AI Article Synopsis

  • A new non-linear regression method is developed for analyzing non-isothermal thermogravimetric data from experiments with varying heating rates.
  • This method efficiently handles complex processes with multiple unknown stages, eliminating the need for prior knowledge of kinetic types.
  • The approach involves a stepwise parameter estimation using Avrami-Erofeev functions initially, then transitioning to Sestak-Berggren functions to enhance the model, and has been validated against both simulated and actual datasets.

Article Abstract

A novel non-linear regression method for modeling non-isothermal thermogravimetric data is proposed. Experiments for several heating rates are analyzed simultaneously. The method is applicable to complex multi-stage processes when the number of stages is unknown. Prior knowledge of the type of kinetics is not required. The main idea is a consequent estimation of parameters when the overall model is successively changed from one level of modeling to another. At the first level, the Avrami-Erofeev functions are used. At the second level, the Sestak-Berggren functions are employed with the goal to broaden the overall model. The method is tested using both simulated and real-world data. A comparison of the proposed method with a recently published 'model-free' deconvolution method is presented.

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http://dx.doi.org/10.1039/c6cp07529kDOI Listing

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