Modeling, analysis and optimization of aircyclones using artificial neural network, response surface methodology and CFD simulation approaches

Powder Technology

Volumn 212, Issue 1, 2011, Pages 115-133

  Elsayed, Khairy ^a   Lacor, Chris ^a  

^a VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

Author keywords

Artificial neural networks (ANN); Cyclone geometry; Cyclone separator; Mathematical modelling; Optimization; Radial basis function neural network (RBFNN)

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CFD SIMULATIONS; CUT-OFF; CYCLONE DESIGN; CYCLONE GEOMETRY; EXPERIMENTAL DATA; EXPERIMENTAL VALUES; GEOMETRICAL DIMENSIONS; GEOMETRICAL PARAMETERS; HIGH EFFICIENCY; LOW PRESSURE DROP; MATHEMATICAL MODELLING; MINIMUM PRESSURE; NEW DESIGN; NON-LINEAR RELATIONSHIPS; OPTIMIZATION TECHNIQUES; PERFORMANCE PARAMETERS; RADIAL BASIS FUNCTION NEURAL NETWORKS; RADIAL BASIS NEURAL NETWORKS; RESIDUAL ERROR; RESPONSE SURFACE METHODOLOGY; SECOND-ORDER POLYNOMIAL; VOLUME FLOW RATE;

COMPUTER SIMULATION; CYCLONE SEPARATORS; DESIGN; GEOMETRY; MATHEMATICAL MODELS; OPTIMIZATION; PRESSURE DROP; RADIAL BASIS FUNCTION NETWORKS; STORMS; SURFACE PROPERTIES; VORTEX FLOW;

NEURAL NETWORKS;

ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; COMPUTATIONAL FLUID DYNAMICS; CONTROLLED STUDY; CYCLONE SEPARATOR; FLOW RATE; GEOMETRY; HYPOBARISM; INTERMETHOD COMPARISON; MATHEMATICAL MODEL; PREDICTION; PROCESS DEVELOPMENT; PROCESS MODEL; PROCESS OPTIMIZATION; RADIAL BASIS FUNCTION NEURAL NETWORK; RESPONSE SURFACE METHOD; VALIDATION PROCESS; VORTEX MOTION;

EID: 79959997024     PISSN: 00325910     EISSN: 1873328X     Source Type: Journal    
DOI: 10.1016/j.powtec.2011.05.002     Document Type: Article

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