Skip to main content
SpringerLink
Log in
Book cover

Handbook of Optimization pp 615–639Cite as

Multi-objective Optimization of Low Density Polyethylene (LDPE) Tubular Reactor Using Strategies of Differential Evolution

  • Chapter

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 38))

Abstract

Multi-objective optimization of industrial low density polyethylene (LDPE) tubular reactor is carried out using improved strategies of multi-objective differential evolution (MODE) algorithm (namely, MODE-III and hybrid-MODE). Two case studies consisting of two-objective optimization and four-objective optimization are considered. In case-1, two objectives namely, maximization of conversion and minimization of the sum of square of normalized side chain concentrations are considered. A set of eleven decision variables, which consists of operating variables, namely, inlet temperature (T in), inlet pressure (P in), the feed flow rates of -oxygen (F o), -solvent (F S), -initiators (F I,1, F I,2), and the five average jacket temperatures (T J,1 - T J,5), are considered. Constraints on maximum temperature attained in the reactor and number average molecular weight are considered. The results of present study show that MODE-III algorithm is able to give consistent results for various control parameters. These results show the ability of the existing algorithm to produce more valuable and practical results that are important to the process plant engineer.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Back, T.: Evolutionary algorithms in theory and practice. Oxford University Press, New York (1996)

    Google Scholar 

  2. Miettinen, K.M.: Nonlinear multiobjective optimization. Kluwer Academic Publishers, Boston (1999)

    MATH  Google Scholar 

  3. Deb, K.: Multi-objective optimization using evolutionary algorithms. John Wiley and Sons Limited, New York (2001)

    MATH  Google Scholar 

  4. Onwubolu, G.C., Babu, B.V.: New optimization techniques in engineering. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  5. Eiben, A.E., Smith, J.E.: Introduction to evolutionary computing. Springer, Hiedelberg (2003)

    MATH  Google Scholar 

  6. Coello Coello, C.A., Lamont, G.B.: Applications of multi-objective evolutionary algorithm: Advances in natural computation, vol. 1. World Scientific Publishing Co. Pte. Ltd, Singapore (2004)

    Book  MATH  Google Scholar 

  7. Tan, K.C., Khor, E.F., Lee, T.H.: Multi-objective evolutionary algorithms and applications. Springer, London (2005)

    Google Scholar 

  8. Biswas, A., Chakraborti, N., Sen, P.K.: A genetic algorithms based multi-objective optimization approach applied to a hydrometallurgical circuit for ocean nodules. Min. Processing. Ext. Metal. Rev. 30, 163–189 (2009)

    Article  Google Scholar 

  9. Biswas, A., Chakraborti, N., Sen, P.K.: Multiobjective optimization of manganese recovery from sea nodules using genetic algorithms. Mat. Manuf. Processes. 24, 22–30 (2009)

    Article  Google Scholar 

  10. Price, K.V., Storn, R.: Differential evolution - a simple evolution strategy for fast optimization. Dr. Dobb’s J. 22, 18–22 (1997)

    Google Scholar 

  11. Babu, B.V.: Process plant simulation. Oxford Press, New York (2004)

    Google Scholar 

  12. Babu, B.V., Angira, R.: Optimal design of an auto-thermal ammonia synthesis reactor. Compu. Che. Engng. 29, 1041–1045 (2005)

    Article  Google Scholar 

  13. Babu, B.V., Angira, R.: Modified differential evolution (MDE) for optimization of non-linear chemical processes. Compu. Che. Engng. 30, 989–1002 (2006)

    Article  Google Scholar 

  14. Goldberg, D.E.: Genetic algorithms in search optimization and machine learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  15. Babu, B.V., Chakole, P.G., Mubeen, J.H.S.: Multi-objective differential evolution (MODE) for optimization of adiabatic styrene reactor. Che. Engng. Sci. 60, 4822–4837 (2005)

    Article  Google Scholar 

  16. Gujarathi, A.M., Babu, B.V.: Multi-objective Optimization of Industrial Styrene Reactor: Adiabatic and Pseudo-isothermal Operation. Che. Engng. Sci. 65, 2009–2026 (2010)

    Article  Google Scholar 

  17. Angira, R.: Evolutionary computation for optimization of selected nonlinear chemical processes. Ph. D. Thesis. Birla Institute of Technology and Science (BITS), Pilani, India (2005)

    Google Scholar 

  18. Gujarathi, A.M., Babu, B.V.: Optimization of Adiabatic Styrene Reactor: A Hybrid Multi-Objective Differential Evolution (H–MODE) Approach. Ind. Engng. Che. Res. 48, 11115–11132 (2009)

    Article  Google Scholar 

  19. Gujarathi, A.M.: Pareto optimal solutions in process design decisions using evolutionary multi-objective optimization. Ph. D. Thesis. Birla Institute of Technology and Science (BITS), Pilani, India (2010)

    Google Scholar 

  20. Gujarathi, A.M., Babu, B.V.: Differential evolution strategies for multi-objective optimization. In: Proceedings of International Conference on Soft Computing for Problem Solving, Roorki, India (2011)

    Google Scholar 

  21. Nelder, J.M., Mead, R.: A Simplex method for function minimization. The. Comput. J. 7, 308–313 (1965)

    MATH  Google Scholar 

  22. Gujarathi, A.M., Babu, B.V.: Hybrid Multi-objective Differential Evolution (H-MODE) for optimization of Polyethylene Terephthalate (PET) reactor. Int. J. Bio-insp. Compu. 2, 213–221 (2010)

    Article  Google Scholar 

  23. Asteasuain, M., Tonelli, S.M., Brandoline, A., Bandoni, J.A.: Dynamic simulation and optimization of tubular polymerization reactors in gPROMS. Compu. Che. Engng. 25, 509–515 (2001)

    Article  Google Scholar 

  24. Brandoline, Capiati, N.J., Farber, J.N., Valles, E.M.: Mathematical model for high-pressure tubular reactor for ethylene polymerization. Ind. Engng. Che. Res. 27, 784–790 (1988)

    Article  Google Scholar 

  25. Brandoline, Valles, E.M., Farber, J.N.: High-pressure tubular reactors for ethylene polymerization optimization aspects. Polym. Engng. Sci. 31, 381–390 (1991)

    Article  Google Scholar 

  26. Kiparissides, C., Verros, G., MacGregor, J.F.: Mathematical modeling optimization and quality control of high pressure ethylene polymerization reactors. Polymer Reviews C33, 437–527 (1993)

    Google Scholar 

  27. Agrawal, N., Rangaiah, G.P., Ray, A.K., Gupta, S.K.: Multi-objective optimization of the operation of an industrial low-density polyethylene tubular reactor using genetic algorithm and its jumping gene adaptations. Ind. Engng. Che. Res. 45, 3182–3199 (2006)

    Article  Google Scholar 

  28. Brandoline, L.M., Urgin, P.E., Capiati, N.J.: High-pressure polymerization of ethylene: an improved mathematical model for industrial tubular reactors. Polym. React. Eng. 4, 193–241 (1996)

    Google Scholar 

  29. Gujarathi, A.M., Babu, B.V.: Advances in Optimization and Simulation of Low Density Polyethylene (LDPE) Tubular Reactor. In: Haghi, A.K. (ed.) Handbook of Research on Chemoinformatics and Chemical Engineering, pp. 109–138. Nova Science Publishers, USA (2010)

    Google Scholar 

  30. Gupta, S.K., Kumar, A., Krishnamurthy, M.V.G.: Simulation of tubular low-density polyethylene reactor. Polym. Engng. Sci. 25, 37–47 (1985)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chemical Engineering Department, Birla Institute of Technology and Science (BITS), Pilani, 333 031, Rajasthan, India

    Ashish M. Gujarathi

  2. JK Lakshmipat University (JKLU), Jaipur, 302 026, Rajasthan, India

    B. V. Babu

Authors
  1. Ashish M. Gujarathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. V. Babu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashish M. Gujarathi .

Editor information

Editors and Affiliations

  1. Technical University of Ostrava, 17. listopadu 15, Ostrava-Porub, 708 33, Czech Republic

    Ivan Zelinka

  2. , Faculty of Electrical Engineering and, Technical University of Ostrava, 17. listopadu 15, Ostrava-Poruba, 708 33, Czech Republic

    Václav Snášel

  3. Auburn, 98071, USA

    Ajith Abraham

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Gujarathi, A.M., Babu, B.V. (2013). Multi-objective Optimization of Low Density Polyethylene (LDPE) Tubular Reactor Using Strategies of Differential Evolution. In: Zelinka, I., Snášel, V., Abraham, A. (eds) Handbook of Optimization. Intelligent Systems Reference Library, vol 38. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30504-7_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30504-7_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30503-0

  • Online ISBN: 978-3-642-30504-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation