Correlation between the porosity of γ-Al2O3 and the performance of CuO–ZnO–Al2O3 catalysts for CO2 hydrogenation into methanol

Abstract

The influence of the porosity of γ-Al2O3 on the performance of CuO–ZnO–Al2O3 catalysts for methanol synthesis from H2 + CO2 mixture was studied. Various types of γ-Al2O3 with different surface areas (from 130 to 280 m2/g) and pore sizes (from 3 to 11 nm) were investigated. N2 adsorption, XRD, TPR studies and grand canonical Monte Carlo simulation were utilized to determine the correlation between their physico-chemical properties and catalytic performance. It was shown that the crystallite size of CuO (determined by XRD) and BET surface area of supports are not the key factors for methanol productivity. The TPR profiles of catalysts demonstrated a direct relationship between CuO–ZnO interaction with their catalytic performance. Interestingly, samples with the uniform pore size of 5 nm exhibit a higher CuO–ZnO interaction and the highest methanol yield. In addition, at this pore size, simulation results showed that the ratio of H2 and CO2 inside the γ-Al2O3 pore was 1.5, which could be an appropriate feed ratio for high methanol productivity.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. 1.

    Höök M, Tang X (2013) Energy Policy 52:797–809

    Article  Google Scholar 

  2. 2.

    Heede R, Oreskes N (2016) Glob Environ Chang 36:12–20

    Article  Google Scholar 

  3. 3.

    Marcos FCF, Assaf JM, Assaf EM (2017) Catal Today 289:173–180

    CAS  Article  Google Scholar 

  4. 4.

    Porosoff MD, Yan B, Chen JG (2016) Energy Environ Sci 9:62–73

    CAS  Article  Google Scholar 

  5. 5.

    Ihm SK, Park YK, Jeon JK, Park KC, Lee DK (1998) Stud Surf Sci Catal 114:505–508

    CAS  Article  Google Scholar 

  6. 6.

    Behrens M (2014) Angew Chem Int Ed 53:12022–12024

    CAS  Article  Google Scholar 

  7. 7.

    Meshkini F, Taghizadeh M, Bahmani M (2010) Fuel 89:170–175

    CAS  Article  Google Scholar 

  8. 8.

    Olah GA, Prakash GKS, Goeppert A (2011) J Am Chem Soc 133:12881–12898

    CAS  Article  Google Scholar 

  9. 9.

    Zangeneh FT, Sahebdelfar S, Ravanchi MT (2011) J Nat Gas Chem 20:219–231

    CAS  Article  Google Scholar 

  10. 10.

    Jadhav SG, Vaidya PD, Bhanage BM, Joshi JB (2014) Chem Eng Res Des 92:2557–2567

    CAS  Article  Google Scholar 

  11. 11.

    Arena F, Mezzatesta G, Zafarana G, Trunfio G, Frusteri F, Spadaro L (2013) J Catal 300:141–151

    CAS  Article  Google Scholar 

  12. 12.

    Fujita S, Moribe S, Kanamori Y, Kakudate M, Takezawa N (2001) Appl Catal A 207:121–128

    CAS  Article  Google Scholar 

  13. 13.

    Guo XJ, Li LM, Liu SM, Bao GL, Hou WH (2007) J Fuel Chem Technol 35:329–333

    CAS  Article  Google Scholar 

  14. 14.

    Wu J, Saito M, Mabuse H (2000) Catal Lett 68:55–58

    CAS  Article  Google Scholar 

  15. 15.

    Lei H, Hou Z, Xie J (2016) Fuel 164:191–198

    CAS  Article  Google Scholar 

  16. 16.

    Fujitani T, Nakamura J (1998) Catal Lett 56:119–124

    CAS  Article  Google Scholar 

  17. 17.

    Choi Y, Futagami K, Fujitani T, Nakamura J (2001) Appl Catal A 208:163–167

    CAS  Article  Google Scholar 

  18. 18.

    Jansen WPA, Beckers J, vd Heuvel JC, vd Gon AD, Bliek A, Brongersma HH (2002) J Catal 210:229–236

    CAS  Article  Google Scholar 

  19. 19.

    Grunwaldt JD, Molenbroek AM, Topsøe NY, Topsøe H, Clausen BS (2000) J Catal 194:452–460

    CAS  Article  Google Scholar 

  20. 20.

    Phongamwong T, Chantaprasertporn U, Witoon T, Numpilai T, Poo-arporn Y, Limphirat W, Donphai W, Dittanet P, Chareonpanich M, Limtrakul J (2017) Chem Eng J 316:692–703

    CAS  Article  Google Scholar 

  21. 21.

    Ahouari H, Soualah A, Le Valant A et al (2013) Reac Kinet Mech Cat 110:131–145

    CAS  Article  Google Scholar 

  22. 22.

    Jeong H, Cho CH, Kim TH (2012) Reac Kinet Mech Cat 106:435–443

    CAS  Article  Google Scholar 

  23. 23.

    Tursunov O, Kustov L, Tilyabaev Z (2017) J Taiwan Inst Chem Eng 78:416–422

    CAS  Article  Google Scholar 

  24. 24.

    Ren H, Xu CH, Zhao HY, Wang YX, Liu J (2015) J Ind Eng Chem 28:261–267

    CAS  Article  Google Scholar 

  25. 25.

    Donphai W, Piriyawate N, Witoon T, Jantaratana P, Varabuntoonvit V, Chareonpanich M (2016) J CO2 Util 16:204–211

    CAS  Article  Google Scholar 

  26. 26.

    Karelovic A, Bargibant A, Fernández C, Ruiz P (2012) Catal Today 197:109–118

    CAS  Article  Google Scholar 

  27. 27.

    Witoon T, Bumrungsalee S, Chareonpanich M, Limtrakul J (2015) Energy Convers Manag 103:886–894

    CAS  Article  Google Scholar 

  28. 28.

    Digne M, Sautet P, Raybaud P, Euzen P, Toulhoat H (2002) J Catal 211:1–5

    CAS  Article  Google Scholar 

  29. 29.

    Plimpton S, Crozier P, Thompson A (2007) LAMMPS-large-scale atomic/molecular massively parallel simulator. Sandia National Laboratories, Albuquerque

    Google Scholar 

  30. 30.

    Potoff JJ, Siepmann JI (2001) AlChE J 47:1676–1682

    CAS  Article  Google Scholar 

  31. 31.

    Cygan RT, Liang J-J, Kalinichev AG (2004) J Phys Chem B 108:1255–1266

    CAS  Article  Google Scholar 

  32. 32.

    Trinh TT, Vlugt TJ, Hagg MB, Bedeaux D, Kjelstrup S (2013) Front Chem 1:38

    Article  Google Scholar 

  33. 33.

    Yeh I-C, Lenhart JL, Rinderspacher BC (2015) J Phys Chem C 119:7721–7731

    CAS  Article  Google Scholar 

  34. 34.

    Harris KDM, Tremayne M, Kariuki BM (2001) Angew Chem Int Ed 40:1626–1651

    CAS  Article  Google Scholar 

  35. 35.

    McCusker LB, Von Dreele RB, Cox DE, Louer D, Scardi P (1999) J Appl Crystallogr 32:36–50

    CAS  Article  Google Scholar 

  36. 36.

    Koizumi N, Jiang X, Kugai J, Song C (2012) Catal Today 194:16–24

    CAS  Article  Google Scholar 

  37. 37.

    Nishida K, Atake I, Li D, Shishido T, Oumi Y, Sano T, Takehira K (2008) Appl Catal A 337:48–57

    CAS  Article  Google Scholar 

  38. 38.

    Fierro G, Jacono ML, Inversi M, Porta P, Cioci F, Lavecchia R (1996) Appl Catal A 137:327–348

    CAS  Article  Google Scholar 

  39. 39.

    Bahmani M, Vasheghani Farahani B, Sahebdelfar S (2016) Appl Catal A 520:178–187

    CAS  Article  Google Scholar 

  40. 40.

    Natesakhawat S, Lekse JW, Baltrus JP, Ohodnicki PR Jr, Howard BH, Deng X, Matranga C (2012) ACS Catal 2:1667–1676

    CAS  Article  Google Scholar 

  41. 41.

    Saeidi S, Amin NAS, Rahimpour (2014) J. CO2 Util. 5:66–81

    CAS  Article  Google Scholar 

Download references

Acknowledgement

This work was carried out at PVPro, VPI and supported by Vietnam National Oil and Gas Group (03/NCCB(PVPro)/2012/HĐ-NCKH) and the Ministry of Industry and Trade of Vietnam (DT.03.12/NLSH).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Nguyen Le-Phuc.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Le-Phuc, N., Van Tran, T., Thuy, P.N. et al. Correlation between the porosity of γ-Al2O3 and the performance of CuO–ZnO–Al2O3 catalysts for CO2 hydrogenation into methanol. Reac Kinet Mech Cat 124, 171–185 (2018). https://doi.org/10.1007/s11144-017-1323-7

Download citation

Keywords

  • CO2 hydrogenation
  • γ-Al2O3
  • CuO–ZnO interaction
  • TPR
  • Pore size distribution
  • Monte Carlo