during cogeneration. For the past few years, growing interest on production of electricity in ethanol production plants has been observed, which may improve revenues and competitiveness of sugarcane ethanol. In this work, simulations of ethanol production from sugarcane were carried out using SuperPro Designer 7.5 (Intelligen, Inc) in order to evaluate production costs within the industrial site. A “standard” autonomous distillery is considered, in which 500 tons of sugarcane (TC) per hour are processed, producing 1000 m³/day of anhydrous bioethanol, during 180 days (sugarcane harvest season). Data used to simulate the unit operations were obtained from industrial sites and from literature.
2. Ethanol production process from sugarcane In an autonomous distillery, all the sugarcane processed is used to provide sugars for fermentation, from which ethanol is produced.
M.O.S. Dias et al.
The main steps required for the ethanol production process from sugarcane in an autonomous distillery are illustrated in Fig. 1.
Figure 1. Simplified block flow diagram of the anhydrous bioethanol production process from sugarcane in an autonomous distillery.
3. Process simulation procedure Operating and process parameters of the autonomous distillery were obtained in the literature and from operating industries. A “standard” plant is considered, with unit operations typical of those found in the Brazilian bioethanol industry. 500 TC per hour are processed for production of around 1000 m³/day of anhydrous bioethanol (99.3 wt % ethanol). 3.1. Simulation Firstly, a mass balance of the process was carried out using a spreadsheet, in which usual industrial parameters (efficiency of unit operations, amount of raw materials and yields) were employed. Then, simulation was carried out using software SuperPro Designer 7.5 from Intelligen, Inc, considering the unit operations indicated in Fig. 1 except for the operations related to the cogeneration system. In these operations, where steam and electric energy are produced, calculation was performed using a conventional spreadsheet, since the simulation software does not have the procedures required to perform its simulation at the moment. A simplified flowsheet of the simulation of bioethanol production process is shown in Fig. 2. Several hypothetic components (sugarcane bagasse constituents, sugarcane impurities, etc.) were inserted into the simulator database, in order to represent the bioethanol production process more accurately. Energy demand of unit operations that are not available in the simulator database, such as extraction of sugars in the mills, azeotropic distillation and adsorption onto molecular sieves (processes used on ethanol dehydration), was obtained in the literature (Ensinas et al., 2007; Andrietta, 2009). The main process parameters are listed in Table 1. 3.2. Dehydration processes Two dehydration processes were considered: azeotropic distillation using cyclohexane as entrainer, which is the most common and most energy-intensive process used in ethanol dehydration, and adsorption onto molecular sieves, which presents the lowest energy consumption among the commercial dehydration processes available, but requires a larger investment.
Simulation of ethanol production from sugarcane in Brazil: economic study of an autonomous distillery
Figure 2. Simplified flowsheet of the simulation of bioethanol production process carried out using SuperPro Designer. Table 1. Main parameters adopted in the simulation of the “standard” plant.
Parameter Sugarcane crushing rate Days of operation Fibre on sugarcane Sugars on sugarcane Amount of sugarcane trash produced in the fields Dirt removal on sugarcane cleaning Sugar losses on sugarcane cleaning Sugars recovery on the mills Sugarcane bagasse water content Recovery of sugars on juice treatment Fermentation yield Ethanol recovery on distillation and dehydration
Value Unit 500 180 12 14 140 90 0.8 96 50 99.5 90 99.7
TC/h days/year wt% wt% kg/TC % kg/TC % wt% % % %
3.3. Cogeneration system Conventional plants are equipped with boilers for the production of 22 bar steam, in which sugarcane bagasse, produced in the mills, is used as fuel. The steam produced in the boilers is used to produce electricity in steam turbines and as thermal energy for the process, besides being used in mechanical drivers in the sugarcane preparation and juice extraction systems. A 90 bar cogeneration system with back pressure and condensing steam turbines for production of steam and electric energy is considered as well, in which surplus electricity is sold to the grid. The amount of sugarcane bagasse available for cogeneration and the process steam demand are obtained in the simulation and used to determine the parameters of the coproduction of heat and power (CHP) plant; as a
M.O.S. Dias et al.
result, the amount of surplus electric energy available for sale is determined. The main parameters of the CHP system are shown in Table 2. Table 2. Main parameters considered in the simulation of the CHP system (Dias et al., 2009; Ensinas, 2008; Seabra, 2008).
Value
Parameter
Unit
*
22 bar – boiler thermal efficiency 75 % 90 bar – boiler thermal efficiency* 86 % High pressure steam turbines isentropic efficiency 72 % Intermediate pressure steam turbines isentropic efficiency 81 % Condensation turbine isentropic efficiency 70 % Mechanical drivers – turbine isentropic efficiency 55 % Generator efficiency 96 % Sugarcane bagasse LHV (50 wt% water) 7565 kJ/kg Sugarcane trash LHV (15 wt% water) 12960 kJ/kg Electric power demand of the distillery 12 kWh/TC Mechanical power demand – cane preparation and juice extraction 16 kWh/TC Electric power demand of the distillery – electric drivers 18 kWh/TC Outlet pressure of high pressure steam turbine 22 bar 6 bar 1st extraction pressure 2.5 bar 2nd extraction pressure Process steam pressure 2.5 bar * Low Heating Value (LHV) base Besides the use of more efficient boilers (90 bar), two other process improvements were considered: the use of electric drives for mills and other equipments, replacing the mechanical drivers, and the use of sugarcane trash as fuel in boilers. Sugarcane trash is composed by leaves and tops, and nowadays is burned before harvest or left in the field, but a fraction of the trash generated may be recovered and used as a fuel in the plant. In all the cases where excess steam is produced, it is condensed on condensing steam turbines, increasing the amount of electricity produced. In this work, 50 % of trash is used as fuel for the production of steam and electricity; the remaining fraction is left in the fields in order to provide control of weeds and diseases (Hassuani et al., 2005).
4. Simulation results and discussion Different configurations were analysed, combining the options shown in Table 3. Table 3. Parameters considered in the studied scenarios.
Parameter st
1 generation anhydrous ethanol production 22 bar boilers Dehydration by azeotropic distillation Dehydration by adsorption onto molecular sieves Sell of surplus bagasse 90 bar boilers Burning of surplus bagasse Sell of surplus electricity Electrification of drives 50 % of trash used
I II III IV X X X X X X X X X
X
X
X
X
X X X X
X X X X X
Simulation of ethanol production from sugarcane in Brazil: economic study of an autonomous distillery
Case I presents the typical traditional autonomous distillery; cases II through IV present increasing levels of technologic improvements considered in this work, which influence the production of electricity. Simulation results for each of the studied scenarios are displayed in Table 4. Table 4. Simulation results for each of the studied scenarios.
I
Parameter
II
III
IV
Anhydrous ethanol production – L/t sugarcane 83.3 83.3 83.3 83.3 Surplus bagasse – kg/t sugarcane 16.6 0 0 0 Surplus electricity sold – kWh/t sugarcane 0 68.2 73.7 154.9
5. Economic evaluation and discussion On Table 5 the basic parameters for the economic analysis are displayed. Table 5. Basic parameters used in the economic analysis1.
Parameter
Value 25 years 2 years US$ 16.58/t US$ 12.21/t 10 years 34.0% US$ 0.40/L US$ 67.05/MWh US$ 16.58/t
Project lifetime Salvage value of equipment Construction and start-up Sugarcane price2 Trash price (15% moisture)3 Depreciation (linear) Tax rate (income and social contributions) Ethanol (producer price)4 Bioelectricity (producer price)3 Bagasse (producer price)5 1
Considered the exchange rate US$ 1.00 = R$ 2.088 (average of the past 12 months) Average of the last 12 months (UDOP, 2009) 3 Seabra, 2008 4 Average of the last 12 months (CEPEA, 2009) 5 Considered equal to the sugarcane price 2
In order to evaluate ethanol production costs, equipment costs were evaluated for each of the studied scenarios based on data provided by the industry. Firstly, economic analysis of each of the studied scenarios was carried out using the parameters presented in Table 5. Production costs were then calculated reducing ethanol, surplus bagasse and electricity prices (amount paid to the producer) simultaneously, at the same proportion, until real profits reached zero (i.e, internal rate of return per year equal to zero). Table 6 presents the investment required on equipments for each scenario and the calculated ethanol production costs. Table 6. Investment and ethanol production costs on each scenario.
I
Parameter
II
III
IV
6
Equipment costs – 10 US$ 144 185 185 205 Ethanol production costs – US$/L 0.313 0.289 0.286 0.265 Since sugarcane costs are equivalent to roughly 60 % of the final production costs of ethanol, a sensitivity analysis was carried out in order to evaluate sugarcane costs on the final ethanol cost; results are presented in Fig. 3 for the four studied scenarios.
M.O.S. Dias et al.
Figure 3. Evaluation of anhydrous ethanol production costs for different sugarcane prices.
6. Conclusions In this work simulation of a typical autonomous distillery for anhydrous bioethanol production from sugarcane was carried out. Selling of surplus electricity (cases II through IV) can improve the profitability of ethanol production from sugarcane, since it leads to a reduction on ethanol production costs. The use of sugarcane trash as a fuel on cogeneration systems can significantly reduce ethanol production costs, since large amounts of electricity are available for sale.
7. Acknowledgements The authors acknowledge Dedini Indústrias de Base S/A for supplying data for investment calculations.
References S.R. Andrietta, Optimal Industrial Fermentation, In: BIOEN Workshop on Process for ethanol production, FAPESP, 2009. Available online at http://www.fapesp.br/eventos/2009/09/ 10_bioen/Silvio_Roberto.pdf, retrieved on sept 15, 2009 (in Portuguese) M. Balat, H. Balat, C. Öz, Prog. in En. and Comb.Sci., 34 (2008) 551-573 J. Bake, M. Junginger, A. Faaij, T. Poot, A. Walter, Biom. Bioen., 33 (2009) 644-658 CEPEA - Center for Advanced Studies on Applied Economics, 2009, Available online at www.cepea.usp.br, retrieved on nov 15, 2009 M.O.S. Dias, A.V. Ensinas, S.A. Nebra, R. Maciel Filho, C.E.V. Rossell, M.R.W. Maciel, Chem. Eng. Res. Des., 87 (2009) 1206-1216 A.V. Ensinas, S.A. Nebra, M.A. Lozano, L.M. Serra, En. Conv. Manag., 48 (2007) 2978–2987 A.V. Ensinas, PhD Thesis (Mechanical Engineering), School of Mechanical Engineering, State University of Campinas (in Portuguese), 2008 R. Leite, M. Leal, L. Cortez, W. Griffin, M. Scandiffio, Energy, 34 (2009) 655-661 S.J. Hassuani, M.R.L.V. Leal, I.C. Macedo (eds.), Biomass Power Generation—Sugarcane Bagasse and Trash. Piracicaba: PNUD-CTC, 2005 J.E.A. Seabra, PhD Thesis (Energetic Systems Planning), School of Mechanical Engineering, State University of Campinas (in Portuguese), 2008 UDOP – Union of Biofuel Producers, Sugarcane prices, 2009. Available online at www.udop.com.br/index.php?item=cana. Retrieved on nov 15, 2009