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Global Optimisation of General Process Models

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Global Optimization in Engineering Design

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 9))

Abstract

This paper is concerned with the application of deterministic methods for global optimisation to general process models of the type used routinely for other applications. A major difficulty in this context is that the methods currently available are applicable only to rather restricted classes of problems. We therefore present a symbolic manipulation algorithm for the automatic reformulation of an algebraic constraint of arbitrary complexity involving the five basic arithmetic operations of addition, subtraction, multiplication, division and exponentiation, as well as any univariate function that is either convex or concave over the entire domain of its argument. This class includes practically every constraint encountered in commonly used process models.

The reformulation converts the original nonlinear constraint into a set of linear constraints and a set of nonlinear constraints. Each of the latter involves a single nonlinear term of simple form that can be handled using a spatial branch and bound algorithm.

The symbolic reformulation and spatial branch and bound algorithms have been implemented within the gPROMS process modelling environment. An example illustrating its application is presented.

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Author information

Authors and Affiliations

  1. Centre for Process Systems Engineering, Imperial College of Science, Technology and Medicine, London, SW7 2BY, UK

    Edward M. B. Smith & Constantinos C. Pantelides

Authors
  1. Edward M. B. Smith
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  2. Constantinos C. Pantelides
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Editor information

Editors and Affiliations

  1. Carnegie Mellon University, USA

    Ignacio E. Grossmann

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© 1996 Springer Science+Business Media Dordrecht

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Smith, E.M.B., Pantelides, C.C. (1996). Global Optimisation of General Process Models. In: Grossmann, I.E. (eds) Global Optimization in Engineering Design. Nonconvex Optimization and Its Applications, vol 9. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5331-8_12

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  • DOI: https://doi.org/10.1007/978-1-4757-5331-8_12

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4754-3

  • Online ISBN: 978-1-4757-5331-8

  • eBook Packages: Springer Book Archive

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