Skip to main content
SpringerLink
Log in

Adaptive Search for Optimum in a Problem of Oil Stabilization Process Design

  • Conference paper
Book cover Adaptive Computing in Design and Manufacture VI

Abstract

The formulation of a model for an industrial problem in process design leads to an optimization problem with a small, implicitly defined, feasible region, a region which is difficult to identify a priori. The difficulties of obtaining a good solution with conventional optimization methods are discussed. A novel method is proposed, based on the paradigm of evolutionary computing and a two stage search: the first stage aims to find a set of points covering the feasible region and the second stage is a search for the optimum, modelling the evolution of the population, the set of points, found in the first stage. The results of profit optimization for an industrial case study are presented.

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

Access this chapter

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Törn and A. Žilinskas. Global optimization. Lecture Notes in Computer Science, 350:1–255, 1989.

    Article  Google Scholar 

  2. Z. Michalevich. Genetic Algorithms + Data Structures = Evolution Programs. Springer, NY, 1996.

    Google Scholar 

  3. H.-P. Schwefel. Numerical Optimization of Computer Models. J.Wiley, NY, 1981.

    MATH  Google Scholar 

  4. I. Parmee. Evolutionary and Adaptive Computing in Engineering Design. Springer, NY, 2001.

    Book  Google Scholar 

  5. J. E. Smith. Genetic algorithms. In Panos M. Pardalos and H. Edwin Romeijn, editors, Handbook of Global Optimization Volume 2, Nonconvex optimization and its applications, pages 275–362. Kluwer Academic Publishers, 2002.

    Google Scholar 

  6. K. Miettinen, M. Mäkelä, and J. Toivanen. Numerical comparison of some penalty-based constrainet handling techniques in genetic algorithms. Journal of Global Optimization, 27:427–446, 2003.

    Article  MATH  Google Scholar 

  7. R. Smith. Efficient Monte Carlo procedures for generating points uniformly distributed over bounded regions. Operations Research, 32:1296–1308, 1984.

    Article  MathSciNet  MATH  Google Scholar 

  8. E. McCarthy, E. S. Fraga, and J. W. Ponton. An automated procedure for multi-component product separation synthesis. Computers & Chemical Engineering, 22(7):S77–S84, 1998.

    Article  Google Scholar 

  9. K. Wang, A. Salhi, and E. S. Fraga. Process design optimisation using embedded hybrid visualisation and data analysis techniques within a genetic algorithm optimisation framework. Chemical Engineering and Processing, 43(5):657–669, 2004.

    Article  Google Scholar 

  10. M. Gen and R. Cheng. Genetic Algorithms and Engineering Optimization. J. Wiley, NY, 2000.

    Google Scholar 

  11. L. Kocis and W. Whiten. Computational investigation of low-discrepancy sequences. ACM Trans. on Mathematical Software, 23:266–294, 1997.

    Article  MATH  Google Scholar 

  12. E. S. Fraga, K. Wang, and A. Salhi. Interactivity and automated process design. Chemical Engineering Technology, 26(8):823–827, 2003.

    Article  Google Scholar 

  13. P. J. M. van Laarhoven and E. H. L. Aarts. Simulated annealing: Theory and applications. Kluwer Academic Publishers (Dordrecht), 1987.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vytautas Magnus University, 8 Vileikos str., Kaunas, LT 3035, Lithuania

    A. Žilinskas, A. Mackutė & A. Varoneckas

  2. School of Mathematics, Cardiff University, Cardiff, CF24 4YH, UK

    A. Žilinskas

  3. Centre for Process Systems Engineering Department of Chemical Engineering, UCL (University College London), London, WC1E 7JE, UK

    E. S. Fraga

Authors
  1. A. Žilinskas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. S. Fraga
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Mackutė
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Varoneckas
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. CEMS, University of the West of England, Coldharbour Lane, Bristol, BS16 1QY, UK

    I. C. Parmee PhD, CEng, MASCE, MEI

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag London

About this paper

Cite this paper

Žilinskas, A., Fraga, E.S., Mackutė, A., Varoneckas, A. (2004). Adaptive Search for Optimum in a Problem of Oil Stabilization Process Design. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture VI. Springer, London. https://doi.org/10.1007/978-0-85729-338-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-338-1_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-829-9

  • Online ISBN: 978-0-85729-338-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation