Neural network approach for modeling the performance of reverse osmosis membrane desalting

Journal of Membrane Science

Volumn 326, Issue 2, 2009, Pages 408-419

  Libotean, Dan ^a   Giralt, Jaume ^a   Giralt, Francesc ^a   Rallo, Robert ^a   Wolfe, Tom ^b   Cohen, Yoram ^c  

^a UNIVERSITAT ROVIRA I VIRGILI   (Spain)

^b Toray Membrane USA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Artificial neural networks; Desalination; Flux decline; Process performance forecasting; Reverse osmosis; Salt passage

Indexed keywords

ARTIFICIAL NEURAL NETWORKS; CONTROL STRATEGIES; FLUX DECLINE; FORECASTING MODELS; INPUT VARIABLES; MODEL ARCHITECTURES; MODELING APPROACHES; OPERATING PARAMETERS; OPERATIONAL DIAGNOSTICS; PERMEATE FLUXES; PLANT INFORMATIONS; PLANT MODELS; PLANT PERFORMANCES; PREDICTIVE ACCURACIES; PROCESS DIAGNOSTICS; PROCESS PERFORMANCE FORECASTING; REVERSE OSMOSIS MEMBRANES; RO PLANTS; SALT PASSAGE; SEQUENTIAL MODELS; SUPPORT VECTOR REGRESSIONS; TARGET VALUES; TIME CONDITIONS; TIME SCALES; TIME-SERIES ANALYSES; UNSTEADY STATES;

BACKPROPAGATION; BRAIN; DESALINATION; FORECASTING; HARMONIC ANALYSIS; MATHEMATICAL MODELS; OSMOSIS; OSMOSIS MEMBRANES; REVERSE OSMOSIS; VECTORS; VEGETATION; WATER FILTRATION;

NEURAL NETWORKS;

SODIUM CHLORIDE;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; CONTROL STRATEGY; CONTROLLED STUDY; DESALINATION; MEMBRANE MODEL; PLANT MODEL; PRIORITY JOURNAL; REVERSE OSMOSIS; STEADY STATE; SUPPORT VECTOR MACHINE; TIME SERIES ANALYSIS;

EID: 58149196170     PISSN: 03767388     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.memsci.2008.10.028     Document Type: Article

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