Skip to main content
SpringerLink
Log in

Optimization of Performance Model of Ethyl Acetate Saponification Using Multiple Regression Analysis

  • Various Technological Processes
  • Published:
Russian Journal of Applied Chemistry Aims and scope Submit manuscript

Abstract

The purpose of the current study is to optimize the batch reactor performance model using multiple regression analysis for saponification of ethyl acetate by sodium hydroxide. Reaction temperature, reactor volume, agitation rate and reactants initial concentration were the main parameters examined including their interaction effect. Selected process response was the reaction conversion with respect of NaOH. Regression analysis was used to screen out the insignificant factors and the reaction temperature and volume were found to have insignificant effect on the response at the 5% selected significance level (α = 0.05). As a result of multiple regression analysis, agitation rate and reactants concentration were found to be significant operating parameters. The dependence of reaction conversion (response) on agitation rate and concentration was explained by a second order polynomial model and it was concluded that regression model with second order polynomial was good enough to fit the experimental data. The maximum conversion (99.5%) was obtained under optimum operating conditions of agitation rate (70 rpm) and reactants concentration (0.05 M) as evident from surface contours.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Montgomery, D.C., Design and Analysis of Experiments, New York: John Wiley and Sons, 1991, pp.270–569.

    Google Scholar 

  2. Zivorad, R.L., Design of Experiments in Chemical Engineering, Weinheim: Wiley-VCH, 2004.

    Google Scholar 

  3. Carr, J.M. and McCraken, E.A., Chem. Eng. Prog., 1960, vol.56 (11), pp. 56–61.

    Google Scholar 

  4. Asprey, S.P. and Macchietto, S., Comput. Chem. Eng., 2000, vol. 24, pp. 1261–1267.

    Article  CAS  Google Scholar 

  5. Keyvanloo, K., Towghi, J., Sadrameli, S.M., and Mohamadalizadeh, A., J. Anal. Appl. Pyrolysis, 2010, vol. 87, p. 224–230.

    Article  CAS  Google Scholar 

  6. Bursali, N., Ertunc, S., and Akay, B., Chem. Eng. Process., 2006, vol. 45, pp. 980–989.

    Article  CAS  Google Scholar 

  7. Zofia, V.L., Comput. Ind., 1998, vol.36, pp. 279–300.

    Article  Google Scholar 

  8. Atkinson, A.C., Bagacka, B., and Bogaki, M.B., Chemom. Intell. Lab Syst., 1998, vol. 43, pp.185–198.

    Article  CAS  Google Scholar 

  9. Mata-Segreda, J.F., J. Am. Chem. Soc., 2002, vol. 124 (10), pp. 2259–2262.

    Article  CAS  PubMed  Google Scholar 

  10. Ortiz, M.I., Romero, A., and Irabien, A., Thermochimica Acta, 1989, vol. 141, pp.169–180.

    Article  CAS  Google Scholar 

  11. Garu, M.G., Nougues, J.M., and Puigjaner, L., 2002. Chem. Eng. J. 88, pp. 225–232.

    Article  Google Scholar 

  12. Danish, M., Al Mesfer, M.K., and Rashid, M.M., Int. J. Eng. Res., 2015, vol. 5(2), pp.74–78.

    Google Scholar 

  13. Kuheli, D., Sahoo, P., Saibaba, M., Murali, N., and Swaminthan, P., Int. J. Chem. Kinet., 2011, vol. 43, pp.648–656.

    Article  CAS  Google Scholar 

  14. Mukhtar, A., Shafiq, U., Khan, A.F., Qadir, H.A., and Qizilbash, M., Res. J. Chem. Sci., 2015, vol. 5(11), pp. 46–50.

    CAS  Google Scholar 

  15. Schneider, M.A. and Stoessel, F., Chem. Eng. J., 2005, vol. 115, pp.73–83.

    Article  CAS  Google Scholar 

  16. Tsujikawa, H. and Inoue, I., Bull. Chem. Soc. Jpn, 1966, vol. 39(9), pp.1837–1842.

    Article  CAS  Google Scholar 

  17. Theodorou, V., Skobridis, K., Tzakos, A.G., and Ragoussis, V., Tetrahedron Lett., 2007, vol. 48, pp. 8230–8233.

    Article  CAS  Google Scholar 

  18. Wijayarathne, U.P.L. and Wasalathilake, K.C., J. Chem. Eng. Process Technol., 2014, vol. 5(6), pp. 1–8.

    Google Scholar 

  19. Ahmad, A., Ahmad, M.I., Younas, M., Khan, H., and Shah, M.H., Iran. J. Chem. Chem. Eng., 2013, vol. 32(4), pp. 33–44.

    CAS  Google Scholar 

  20. Ullah, I., Ahmad, M.I., and Younas, M., Pak. J.Eng. & Appl. Sci., 2015, vol. 16, pp. 84–92.

    Google Scholar 

  21. Li, H., M. Ying., Li, Xingang., and Gao, X., Chem. Eng. Res. Des., 2016, vol.111, pp. 479–491.

    Article  CAS  Google Scholar 

  22. Al-Mesfer, M., Int J. Chem. Reactor Eng., 2017. doi: 10.1515/ijcre-0174

    Google Scholar 

  23. Walker, J., Proc. R Soc A, 1906, pp. 78, pp. 157–160.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Chemical Engineering, King Khalid University, Abha, 61411, Saudi Arabia

    M. K. Al Mesfer & M. Danish

  2. College of Applied Science, King Khalid University, Abha, 61411, Saudi Arabia

    M. M. Alam

Authors
  1. M. K. Al Mesfer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Danish
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. M. Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Danish.

Additional information

The text was submitted by the authors in English.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al Mesfer, M.K., Danish, M. & Alam, M.M. Optimization of Performance Model of Ethyl Acetate Saponification Using Multiple Regression Analysis. Russ J Appl Chem 91, 1895–1904 (2018). https://doi.org/10.1134/S1070427218110228

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1070427218110228

Keywords

Search

Navigation