Third Day Poster Session, 17 June 2010 - NanoTR-VI

P
P Department
P
P Chemistry
Poster Session, Thursday, June 17
Theme F686 - N1123
Application of Artificial Neural Networks for Kinetic Investigation of Thermal Degradation Process in
Nanocomposites
1
1
1,2
1
UM. KhanmohammadiUP P*, M. Ahmadi AzqandiP P, A. Bagheri GarmarudiP
P, N. KhoddamiP
2
1
Department, Faculty of Science, IKIU, Qazvin, Iran
of Chemistry & Polymer Laboratories, Engineering Research Institute, Tehran, Iran
Abstract-Polyimide-Silica Hybrid nanocomposite samples were prepared by sol-gel technique. Specimens from the hybrid nanocomposite were
submitted to thermogravimetric afnalysis and thermal degradation kinetics of hybrid nanocomposite was investigated by thermogravimetric
analysis. The kinetic parameters were obtained via the chemometric data processing of mass loss curves. The non-linear fitting method based on
Particle Swarm Optimization (PSO) algorithm was used to fit the mass loss curves at three heating rates and to adjust the non-linear curves. PSO
is a population based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. PSO shares many
similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random
solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and
mutation. Moreover, the activation energy, order of reaction and pre-exponential factor of degradation for the nanocomposite containing
different amount of inorganic filler was determined by PSO-ANN and then being compared with other methods e.g. pseudo first-order. It was
concluded that PSO algorithm is stronger and more efficient in comparison with other methods for predication of kinetic parameters.
Particle swarm optimization (PSO) is a population based
stochastic optimization technique inspired by social behavior
of bird flocking or fish schooling [1]. PSO shares many
similarities with evolutionary computation techniques such as
Genetic Algorithms (GA). The system is initialized with a
population of random solutions and searches for optima by
updating generations [2,3]. However, unlike GA, PSO has no
evolution operators such as crossover and mutation. In PSO,
the potential solutions, called particles, fly through the
problem space by following the current optimum particles.
Each particle keeps track of its coordinates in the problem
space which are associated with the best solution (fitness) it
has achieved so far. (The fitness value is also stored.) This
value is called pbest. Another "best" value that is tracked by
the particle swarm optimizer is the best value, obtained so far
by any particle in the neighbors of the particle. This location is
called lbest. When a particle takes all the population as its
topological neighbors, the best value is a global best and is
called gbest. The particle swarm optimization concept consists
of, at each time step, changing the velocity of (accelerating)
each particle toward its pbest and lbest locations (local version
of PSO). Acceleration is weighted by a random term, with
separate random numbers being generated for acceleration
toward pbest and lbest locations.
Nanocomposite was prepared by sol-gel technique. In this
method, tetraethoxysilane (TEOS), 10% wt solution of
polyamic acid in DMAc and water was used as precursors.
Specimens from the Polyimide-Silica Hybrid nanocomposite
were submitted to thermogravimetric analysis using the
TA2100 (TA Instruments) thermal analyzer. The mass of the
samples ranged from 5 to 10 mg and the analysis was done in
a flowing of nitrogen atmosphere (50 ml/min). The experiment
was conducted by heating samples at a constant linear heating
rates of 5 °C/min, 10 °C /min, 20 °C /min up to 700 °C.The
resulting curves are percentages of remaining nano composite.
PSO algorithm was used in fitting of non-linear curves that
were used in order to obtain the kinetic parameters as function
of temperature and different heating rates, which could fit the
weight loss curves. In this way, the mathematical modeling
was based on bellow equation:
AW .
inEa(
n 1)
w
R
10
.
Ea
2.3150.4567
RT
(1 W
1
1n
1n
f
)
W
f
where: w is residual mass fraction (g); Win is initial mass of
specimen (g); A is pre-exponential factor; Ea is activation
energy (J/mol); n is order of reaction; b is heating rate
(°C /min); R is gas constant (8.31451 J/mol.K); T is
temperature (K) and wRf Ris final mass fraction (g). The method
is based on linear fitting of model related to the obtained
parameters. PSO has been successfully applied in many
research and application areas. It was demonstrated that PSO
gives reliable results in kinetic investigation of thertmal
degradation. Another reason of PSO attraction is the few
parameters which are needed to be adjusted. One version, with
slight variations, works well in a wide variety of applications.
We found PSO algorithm is more efficient than other methods
in the predication of kinetic parameters.
*Corresponding author: mrkhanmohammadi@gmail.com
[1] Zhang C, Li Y and Shao H, Proceedings of the World Congress
on Intelligent Control and Automation (WCICA), 2, 1065-1068,
2000.
[2] Peng J, Chen Y, and Eberhart R, Proceedings of the Fifteenth
Annual Battery Conference on Applications and Advances, 173-177,
2000.
[3] Chatterjee A and Siarry P, Computers & Operations Research,
33, 859-871, 2006
6th Nanoscience and Nanotechnology Conference, zmir, 2010 711