Property Modeling, Energy Balance and Process Simulation Applied to Bioethanol Purification

Abstract

The use of renewable sources has been an alternative to decrease the negative environmental impacts of fossil fuels. In this context, bioethanol from sugarcane has proved to be a successful option to gasoline. However, bioethanol purification through conventional distillation requires high-energy demand. For this reason, energy balance calculations are very useful to estimate steam demand. As a result, simpler alternatives for estimation of steam consumption from spreadsheet calculations have been encouraged. In contrast, many efforts have focused to solve complex flowsheets including tear streams, which increases convergence complexity for each change on feed composition and additional components. In this work, mathematical models were fitted to bioethanol–water mixture property data and applied to energy balance calculations involved in the conventional process for hydrated bioethanol purification. Total steam consumption was obtained for volume percentages of bioethanol in the wine feed stream of 6, 8 and 10 °GL considering potential losses found in the industrial reality. The calculated data were compared to simulations carried out in the Aspen Plus® software. Similar values of total steam consumption were found comparing the two approaches, where the average absolute relative deviation was kept below 5%. Moreover, simulated temperature and composition profiles agreed to process data. Finally, mathematical models and energy balance calculations proved to be a simpler and faster alternative to estimate total steam consumption involved in the hydrated bioethanol purification from wine.

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Abbreviations

a :

Constant defined by the experimental data to obtain c (kcal kg−1 °C−1)

b :

Constant defined by the experimental data to obtain c (kcal kg−1)

ARD:

Average relative deviation (%)

C :

Steam consumption per liter of bioethanol purified for the column (kg L−1)

c :

Specific heat of the material (kcal kg−1 °C−1)

C S :

Total steam consumption per liter of bioethanol purified for both columns (kg L−1)

F lP :

Factor of volume percentage of liquid phlegm relative to total phlegm volume

F Et2 :

Factor of volume percentage of second-grade bioethanol relative to total phlegm volume

H :

Specific enthalpy from the material stream (kcal kg−1)

k C :

Factor to take in account existing losses for an isolated column

k Et :

Bioethanol loss

k T :

Factor to take in account the steam loss in tubes

L :

Latent heat (kcal kg−1)

M :

Total utility mass injected in the column (kg)

m :

Mass from the material stream (kg)

Q :

Amount of energy from the material stream related to sensible heat (kcal)

q :

Amount of energy from the material stream related to phase change (kcal)

\( Q_{\text{loss}} \) :

Heat loss related to potential losses for an isolated column (kcal)

R :

Reflux ratio

R 2 :

Coefficient of determination

T :

Material temperature (°C)

V :

Volume of material (L)

x :

Liquid mole fraction

y :

Vapor mole fraction

Y :

Dependent variable

\( \bar{Y} \) :

Average value of the dependent variable Y

ρ :

Material density (kg L−1)

\( \varPhi \) :

Volume percentage of bioethanol (°GL)

\( \varPsi \) :

Mass percentage of bioethanol (°INPM)

A:

Column A

B:

Column B

D:

Distillate stage

Et:

Hydrated bioethanol

Et2:

Second-grade bioethanol

i :

i-th measured point

in:

Inlet material stream

LHS:

Left-hand side

min:

Minimum

N :

Number of stages

n :

Number of measured points

out:

Outlet material stream

P:

Phlegm stream

R:

Reflux stream

RHS:

Right-hand side

S:

Steam

v:

Vinasse stream

w:

Wine stream

cal:

Calculated

exp:

Experimental

l:

Liquid phase

sim:

Simulated results

v:

Vapor phase

ANP:

National Agency of Petroleum, Natural Gas and Biofuels

FAO:

Food and Agriculture Organization of the United Nations

Flegmass:

Bottom by-product from column B

Hydrated bioethanol:

Top product from column B

OECD:

Organisation for Economic Co-operation and Development

°GL:

Common unit of volume percentage (e.g., 10 °GL = 10 L of bioethanol per 100 L of hydroalcoholic mixture)

°INPM:

Common unit of mass percentage (e.g., 94 °INPM = 94 kg of bioethanol per 100 kg of hydroalcoholic mixture)

Second-grade bioethanol:

Top by-product from column A

Vinasse:

Bottom by-product from column A

Wine:

Hydroalcoholic mixture from fermentation

References

  1. Abbas, T., M. Issa, and A. Ilinca. 2019. Biomass cogeneration technologies: A review. Journal of Sustainable Bioenergy Systems 10: 1–15. https://doi.org/10.1016/j.fuproc.2018.09.019.

    CAS  Article  Google Scholar 

  2. Abdollahipoor, B., S.A. Shirazi, K.F. Reardon, and B.C. Windom. 2018. Near-azeotropic volatility behavior of hydrous and anhydrous ethanol gasoline mixtures and impact on droplet evaporation dynamics. Fuel Processing Technology 181: 166–174. https://doi.org/10.1016/j.fuproc.2018.09.019.

    CAS  Article  Google Scholar 

  3. Albarelli, J.Q., A.V. Ensinas, and M.A. Silva. 2014. Product diversification to enhance economic viability of second generation ethanol production in Brazil: The case of the sugar and ethanol joint production. Chemical Engineering Research and Design 92(8): 1470–1481. https://doi.org/10.1016/j.cherd.2013.11.016.

    CAS  Article  Google Scholar 

  4. Albuquerque, A.A., F.T.T. Ng, L. Danielski, and L. Stragevitch. 2020. Phase equilibrium modeling in biodiesel production by reactive distillation. Fuel 271: 117688. https://doi.org/10.1016/j.fuel.2020.117688.

    CAS  Article  Google Scholar 

  5. ANP. 2015. ANP Resolution No. 19, of April 15th, 2015. Official Diary of the Union, Ministry of Mines and Energy, National Agency of Petroleum, Natural Gas and Biofuels (ANP). http://legislacao.anp.gov.br/?path=legislacao-anp/resol-anp/2015/abril&item=ranp-19-2015. Accessed January 23, 2020 (in Portuguese).

  6. Baeyens, J., Q. Kang, L. Appels, R. Dewil, Y. Lv, and T. Tan. 2015. Challenges and opportunities in improving the production of bio-ethanol. Progress in Energy and Combustion Science 47: 60–88. https://doi.org/10.1016/j.pecs.2014.10.003.

    Article  Google Scholar 

  7. Balat, M., and H. Balat. 2009. Recent trends in global production and utilization of bio-ethanol fuel. Applied Energy 86(11): 2273–2282. https://doi.org/10.1016/j.apenergy.2009.03.015.

    CAS  Article  Google Scholar 

  8. Barreto, T.V., and A.C.D. Coelho. 2015. Chapter 16—Distillation. In Sugarcane, ed. F. Santos, A. Borém, and C. Caldas, 341–363. San Diego: Academic Press. https://doi.org/10.1016/B978-0-12-802239-9.00016-5.

    Google Scholar 

  9. Barros Neto, B., R.E. Bruns, and I.S. Scarminio. 2010. How to do experiments: Applications in science and industry, 4th ed. Porto Alegre: Bookman. (in Portuguese).

    Google Scholar 

  10. Batista, F.R.M., L.A. Follegatti-Romero, L.C.B.A. Bessa, and A.J.A. Meirelles. 2012. Computational simulation applied to the investigation of industrial plants for bioethanol distillation. Computers & Chemical Engineering 46: 1–16. https://doi.org/10.1016/j.compchemeng.2012.06.004.

    CAS  Article  Google Scholar 

  11. Bessa, L.C.B.A., M.C. Ferreira, E.A.C. Batista, and A.J.A. Meirelles. 2013. Performance and cost evaluation of a new double-effect integration of multicomponent bioethanol distillation. Energy 63: 1–9. https://doi.org/10.1016/j.energy.2013.10.006.

    CAS  Article  Google Scholar 

  12. Codistil. 1978. Operation manual of ethyl alcohol distilleries. Piracicaba: Engineering department. (in Portuguese).

    Google Scholar 

  13. Copersucar. 1987. Distillation, 1st ed. São Paulo: Copersucar Technology Center, Industrial division. (in Portuguese).

    Google Scholar 

  14. Danahy, B., D. Minnick, and M. Shiflett. 2018. Computing the composition of ethanol-water mixtures based on experimental density and temperature measurements. Fermentation 4(3): 72. https://doi.org/10.3390/fermentation4030072.

    CAS  Article  Google Scholar 

  15. Dias, M.O.S., M. Modesto, A.V. Ensinas, S.A. Nebra, R.M. Filho, and C.E.V. Rossell. 2011. Improving bioethanol production from sugarcane: evaluation of distillation, thermal integration and cogeneration systems. Energy 36(6): 3691–3703. https://doi.org/10.1016/j.energy.2010.09.024.

    CAS  Article  Google Scholar 

  16. Elia Neto, A., A. Shintaku, A.A.B. Pio, A.J. Conde, F. Giannetti, and J.L. Donzelli. 2009. Water conservation and reuse manual in the sugar-energy agro-industry. Brasília: National Water Agency (ANA), Federation of Industries of the State of São Paulo (Fiespe), Union of the Sugarcane Industry (UNICA), Sugarcane Technology Center (CTC). (in Portuguese).

    Google Scholar 

  17. Ferreira, M.C., A.J.A. Meirelles, and E.A.C. Batista. 2013. Study of the fusel oil distillation process. Industrial and Engineering Chemistry Research 52(6): 2336–2351. https://doi.org/10.1021/ie300665z.

    CAS  Article  Google Scholar 

  18. Fukushima, N.A., M.C. Palacios-Bereche, R. Palacios-Bereche, and S.A. Nebra. 2019. Energy analysis of the ethanol industry considering vinasse concentration and incineration. Renewable Energy 142: 96–109. https://doi.org/10.1016/j.renene.2019.04.085.

    CAS  Article  Google Scholar 

  19. Furlan, F.F., C.B.B. Costa, G.C. Fonseca, R.P. Soares, A.R. Secchi, A.J.G. Cruz, et al. 2012. Assessing the production of first and second generation bioethanol from sugarcane through the integration of global optimization and process detailed modeling. Computers & Chemical Engineering 43: 1–9. https://doi.org/10.1016/j.compchemeng.2012.04.002.

    CAS  Article  Google Scholar 

  20. Jacques, K.A., T.P. Lyons, and D.R. Kelsall. 2003. The Alcohol Textbook: A reference for the beverage, fuel and industrial alcohol industries. Nottingham: Nottingham University Press.

    Google Scholar 

  21. Junqueira, T.L., M.O.S. Dias, R.M. Filho, M.R.W. Maciel, and C.E.V. Rossell. 2009. Simulation of the azeotropic distillation for anhydrous bioethanol production: Study on the formation of a second liquid phase. In Computer aided chemical engineering, vol. 27, ed. R.M. de Brito Alves, C.A.O. do Nascimento, and E.C. Biscaia, 1143–1148. Amsterdam: Elsevier. https://doi.org/10.1016/S1570-7946(09)70411-0.

    Google Scholar 

  22. Khatiwada, D., S. Leduc, S. Silveira, and I. McCallum. 2016. Optimizing ethanol and bioelectricity production in sugarcane biorefineries in Brazil. Renewable Energy 85: 371–386. https://doi.org/10.1016/j.renene.2015.06.009.

    CAS  Article  Google Scholar 

  23. Kumar, S., N. Singh, and R. Prasad. 2010. Anhydrous ethanol: A renewable source of energy. Renewable and Sustainable Energy Reviews 14(7): 1830–1844. https://doi.org/10.1016/j.rser.2010.03.015.

    CAS  Article  Google Scholar 

  24. Luyben, W.L. 2013. Distillation design and control using AspenTM simulation, 2nd ed. Hoboken, New Jersey: Wiley.

    Google Scholar 

  25. Ma, Y., J. Weng, Z. Shao, X. Chen, L. Zhu, and Y. Zhao. 2015. Parallel computation method for solving large scale equation-oriented models. In Computer aided chemical engineering, vol. 37, ed. K.V. Gernaey, J.K. Huusom, and R. Gani, 239–244. Amsterdam: Elsevier. https://doi.org/10.1016/B978-0-444-63578-5.50035-9.

    Google Scholar 

  26. Manochio, C., B.R. Andrade, R.P. Rodriguez, and B.S. Moraes. 2017. Ethanol from biomass: A comparative overview. Renewable and Sustainable Energy Reviews 80: 743–755. https://doi.org/10.1016/j.rser.2017.05.063.

    Article  Google Scholar 

  27. Mariller, C. 1951. Distillery: agricultural and industrial. Paris: J.-B. Baillère & Fils. (in French).

    Google Scholar 

  28. Marquini, M.F., D.C. Mariani, A.J.d.A. Meirelles, O.A.A. Santos, and L.M.d.M. Jorge. 2007. Simulation and analysis of an industrial ethanol distillation column system. Acta Scientiarum: Technology 29(1): 23–28. https://doi.org/10.4025/actascitechnol.v29i1.81. (in Portuguese).

    CAS  Article  Google Scholar 

  29. Matugi, K., O. Chiavone-Filho, M.P.A. Ribeiro, R.P. Soares, and R.C. Giordano. 2018. Vapor-liquid equilibrium calculation for simulation of bioethanol concentration from sugarcane. Brazilian Journal of Chemical Engineering 35: 341–352. https://doi.org/10.1590/0104-6632.20180352s20160278.

    CAS  Article  Google Scholar 

  30. Montgomery, D., E. Peck, and G. Vining. 2012. Introduction to linear regression analysis, 5th ed. Hoboken, New Jersey: Wiley-Blackwell.

    Google Scholar 

  31. Morganti, K., M. Almansour, A. Khan, G. Kalghatgi, and S. Przesmitzki. 2018. Leveraging the benefits of ethanol in advanced engine-fuel systems. Energy Conversion and Management 157: 480–497. https://doi.org/10.1016/j.enconman.2017.11.086.

    CAS  Article  Google Scholar 

  32. Nadaleti, W.C., V.A. Lourenço, P.B. Filho, G.B.d. Santos, and G. Przybyla. 2019. National potential production of methane and electrical energy from sugarcane vinasse in Brazil: A thermo-economic analysis. Journal of Environmental Chemical Engineering 8(2): 103422. https://doi.org/10.1016/j.jece.2019.103422.

    CAS  Article  Google Scholar 

  33. OECD/FAO. 2019. OECD-FAO Agricultural Outlook 2019–2028. Organisation for Economic Co-operation and Development (OECD) Publishing, Paris/Food and Agriculture Organization of the United Nations, Rome. https://doi.org/10.1787/agr_outlook-2019-en. Accessed January 21, 2020.

  34. Oliveira, B.G., J.L. Nunes Carvalho, C.E. Pellegrino Cerri, C.C. Cerri, and B.J. Feigl. 2015. Greenhouse gas emissions from sugarcane vinasse transportation by open channel: a case study in Brazil. Journal of Cleaner Production 94: 102–107. https://doi.org/10.1016/j.jclepro.2015.02.025.

    CAS  Article  Google Scholar 

  35. Puga, F.P., and L.B. Castro. 2018. Developed country: Sectoral agendas to reach the goal, 1st ed. Rio de Janeiro: National Bank for Economic and Social Development (BNDES). (in Portuguese).

    Google Scholar 

  36. Sandler, S.I. 2015. Using Aspen plus in thermodynamics instruction: A step-by-step guide. Hoboken, New Jersey: Wiley.

    Google Scholar 

  37. Seader, J.D., E.J. Henley, and D.K. Roper. 2016. Separation process principles: With applications using process simulators, 4th ed. New York: Wiley.

    Google Scholar 

  38. Silva, W.C., E.C.C. Araújo, C.E. Calmanovici, A. Bernardo, and M. Giulietti. 2017. Environmental assessment of a standard distillery using aspen plus®: Simulation and renewability analysis. Journal of Cleaner Production 162: 1442–1454. https://doi.org/10.1016/j.jclepro.2017.06.106.

    Article  Google Scholar 

  39. Silva, A.P.M., J.A.M. Bono, and F.A.R. Pereira. 2014. Application of vinasse to sugarcane crop: effect on soil and stalk yield. Brazilian Journal of Agricultural and Environmental Engineering 18: 38–43. https://doi.org/10.1590/S1415-43662014000100006. (in Portuguese).

    Article  Google Scholar 

  40. Smith, J.M., H.C. Van Ness, M.M. Abbott, and M.T. Swihart. 2017. Introduction to chemical engineering thermodynamics, 8th ed. New York: McGraw-Hill Education.

    Google Scholar 

  41. Souza, W.L.R., C.S. Silva, L.A.C. Meleiro, and M.F. Mendes. 2016. Ethanol dehydration in packed distillation column using glycerol as entrainer: Experiments and hetp evaluation. Brazilian Journal of Chemical Engineering 33: 415–426. https://doi.org/10.1590/0104-6632.20160332s20150341.

    CAS  Article  Google Scholar 

  42. Sydney, E.B., L.A.J. Letti, S.G. Karp, A.C.N. Sydney, L.P.d.S. Vandenberghe, J.C. de Carvalho, et al. 2019. Current analysis and future perspective of reduction in worldwide greenhouse gases emissions by using first and second generation bioethanol in the transportation sector. Bioresource Technology Reports 7: 100234. https://doi.org/10.1016/j.biteb.2019.100234.

    Article  Google Scholar 

  43. Tututi-Avila, S., N. Medina-Herrera, J. Hahn, and A. Jiménez-Gutiérrez. 2017. Design of an energy-efficient side-stream extractive distillation system. Computers & Chemical Engineering 102: 17–25. https://doi.org/10.1016/j.compchemeng.2016.12.001.

    CAS  Article  Google Scholar 

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Acknowledgements

This work was supported by the National Council for Scientific and Technological Development—CNPq (Grant Nos. 304579/2019-0, 308724/2015-1); Agency for the Financing of Studies and Projects—FINEP; Brazilian Federal Agency for Support and Evaluation of Graduate Education—Capes; Research Support Foundation of the State of Alagoas—FAPEAL. The author M.C.S also thanks to the industrial unit located in Alagoas, Brazil, for the process data provided and to the Federal Institute of Alagoas for the permission for doctoral studies.

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Santos, M.C., Albuquerque, A.A., Meneghetti, S.M.P. et al. Property Modeling, Energy Balance and Process Simulation Applied to Bioethanol Purification. Sugar Tech 22, 870–884 (2020). https://doi.org/10.1007/s12355-020-00841-y

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Keywords

  • Bioethanol
  • Distillation
  • Energy balance
  • Property modeling
  • Simulation
  • Steam consumption