Institute of Information Theories and Applications FOI ITHEA
Abstract:
Many organic compounds cause an irreversible damage to human health and the ecosystem and are
present in water resources. Among these hazard substances, phenolic compounds play an important role on the
actual contamination. Utilization of membrane technology is increasing exponentially in drinking water production
and waste water treatment. The removal of organic compounds by nanofiltration membranes is characterized not
only by molecular sieving effects but also by membrane-solute interactions. Influence of the sieving parameters
(molecular weight and molecular diameter) and the physicochemical interactions (dissociation constant and
molecular hydrophobicity) on the membrane rejection of the organic solutes were studied. The molecular
hydrophobicity is expressed as logarithm of octanol-water partition coefficient. This paper proposes a method
used that can be used for symbolic knowledge extraction from a trained neural network, once they have been
trained with the desired performance and is based on detect the more important variables in problems where
exist multicolineality among the input variables.