### 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 language | English |
---|---|

Pages (from-to) | 1575-1595 |

Number of pages | 21 |

Journal | Computers and Mathematics with Applications |

Volume | 65 |

Issue number | 10 |

DOIs | |

Publication status | Published - 2013 |

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### Keywords

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

### ASJC Scopus subject areas

- Computational Theory and Mathematics
- Modelling and Simulation
- Computational Mathematics

### Cite this

*Computers and Mathematics with Applications*,

*65*(10), 1575-1595. https://doi.org/10.1016/j.camwa.2012.11.024

**Model complexity reduction of chemical reaction networks using mixed-integer quadratic programming.** / Hannemann-Tamás, Ralf; Gábor, Attila; Szederkényi, G.; Hangos, K.

Research output: Contribution to journal › Article

*Computers and Mathematics with Applications*, vol. 65, no. 10, pp. 1575-1595. https://doi.org/10.1016/j.camwa.2012.11.024

}

TY - JOUR

T1 - Model complexity reduction of chemical reaction networks using mixed-integer quadratic programming

AU - Hannemann-Tamás, Ralf

AU - Gábor, Attila

AU - Szederkényi, G.

AU - Hangos, K.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Chemical reaction networks

KW - Mixed-integer quadratic programming

KW - Model reduction

KW - Sensitivity analysis

UR - http://www.scopus.com/inward/record.url?scp=84878526661&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84878526661&partnerID=8YFLogxK

U2 - 10.1016/j.camwa.2012.11.024

DO - 10.1016/j.camwa.2012.11.024

M3 - Article

VL - 65

SP - 1575

EP - 1595

JO - Computers and Mathematics with Applications

JF - Computers and Mathematics with Applications

SN - 0898-1221

IS - 10

ER -