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Title: Trained Neural Network Characterizing Variables for Predicting Organic Retention by Nanofiltration Membranes
Authors: Sotto, Arcadio
Martinez, Ana
Castellanos, Angel
Keywords: Neural Networks
Radial Basis Functions
Knowledge Acquisition
Issue Date: 2008
Publisher: 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.
ISSN: 1313-0455
Appears in Collections:Book 7 Artificial Intelligence and Decision Making

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