Modelling the breakthrough of activated carbon filters by pesticides in surface waters with static and recurrent neural networks

Chemical Engineering Journal

Volumn 145, Issue 1, 2008, Pages 7-15

  Faur, C ^a   Cougnaud, A ^a   Dreyfus, G ^b   Le Cloirec, P ^c  

^a UNIV MONTPELLIER   (France)

^b PSL RESEARCH UNIVERSITY   (France)

^c UNIV RENNES   (France)

Author keywords

Activated carbon; Adsorption; Feed forward neural networks; Pesticides; Recurrent neural networks; Water treatment

Indexed keywords

ACTIVATED CARBON; ADSORPTION; AGRICULTURAL CHEMICALS; AIR FILTERS; ARTIFICIAL INTELLIGENCE; ATMOSPHERIC TEMPERATURE; BIOLOGICAL MATERIALS; CARBON BLACK; CHARCOAL; FEEDFORWARD NEURAL NETWORKS; FILTERS (FOR FLUIDS); IMAGE CLASSIFICATION; ISOTHERMS; NETWORK PROTOCOLS; NEURAL NETWORKS; ORGANIC COMPOUNDS; PESTICIDES; REINFORCEMENT LEARNING; SENSOR NETWORKS; SPRAYING; SURFACE WATERS; VEGETATION; WAVE FILTERS;

ACTIVATED CARBON FILTERS; ADSORPTION PERFORMANCE; BLACK-BOX APPROACH; BREAK-THROUGH CURVES; COMPETITIVE ADSORPTION; COMPETITIVE ADSORPTION ISOTHERMS; DETERMINATION COEFFICIENTS; DIFFERENT OPERATING CONDITIONS; EQUILIBRIUM PARAMETERS; FEED-FORWARD; FREUNDLICH; INPUT VARIABLES; INPUT-OUTPUT; NATURAL ORGANIC MATTER; NATURAL WATERS; STATIC REACTORS; STATISTICAL APPROACHES; WATER TREATMENT;

RECURRENT NEURAL NETWORKS;

EID: 53849117800     PISSN: 13858947     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cej.2008.02.015     Document Type: Article

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