Model complexity reduction of chemical reaction networks using mixed-integer quadratic programming

Ralf Hannemann-Tamás, Attila Gábor, Gábor Szederkényi, Katalin M. Hangos

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8 Citations (Scopus)

Abstract

The model complexity reduction problem of large chemical reaction networks under isobaric and isothermal conditions is considered. With a given detailed kinetic mechanism and measured data of the key species over a finite time horizon, the complexity reduction is formulated in the form of a mixed-integer quadratic optimization problem where the objective function is derived from the parametric sensitivity matrix. The proposed method sequentially eliminates reactions from the mechanism and simultaneously tunes the remaining parameters until the pre-specified tolerance limit in the species concentration space is reached. The computational efficiency and numerical stability of the optimization are improved by a pre-reduction step followed by suitable scaling and initial conditioning of the Hessian involved. The proposed complexity reduction method is illustrated using three well-known case studies taken from reaction kinetics literature.

Original languageEnglish
Pages (from-to)1575-1595
Number of pages21
JournalComputers and Mathematics with Applications
Volume65
Issue number10
DOIs
Publication statusPublished - Jan 1 2013

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Keywords

  • Chemical reaction networks
  • Mixed-integer quadratic programming
  • Model reduction
  • Sensitivity analysis

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computational Theory and Mathematics
  • Computational Mathematics

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