Neva Grošelj,1* Jure Zupan,1 Jorge Magallanes2 and Silvia Reich3
1 National Institute of Chemistry, Hajdrihova 19, Ljubljana, Slovenia
2 Unidad de Actividad Química, Comisión Nacional de Energía Atómica, San Martín,
Argentina
3 Escuela de Ciencia y Tecnología, Universidad de San Martín, San Martín,
Argentina
* Corresponding author: E-mail: neva.groselj@ki.si,
phone: + 386 1 4760 383; fax: + 386 1 4760 300
Abstract
The aim of the optimization is to find out the optimal parameters for complex
system such as synthesis of the compounds,
chemical reactions, analytical methods, property of the products or chemical
processes. The parameters that we
want to determine are the values, which describe the system. The SIMPLEX is one
of the most simple and general optimization
method. It is used to predict the experiments that in quickest way lead to an
optimum.
In this work the SIMPLEX method was used to optimize the parameters of the
counter-propagation neural network
model constructed for the prediction of the ozone concentration as one of the
most outstanding air pollution parameters
in the Buenos Aires region. The network was trained with the data available for
980 collected samples; each of them was
described by the concentrations of 7 pollutants: CO, SO2, O3, NOx, NO, NO2, and
PM10, and 8 weather related variables:
cloudiness, rainfall, insolation factor, temperature, pressure at two locations,
and wind intensity with direction. The
evaluation function as the optimization criterion of the model was thus the
correlation coefficient between the experimental
and predicted ozone concentrations.
Keywords: Air quality; SIMPLEX optimization method; Insolation factor;
Artificial Neural Networks; Ozone formation;
Pollutants