Abstract
Critical Micelle Concentration is a fundamental property on studying behaviour of surfactants. In general terms it depends on temperature, pressure and on the existence and concentration of other surface-active substances and electrolytes. In this work it is presented a model based on Artificial Neural Networks to obtain predictive values of Critical Micelle Concentration (CMC) of some non-ionic surfactants. ANN model works using topological descriptors of the molecules involved together with already known CMC values and provides predictive values for new cases. It is proposed a specific architecture for ANN consisting of an input layer with seven neurons, one intermediate layer with fourteen neurons and one neuron in the output layer. This ANN model seems to be a good method for forecast CMC.
Kurzfassung
Die kritische Mizellbildungskonzentration ist eine wichtige Eigenschaft bei der Untersuchung des Tensidverhaltens. Sie hängt grundsätzlich von der Temperatur, dem Druck und von der Anwesenheit und Konzentration anderer grenzflächenaktiver Substanzen und Elektrolyte ab. In dieser Arbeit wird ein Modell vorgestellt, das auf künstlichen neuronalen Netzwerken (ANN) beruht und zur Vorhersage von kritischen Mizellbildungskonzentrationen (CMC) einiger nichtionischer Tenside verwendet wird. Das ANN arbeitet mit topologischen Deskriptoren der Moleküle und bereits bekannten CMC-Werten und sagt so Werte für neue Fälle voraus. Es wird eine spezielle Bauweise des ANN vorgeschlagen, die aus einer Eingangslage mit sieben Neutronen einer Zwischenschicht mit vierzehn Neuronen und einer Abschlusslage mit einem Neutron besteht. Dieses ANN-Modell scheint ein gutes Verfahren zu sein, um die CMC vorauszusagen.
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