Predicting flux decline in crossflow membranes using artificial neural networks and genetic algorithms

Journal of Membrane Science

Volumn 283, Issue 1-2, 2006, Pages 147-157

  Sahoo, Goloka Behari ^a   Ray, Chittaranjan ^b  

^a University of California   (United States)

^b University of Hawaii at Manoa   (United States)

Author keywords

Crossflow membrane filtration; Genetic algorithms; Input data scaling; Multilayer feedforward back propagation neural network; Radial basis function network

Indexed keywords

FILTRATION; GENETIC ALGORITHMS; IONIC STRENGTH; MEMBRANES; PH EFFECTS; PRESSURE EFFECTS;

CROSSFLOW MEMBRANE FILTRATION; CROSSFLOW MEMBRANES; INPUT DATA SCALING; OPTIMAL GEOMETRY;

RADIAL BASIS FUNCTION NETWORKS;

WATER;

FILTRATION; GENETIC ALGORITHMS; IONIC STRENGTH; MEMBRANES; PH EFFECTS; PRESSURE EFFECTS; RADIAL BASIS FUNCTION NETWORKS;

ACCURACY; ARTICLE; ARTIFICIAL MEMBRANE; ARTIFICIAL NEURAL NETWORK; CROSSFLOW FILTRATION; DATA BASE; EXPERIMENTATION; GENETIC ALGORITHM; GEOMETRY; IONIC STRENGTH; MEMBRANE TRANSPORT; PARAMETER; PARTICLE SIZE; PH; PHYSICAL CHEMISTRY; PRIORITY JOURNAL;

EID: 33747889741     PISSN: 03767388     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.memsci.2006.06.019     Document Type: Article

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