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Automatic pyrolysis mass loss modeling from thermo-gravimetric analysis data using genetic programming

Published:12 July 2011Publication History

ABSTRACT

Modeling to predict flame spread and fire growth is an active area of research in Fire Safety Engineering. A significant limitation to current approaches has been the lack of thermophysical material properties necessary for the simplified pyrolysis models embedded within the models. Researchers have worked to derive physical properties such as density, specific heat capacity, and thermal conductivity from data obtained using bench-scale fire tests such as Thermo-Gravimetric Analysis (TGA). While Genetic Algorithms (GA) have been successfully used to solve for constants in empirical models, it has been shown that the resulting parameters are not valid individually as material properties, especially for complex materials such as wood. This paper describes an alternate approach using Genetic Programming (GP) to automatically derive a mass loss model directly from TGA data.

References

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  • Published in

    cover image ACM Conferences
    GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
    July 2011
    1548 pages
    ISBN:9781450306904
    DOI:10.1145/2001858

    Copyright © 2011 ACM

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    Publication History

    • Published: 12 July 2011

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