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Process Plant Layout Optimization: Equipment Allocation

  • Gleb Belov
  • Tobias Czauderna
  • Maria Garcia de la Banda
  • Matthias Klapperstueck
  • Ilankaikone Senthooran
  • Mitch Smith
  • Michael Wybrow
  • Mark Wallace
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11008)

Abstract

Designing the layout of a chemical plant is a complex and important task. Its main objective is to find a most economical spatial arrangement of the equipment and associated pipes that satisfies construction, operation, maintenance and safety constraints. The problem is so complex it is still solved manually, taking multiple engineers many months (or even years) to complete. This paper provides (a) the most comprehensive model ever reported in the literature for spatially arranging the equipment, and (b) a Large Neighbourhood Search framework that enables complete solvers explore much larger neighbourhoods than previous approaches to this problem. The two contributions are part of a system being developed in collaboration with Woodside Energy Ltd. for arranging their Liquefied Natural Gas plants. The results are indeed so promising that Woodside are actively exploring its commercialisation.

Notes

Acknowledgements

Funded by Woodside Energy Ltd. and the Australian Research Council grant DP180100151. We thank our Woodside collaborators, particularly Solomon Faka and Michelle Frayne, for the many useful discussions.

References

  1. 1.
    AMEC Paragon launches optimized FEED design process. Zeus Technology Magazine, 4(2), 1–3 (2009)Google Scholar
  2. 2.
    Beldiceanu, N., Carlsson, M., Demassey, S., Petit, T.: Global constraint catalogue: Past, present and future. Constraints 12(1), 21–62 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Belov, G., et al.: An optimization model for 3D pipe routing with flexibility constraints. In: Beck, J.C. (ed.) CP 2017. LNCS, vol. 10416, pp. 321–337. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66158-2_21CrossRefGoogle Scholar
  4. 4.
    Belov, G., Stuckey, P.J., Tack, G., Wallace, M.: Improved linearization of constraint programming models. In: Rueher, M. (ed.) CP 2016. LNCS, vol. 9892, pp. 49–65. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-44953-1_4CrossRefGoogle Scholar
  5. 5.
    Chu, G.G.: Improving combinatorial optimization. Ph.D. thesis (2011)Google Scholar
  6. 6.
    Guirardello, R., Swaney, R.E.: Optimization of process plant layout with pipe routing. Comput. Chem. Eng. 30(1), 99–114 (2005)CrossRefGoogle Scholar
  7. 7.
    Gurobi Optimization, Inc.: Gurobi Optimizer Reference Manual Version 7.5. Gurobi Optimization, Houston, Texas (2017)Google Scholar
  8. 8.
    IBM: IBM ILOG CPLEX Optimization Studio. CPLEX User’s Manual (2017)Google Scholar
  9. 9.
    Kar, Y.T., Shi, G.L.: A hierarchical approach to the facility layout problem. Int. J. Prod. Res. 29(1), 165–184 (1991)CrossRefGoogle Scholar
  10. 10.
    Mecklenburgh, J.C.: Process Plant Layout. Halsted Press; Wiley, New York (1985)Google Scholar
  11. 11.
    Nethercote, N., et al.: MiniZinc: Towards a standard CP modelling language. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 529–543. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74970-7_38CrossRefGoogle Scholar
  12. 12.
    Peters, M.S., Timmerhaus, K.D.: Plant Design and Economics for Chemical Engineers, 5th edn. McGraw-Hill Book Company, New York (2004)Google Scholar
  13. 13.
    Pisinger, D., Sigurd, M.M.: Using decomposition techniques and constraint programming for solving the two-dimensional bin-packing problem. INFORMS J. Comput. 19(1), 36–51 (2007)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Sakti, A., Zeidner, L., Hadzic, T., Rock, B.S., Quartarone, G.: Constraint programming approach for spatial packaging problem. In: Quimper, C.-G. (ed.) CPAIOR 2016. LNCS, vol. 9676, pp. 319–328. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-33954-2_23CrossRefzbMATHGoogle Scholar
  15. 15.
    Schulte, C., Tack, G., Lagerkvist, M.Z.: Modeling and programming with Gecode (2017). www.gecode.org
  16. 16.
    Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, M., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998).  https://doi.org/10.1007/3-540-49481-2_30CrossRefGoogle Scholar
  17. 17.
    Xu, G., Papageorgiou, L.G.: A construction-based approach to process plant layout using mixed-integer optimization. Ind. Eng. Chem. Res. 46(1), 351–358 (2007)CrossRefGoogle Scholar
  18. 18.
    Xu, G., Papageorgiou, L.G.: Process plant layout using an improvement-type algorithm. Chem. Eng. Res. Des. 87(6), 780–788 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Gleb Belov
    • 1
  • Tobias Czauderna
    • 1
  • Maria Garcia de la Banda
    • 1
  • Matthias Klapperstueck
    • 1
  • Ilankaikone Senthooran
    • 1
  • Mitch Smith
    • 2
  • Michael Wybrow
    • 1
  • Mark Wallace
    • 1
  1. 1.Faculty of Information TechnologyMonash UniversityMelbourneAustralia
  2. 2.Woodside Energy Ltd.PerthAustralia

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