template Inforino 2026 MPEI_ENG_finish (1)

1.

Moscow, Russia
14 - 17 Apr, 2026
Use of Information Technology for Investigating
the Kinetics of Pyrolysis of Carbon-containing
Materials
AUTHORS:
Ivan Burakov, National Research Universiti “MPEI”
Anna Valineeva, National Research Universiti “MPEI”
Stanislav Popov, National Research Universiti “MPEI”
Korobkin Dmitry, National Research Universiti “MPEI”
Ye Yint Aung , National Research Universiti “MPEI”
SPEAKER:
Anna Valineeva
National Research Universiti “MPEI”

2.

Moscow, Russia
14- 17 Apr, 2026
TGA as the main tool for pyrolysis kinetic studies
Understanding the pyrolysis kinetics of solid carbon-containing materials across
different temperature ranges is essential for developing efficient thermal engineering
plants. Thermogravimetric analysis (TGA) is widely used for this purpose.
Based on the TGA curve, kinetic parameters such as the activation energy and the
pre-exponential factor in the Arrhenius equation are determined. For this purpose, a
number of methods are employed, namely Ozawa–Flynn–Wall (OFW), Kissinger, KAS,
and others.
This work focuses on developing IT-based tools for efficiently studying the pyrolysis
kinetics of solid carbon-containing materials. The research method involves the software
implementation of TGA data processing in Mathcad, along with the use of Mathcad for
statistical data analysis and graphical representation
2

3.

Moscow, Russia
14- 17 Apr, 2026
Arrhenius equation for describing the pyrolysis process
Assume that the carbon-containing materials pyrolysis process is described by a first-order chemical
reaction equation
E
da
k 1 a ; k A exp
(1)
dτ
RuT
a w0 w τ w0 w – the conversion range;
w0 , w
– initial and final mass of the substance undergoing pyrolysis;
w τ – current mass at time;
k – rate constant described by the Arrhenius equation, s⁻¹;
E – activation energy, kJ/mol;
A – pre-exponential factor, s⁻¹;
Ru – universal gas constant, kJ/mol/K;
m τ 1 a 1 m
m τ w τ w0
3
– relative mass;
(2)

4.

Moscow, Russia
14- 17 Apr, 2026
Thermogravimetric analysis of the coal sample
A sample of Skopin-Petrushinsky coal was
studied using TGA in an inert argon atmosphere
(purity 99.999%). The tests were conducted on a
NETZSCH STA 409 F1 Jupiter simultaneous
thermal analyzer over a temperature range from
40 °С ( = 313 K) to 600 °С ( = 873 K) at a
heating rate ( dT dτ ) of 10 °C/min, with a
sample mass of 15 mg.
Fig. 1. TGA of the Skopin-Petrushinsky coal sample
4

5.

Moscow, Russia
14- 17 Apr, 2026
Algorithm for determining the kinetic parameters
1. The coordinates of the points are taken from the thermogram in total k max. The coordinates are stored in
arrays: xk – temperature array, K; yk – relative mass array; array element index k 1, k max 1 ;
2. The lower bound klow and upper bound khigh for sampling from the arrays xk , yk
where 0 klow khigh k max 1 . The resulting samples are denoted as arrays xˆ p and yˆ p ;
are set,
For these arrays, we determine the kinetic parameters A and E using the OFW method:
arrays are formed X p , Y p according to the formulas
X p 1 xˆ p ; Yp ln( ln( yˆ p ));
(3)
using the regress() function in the Mathcad environment, the coefficients of the linear regression
equation Y a1 X b1 are determined by the least squares method;
a system of two equations is formed
E
a1 1.052 R
u
b1 ln A E 5.3305
R
u
(4)
which is solved in the Mathcad environment using the Given–Find block for the unknown
quantities A and E .
5

6.

Moscow, Russia
14- 17 Apr, 2026
Algorithm for determining the kinetic parameters
3. Using the obtained values of A and E , the temperature dependence of the relative mass mcalct T is
calculated over the same temperature range as that used in the TGA experiment [ T min, T max]. This calculation
is implemented in Mathcad based on the equations:
A T
E
a a T 1 exp exp
dT ;
T0
RuT
mcalct T 1 a T 1 m ;
(5)
T T min , T max .
4. The calculated TGA curve is compared with the experimental one, determining quantitative measures of
their agreement: σ – the root-mean-square deviation (RMSD) of the experimental points xk , yk from the
curve mcalct T , as well as R2 – the coefficient of determination, calculated in the Mathcad environment
using the corr() function.
5. By varying
parameters klow , khigh and
cyclically repeating steps 2–4, we obtain
R 2 R 2 klow , k high
dependencies σ σ klow , khigh
and
. A search is conducted for the optimal
opt
opt
parameters klow and khigh , and that yield a minimum function of σ σ klow , khigh and a maximum of
R 2 R 2 klow , k high .
(6)
σ klow , khigh 0
R 2 klow , khigh 1
6

7.

Moscow, Russia
14- 17 Apr, 2026
Algorithm Implementation
The algorithm was implemented for the thermogram presented in Fig. 1. As part of this process, 29 point
coordinates
were
recorded
from
the
TGA
curve.
The
optimization
of
the
2
2
functions σ σ klow , khigh and R R klow , khigh was carried out by analyzing their one-factor dependencies in
two stages.
At the first stage, it was decided that khigh = k max –1 = 28, klow increases from 8 to 16. The results are
presented in Table 1.
Optimal compliance with conditions (5) is
achieved at klow = 15.
7

8.

Moscow, Russia
14- 17 Apr, 2026
Algorithm Implementation
At the second stage, we fix the previously found value klow = 15 and sequentially decrease khigh . The results
are presented in Table 2. The best fulfillment of conditions (6) is observed at khigh = 23.
opt
opt
As a result, the optimizing values khigh
= 15 and klow = 23
were found and these correspond to the values of the
kinetic parameters that providing the extremes of
functions (6) and the best approximation of the
calculated data to the experimental TGA results.
A = 190.446 s–1, E= 66.91 kJ/mol,
Fig. 2. Comparison of calculated and experimental
data on coal pyrolysis:
curve – mcalct T dependence with parameters (7); the
circles are experimental points taken from the TGA
curve (Fig. 1)
8
(7)

9.

Application of the developed algorithm to Wind Turbine Blade Components
Moscow, Russia
14- 17 Apr, 2026
Fig. 3. Comparison of calculated and experimental data on
thermoplastic polymer pyrolysis:
curve – dependence mcalct T with parameters (8); the circles
are experimental points taken from the TGA curve*
A = 2161 s–1, E = 82.05 kJ/mol. (8)
σ = 0.109
R 2= 0.971
Fig. 4. Comparison of calculated and experimental data
on wood pyrolysis:
curve – dependence mcalct T with parameters (9); the
circles are experimental points taken from the TGA curve*
A = 368.3 s–1,
σ = 0.108
E = 59.52 kJ/mol. (9)
R 2 = 0.98
*X. Zhao, D. Pakuła, M. Frydrych, R. Konieczna, B. Sztorch, R. Kozera, H. Liu, H. Zhou, R.E. Przekop, “Treatment and Valorization of Waste Wind Turbines: Component Identification and
Analysis,” Materials 2025, 18, 468. https://doi.org/10.3390/ma18020468
Wardach-Swiecicka, I. Modelling thermal behaviour of a single solid particle pyrolysing in a hot gas flow / Izabela Wardach-Swiecicka, Dariusz Kardas. Energy, 221, 2021, 119802.
https://doi.org/10.1016/j.energy.2021.119802
9

10.

Moscow, Russia
14- 17 Apr, 2026
Conclusion
1. An algorithm for determining the kinetic parameters of the pyrolysis process based on
experimental thermogravimetric analysis data has been developed. The software
implementation of the algorithm utilizes information technologies and has been tested on a
series of carbon-containing materials.
2. The software product developed in the Mathcad environment is used in scientific and
engineering activities and in the educational practice of the university.
10

11.

Title
Москва, Россия
14- 17 апреля 2026
THANK YOU
Speaker contacts :
Anna Valineeva
National Research Universiti “MPEI”
valineevaaa@mpei.ru
11