AUTOMATED FITTING OF THERMOGRAVIMETRIC 

ANALYSIS DATA

Morgan C. Bruns A and Isaac T. Leventon B

A Virginia Military Institute, Department of Mechanical Engineering

710 Nichols Hall; Lexington, VA 24450; United States

B National Institute of Standards and Technology, Fire Research Division,

100 Bureau Drive; Building 224, Room A265; Gaithersburg, MD 20899; United States

ABSTRACT

A novel methodology has been developed for extracting pyrolysis kinetic parameters from 

thermogravimetric analysis (TGA) data. The development of this methodology is motivated by a need 

to automate the determination of material properties for use in fire models. The algorithm with which 

the methodology is implemented is described. Aside from being fully-automated, the resultant script 

has the advantage of being efficient—a full set of kinetic parameters is provided in less than one 

second. The script is verified against manufactured TGA data for one and two reaction mechanisms

and the effects of reaction peak width and the distance between reaction peaks is examined. 

Validation is accomplished by applying the script to TGA data for Nylon 6,6, a flexible polyurethane 

(PU) foam, and polyvinyl chloride (PVC). The resultant kinetic parameters are tabulated, and plots of 

the actual and predicted TGA data show that the algorithm is quite effective for one, two, and three 

reaction mechanisms.

INTRODUCTION

Computational fire models have proven to be effective at predicting the spread of heat and 

smoke in a wide range of building fire scenarios. However, such models still generally require user 

input describing the actual fire that is generating the heat and smoke. Computational predictions of 

flame spread and fire growth require somewhat detailed models of condensed phase physics, and a 

number of condensed phase pyrolysis models have been developed1-3. Such models have proven 

effective at modeling the burning rate of small slabs, but their application to flame spread calculations 

is more limited. Part of the problem is that these pyrolysis models require the specification of a large 

number of material properties. Furthermore, there are many different flammable materials that need to 

be considered in fire scenarios. Substantial progress in applying computational fire models to 

predicting flame spread could be achieved by the development of both a streamlined methodology for 

characterizing the thermophysical properties of flammable materials as well as the creation of a 

publicly available database of such material properties.

One successful approach for characterizing materials is based on performing a number of milligramscale and bench scale tests such as thermogravimetric analysis (TGA), differential scanning 

calorimetry (DSC), and the controlled atmosphere pyrolysis apparatus (CAPA)4. An example of a 

public database of material properties is found in the Validation Guide of the Fire Dynamics 

Simulator (FDS)5. Currently, an international effort is underway to further develop experimental and 

modeling tools such as these in order to “advance predictive fire modelling”6. The present manuscript 

presents work based on a coordinated effort to develop a comprehensive database of material 

properties for use in fire models. This database will include both raw data from a suite of milligramscale tests and a list of properties for each material that could be directly used as inputs for a fire 

model such as FDS. In order to generate these tables of material properties, automated computational 

scripts are required to robustly analyze raw data from small scale tests. In this paper, we present such 

a script for calibrating a generalized pyrolysis kinetic model to TGA data. Previous work has looked 










Three different materials were used to validate the algorithm: Nylon 66, PVC, and flexible 

polyurethane foam. All TGA tests for these three materials were performed at a heating rate of 

10 K/min in a nitrogen environment with the samples placed in an alumina crucible. These cases 

proved to be a rigorous test of the algorithm since the materials varied in the number of reactions 

predicted as well as the noise in the underlying data. Kinetic parameters for all three of these materials 

were determined using the automated algorithm, and the resultant model predictions agreed with the 

data nicely.

Although the algorithm has been verified and validated to some extent, more validation is always 

desirable in order to find the limits of applicability of the algorithm. The results presented in this 

paper are quite promising, but it would be desirable to make better fits for broader peaked reactions as 

well as for overlapping reactions that occur at very similar temperatures. Furthermore, as was seen in 

the validation cases, different materials can produce different amounts of noise in the raw TGA data. 

It would therefore be helpful to make the algorithm more robust against varying degrees of noise. 

These improvements are primarily related to the very first step in the algorithm which uses the 

Savitzky-Golay filter to smooth the data and find sufficiently smoothed derivatives. There are several 

parameters involved in this process that need to be optimized by further study.

Finally, the true test of the effectiveness of this novel methodology lies in its effectiveness at 

providing parameters that accurately predict burning rate and flame spread in fire models such as 

FDS. Future work will therefore involve developing similar procedures to use other microscale data 

such as differential scanning calorimeter (DSC) and microscale combustion calorietery (MCC) to 

estimate the other parameters needed in fire models. Once a more complete program of automation is 

achieved, it will be possible to validate the procedures by comparing fire model results with 

experimental flame spread data of real materials. Such a process will take significant effort, but the 

present paper provides a critical first step in this direction.

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