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Journal of Thermal Analysis and Calorimetry

, Volume 116, Issue 3, pp 1141–1151 | Cite as

Optimizing fitting parameters in thermogravimetry

  • Matilde Ríos-Fachal
  • Javier Tarrío-Saavedra
  • Jorge López-Beceiro
  • Salvador Naya
  • Ramón Artiaga
Article

Abstract

This study presents an alternative to simple estimation of parametric fitting models used in thermal analysis. The addressed problem consists in performing an alternative optimization method to fit thermal analysis curves, specifically TG curves and their first derivatives. This proposal consists in estimating the optimal parameters corresponding to fitting kinetic models applied to thermogravimetric (TG) curves, using evolutionary algorithms: differential evolution (DE), simulated annealing and covariance matrix adapting evolutionary strategy. This procedure does not need to include a vector with the initial values of the parameters, as is currently required. Despite their potential benefits, the application of these methods is by no means usual in the context of thermal analysis curve’s estimation. Simulated TG curves are obtained and fitted using a generalized logistic mixture model, where each logistic component represents a thermal degradation process. The simulation of TG curves in four different scenarios taking into account the extent of processes overlapping allows us to evaluate the final results and thus to validate the proposed procedure: two degradation processes non-overlapped simulated using two generalized logistics, two processes overlapped, four processes non-overlapped and four processes overlapped two by two. The mean square error function is chosen as objective function and the above algorithms have been applied separately and together, i.e., taking the final solution of the DE algorithm is the initial solution of the remaining. The results show that the evolutionary algorithms provide a good solution for adjusting simulated TG curves, better than that provided by traditional methods.

Keywords

TG DTG Model-fitting Overlapping Global optimization Evolutionary algorithms 

Notes

Acknowledgements

This research has been partially supported by the Spanish Ministry of Science and Innovation, Grant MTM2011-22392 (ERDF included).

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2014

Authors and Affiliations

  • Matilde Ríos-Fachal
    • 1
  • Javier Tarrío-Saavedra
    • 2
  • Jorge López-Beceiro
    • 2
  • Salvador Naya
    • 2
  • Ramón Artiaga
    • 2
  1. 1.CPI Cruz do Sar. Consellería de Cultura, Educación e Ordenación UniversitariaBergondoSpain
  2. 2.Escola Politécnica SuperiorUniversidade da CoruñaFerrolSpain

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