Effect of the number of TGA curves employed on the biomass pyrolysis kinetics results obtained using the Distributed Activation Energy Model Articles uri icon

publication date

  • June 2015

start page

  • 360

end page

  • 371


  • 134

International Standard Serial Number (ISSN)

  • 0378-3820

Electronic International Standard Serial Number (EISSN)

  • 1873-7188


  • The Distributed Activation Energy Model (DAEM) is widely used for determination of characteristic parameters of pyrolysis kinetics from a certain number of experimental thermal gravimetric analysis (TGA) curves obtained at different heating rates. The number of heating rates employed to obtain different TGA curves varies between different authors, being typically between 3 and 5. The validity of DAEM has been discussed by several authors based on its capability of describing the devolatilization kinetics of the samples. Nevertheless, to our knowledge, there are no studies available in the literature evaluating the uncertainties associated to the simplified DAEM method or quantifying the effect of considering different numbers of TGA curves in the analysis. Therefore, we studied the effect of the number of TGA curves considered in this work, as well as the effect of considering or neglecting the uncertainties of different measurement parameters employed in the calculation. The characterization was carried out using four types of biomass (pine wood, olive kernel, thistle flower and corncob) employing up to 9 different heating rates between 10 and 40 K/min during the TGA tests. Activation energies and pre-exponential factors were given and their uncertainties were reduced by increasing the number of TGA curves obtained at different heating rates. Nonetheless, considering measurement uncertainties during the calculations reduces significantly the final uncertainties of the pyrolysis parameters, permitting the reduction of the number of TGA curves used in the analysis. (C) 2015 Elsevier B.V. All rights reserved.


  • Mechanical Engineering


  • distributed activation energy model (daem); biomass pyrolysis; heating rate uncertainty; temperature uncertainty; thermal gravimetric analysis (tga)