Advances in Interval Methods for Deterministic Global Optimization in Chemical Engineering文档.pdf
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Advances in Interval Methods for Deterministic Global Optimization in Chemical Engineering文档
Journal of Global Optimization 29: 281–296, 2004. 281
© 2004 Kluwer Academic Publishers. Printed in the Netherlands.
Advances in Interval Methods for Deterministic
Global Optimization in Chemical Engineering
Y. LIN and M.A. STADTHERR
Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame,
IN 46556,USA (E-mail: markst@)
(Received 31 July 2003; accepted in revised form 7 August 2003)
Abstract. In recent years, it has been shown that strategies based on an interval-Newton approach
can be used to reliably solve a variety of nonlinear equation solving and optimization problems in
chemical process engineering, including problems in parameter estimation and in the computation
of phase behavior. These strategies provide a mathematical and computational guarantee either that
all solutions have been located in an equation solving problem or that the global optimum has been
found in an optimization problem. The primary drawback to this approach is the potentially high
computational cost. In this paper, we consider strategies for bounding the solution set of the linear
interval equation system that must be solved in the context of the interval-Newton method. Recent
preconditioning techniques for this purpose are reviewed, and a new bounding approach based on
the use of linear programming (LP) techniques is presented. Using this approach it is possible to
determine the desired bounds exactly (within round out), leading to significant overall improvements
in computational efficiency. These techniques will be demonstrated using several global optimization
problems, with focus on problems arising in chemical engineering, including parameter estimation
and molecular modeling. These problems range in size from under ten variables to over two hundred,
and are solved deterministically using the interval methodology.
Key words: Deterministic global optimization, Interval
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