MULTIVARIATE STATISTICAL OPTIMIZATION OF THE ETHANOL FUEL DEHYDRATION PROCESS USING IONIC LIQUIDS

Original scientific paper

Authors

  • CLÁUDIA JÉSSICA DA SILVA CAVALCANTI Department of Chemical Engineering, Federal University of Pernambuco, Recife-PE, Brazil
  • JOÃO PAULO DA SILVA QUEIROZ Department of Chemical Engineering, Federal University of São Carlos, São Carlos-SP, Brazil
  • LUIZ STRAGEVITCH Department of Chemical Engineering, Federal University of Pernambuco, Recife-PE, Brazil
  • FLORIVAL RODRIGUES DE CARVALHO Department of Chemical Engineering, Federal University of Pernambuco, Recife-PE, Brazil
  • MARIA FERNANDA PIMENTEL Department of Chemical Engineering, Federal University of Pernambuco, Recife-PE, Brazil

DOI:

https://doi.org/10.2298/CICEQ200410035C

Keywords:

bioethanol, desirability, energy, extractive distillation, ionic liquid, optimization

Abstract

In this work, the ethanol fuel dehydration process was optimized using the Aspen Plus® simulator and a multivariate statistical technique based on the desirability function. The suitability of the ionic liquids 1-methylimidazolium chloride ([Mim][Cl]), 1-ethyl-3-methylimidazolium chloride ([Emim][Cl]), 1-butyl-
-3-methylimidazolium chloride ([Bmim][Cl]) and 1-hexyl-3-methylimidazolium chloride ([Hmim][Cl]), as extractive distillation entrainers, was also evaluated and compared to the conventional solvents, ethylene glycol and cyclohexane. Among the solvents studied, [Mim][Cl] required the lowest energy con­sump­tion, about 8% less energy use when compared to the optimized process using ethylene glycol. The multivariate statistical techniques employed were effective in the optimization of the extractive distillation processes as the process energy consumption could be minimized while achieving ethanol purity in agreement with the current specifications as well as obtaining a high solvent recovery. With the desirability approach it was possible to improve the process performance with little or no modification of existing processing plants.

Author Biography

  • JOÃO PAULO DA SILVA QUEIROZ, Department of Chemical Engineering, Federal University of São Carlos, São Carlos-SP, Brazil

    In this work, the ethanol fuel dehydration process was optimized using the Aspen Plus® simulator and a multivariate statistical technique based on the desirability function. The suitability of the ionic liquids 1-methylimidazolium chloride ([Mim][Cl]), 1-ethyl-3-methylimidazolium chloride ([Emim][Cl]), 1-butyl-
    -3-methylimidazolium chloride ([Bmim][Cl]) and 1-hexyl-3-methylimidazolium chloride ([Hmim][Cl]), as extractive distillation entrainers, was also evaluated and compared to the conventional solvents, ethylene glycol and cyclohexane. Among the solvents studied, [Mim][Cl] required the lowest energy con­sump­tion, about 8% less energy use when compared to the optimized process using ethylene glycol. The multivariate statistical techniques employed were effective in the optimization of the extractive distillation processes as the process energy consumption could be minimized while achieving ethanol purity in agreement with the current specifications as well as obtaining a high solvent recovery. With the desirability approach it was possible to improve the process performance with little or no modification of existing processing plants.

References

J. Belincanta, J.A. Alchorne, M.T. Silva, Braz. J. Chem. Eng. 33 (2016) 1091-1102

Renewable Fuels Association (RFA). Leading the U.S. Ethanol Industry: 2016 (http://www.ethanolrfa.org/res-ources/industry/ statistics/)

L.C.B.A. Bessa, F.R.M. Batista, A.J.A. Meirelles, Energy 45 (2012) 603−612

G.H.S.F. Ponce, M. Alves, J.C.C. Miranda, R.M. Filho, M.R.W. Maciel, Chem. Eng. Res. Des. 95 (2015) 55−63

R. Palacios-Bereche, A.V. Ensinas, M. Modesto, S.A. Nebra, Energy 82(2015) 1−12

A. Chianese, F. Zinnamosca, Chem. Eng. J. Netherlands (1990) 59-65

A. Meirelles, S. Weiss, H.J. Herfurth, Chem. Technol. Biotechnol. 53 (1992) 181-188

R. Pinto, M. Wolf-Maciel, L. Lintomen, Comput. Chem. Eng. 24 (2000) 1689-1694

J. Fu, ‎Ind. Eng. Chem. Res. 43 (2004) 1274-1278

M.A.S.S. Ravagnani, M.H.M. Reis, R. Maciel Filho, M. R. Wolf-Maciel, Process Saf. Environ. 88 (2009) 67-73

L.C. Branco, Sociedade Portuguesa de Química, Lisboa, 1 (2015) 15-22

M. Seiler C. Jork, A. Kavarnou, W. Arlt, R. Hirsch, AIChE J. 50 (2004) 2439-2454

C. Jork, M. Seiler, Y.A. Beste, W. Arlt, ‎J. Chem. Eng. Data (2004) 852-857

N. Calvar, B. González, E. Gómez, Á. Domínguez, J.. Chem. Eng. Data (2006) 2178-2181

X.C. Jiang, J.F. Wang, C.X. Li, L.M. Wang, Z.H. Wang, J. Chem. Thermodyn. (2007) 841-846

L. Zhang, Y. Ge, D. Ji, ‎J. Chem. Eng. Data (2009) 2322-

–2329

J.J. Figueroa, B.H. Lunelli, R. Maciel Filho, M.R. Wolf Maciel, Procedia Eng. (2012) 1016-1026

Z. Zhu, Y. Ri, M. Li, H. Jia, Y. Wang, Y. Wang, Chem. Eng. Process, China 1 (2016) 190-198

T.F. Edgar, D.M. Himmelblau, L.S. Lasdon, Optimization Of Chemical Processes, McGraw-Hill International, 2nd ed., 2001

K. Deb, Optimization For Engineering Design : Algorithms And Examples, Phi Learning Private Limited, New Delhi, 2nd ed., 2012

M. Martín, I.E. Grossmann, AIChE J. 57 (2011) 3408-3428

R. Brunet, G. Guillén-Gosálbez, L. Jiménez, AIChE J. 60 (2013) 500-506

P. Kanchanalai, R.P. Lively, M.J. Realff, Y. Kawajiri, Ind. Eng. Chem. Res. 52 (2013) 11132-11141

M.B. Franke, Comput. Chem. Eng. 89 (2016) 204-221

X.L. Yang, J.D. Ward, Ind. Eng. Chem. Res. 57 (2018), 11050-11060

A. Yang, H. Zou, I. Chien, D. Wang, S. Wei, J. Ren, W.; Shen, Ind. Eng. Chem. Res. 58 (2019) 7265-7283

Y. Cui, Z. Zhang, X. Shi, C. Guang, J. Gao, Sep. Purif. Technol. 236 (2020) 116303

R.H. Myers, D.C. Montgomery, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Wiley Series in Probability and Statistics, New York, 2016

A.M. Uyazán, I.D. Gil, J. Aguilar, G. Rodríguez, L.A. Caicedo, Ing Invest. Bogotá 26 (2006) 45-50

I.D. Gil, A.M. Uyazán, J. Aguilar, G. Rodríguez, L.A. Caicedo, Braz. J. Chem. Eng. (2008) 207-215

I.D. Gil, J. M. Gómez, G. Rodríguez, Comput. Chem. Eng. (2012) 129-142

E. Ebrahimiaqda, K.L. Ogden, ACS Sustain. Chem. Eng, 5 (2017) 6854-6862

M.A. Bezerra, S.L.C. Ferreira, C.G. Novaes, A.M.P. Santos, G.S. Valassques, U.M.F.M. Cerqueira, J.P.S. Alves, Talanta (2018) 941–959

G. Derringer, R.J. Suich, Qual. Technol. 12 (1980) 214-219

C. Buratti, M. Barbanera, E. Lascaro, F. Cotana, Waste Manage. 71 (2017) 523-534

L. Mesa, Y. Martínez, E. Barrio, E. González, Appl. Energy (2017) 299-311

I.D.G. Chaves, J.R.G. López, J.L.G. Zapata, A.L. Robayo, G.R. Niño, Process Analysis and Simulation in Chemical Engineering, Springer, 1st ed., London, 2016

H. Renon, J. M. Prausnitz, (). Local compositions in thermodynamic excess functions for liquid mixtures. AIChE J. 14 (1968) 135-144

E.C. Carlson, Chem. Eng. Prog. 92 (1996) 35-46

Y. Ge, L. Zhang, X. Yuan, W. Geng, J. Ji, ‎J. Chem. Thermodyn. 40( 2008) 1248-1252

N. Calvar, B. González, E. Gómez, G. Domínguez, J. Chem. Eng. Data 1 (2009) 1004-1008

J.N.C. Lopes, A.A.H. Pádua, ‎J. Phys. Chem. 110 (2006) 19586-19592

GUIDECHEM - Chemical Trading Guide, Chemical Dictionary: CAS No. 35487-17-3, https://www.guide-chem.com/encyclopedia/1h-imidazole-1-methyl-hydrochl-dic44712.html#Properties, (accessed on July 2020)

S. A. Bolkan J. T. Yoke, J. Chem. Eng. Data 31 (1986)194-197

J.G. Huddleston, A.E. Visser, W.M. Reichert, H.D. Willauer, G.A. Broker R.D. Rogers, Green Chem. 3 (2001) 156-164

E. Gomez N. Calvar, I. Dominguez, A. Dominguez,Phys. Chem. Liq. 44 (2006) 409-417

C. Shen, X. Li, Y. Lu, C. Li, J. Chem. Thermodyn. 43 (2011) 1748-1753

E.R.P. Filho, Editora UFSCar, 1ª. Ed., São Carlos, 2015. p. 88

B. Barros Neto, I.S. Scarminio, R.E. Bruns, Bookman, Porto Alegre, 4. ed., 2010, p. 414

A. Singh, G. P. Rangaiah, Ind. Eng. Chem. Res. 56 (2017) 5147-5163

G. Li, P. Bai, Ind. Eng. Chem. Res. 51 (2012), 2723−2729

I.D. Gil, L.C. García, G. Rodríguez, Braz. J. Chem. Eng. 31 (2014) 259−270

P. García-Herreros, J.M. Gómez, I.D. Gil, G. Rodríguez,, Ind. Eng. Chem. Res. 50 (2011) 3977−3985

L.-Y. Sun, X.-W. Chang, C.-X. Qi, Q.-S. Li, Sep. Sci. Technol. 46 (2011) 1365−1375.

Vol 27 No 2

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Published

14.07.2021

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Vol. 27 No. 2 (2021)

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How to Cite

MULTIVARIATE STATISTICAL OPTIMIZATION OF THE ETHANOL FUEL DEHYDRATION PROCESS USING IONIC LIQUIDS: Original scientific paper. (2021). Chemical Industry & Chemical Engineering Quarterly, 27(2), 165-176. https://doi.org/10.2298/CICEQ200410035C

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