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Global optimization for parameter estimation of differential-algebraic systems

  • Michal Čižniar EMAIL logo , Marián Podmajerský , Tomáš Hirmajer , Miroslav Fikar and Abderrazak Latifi
Published/Copyright: March 25, 2009
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Abstract

The estimation of parameters in semi-empirical models is essential in numerous areas of engineering and applied science. In many cases, these models are described by a set of ordinary-differential equations or by a set of differential-algebraic equations. Due to the presence of non-convexities of functions participating in these equations, current gradient-based optimization methods can guarantee only locally optimal solutions. This deficiency can have a marked impact on the operation of chemical processes from the economical, environmental and safety points of view and it thus motivates the development of global optimization algorithms. This paper presents a global optimization method which guarantees ɛ-convergence to the global solution. The approach consists in the transformation of the dynamic optimization problem into a nonlinear programming problem (NLP) using the method of orthogonal collocation on finite elements. Rigorous convex underestimators of the nonconvex NLP problem are employed within the spatial branch-and-bound method and solved to global optimality. The proposed method was applied to two example problems dealing with parameter estimation from time series data.

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Published Online: 2009-3-25
Published in Print: 2009-6-1

© 2008 Institute of Chemistry, Slovak Academy of Sciences

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