Study of dead-end microfiltration features in sequencing batch reactor (SBR) by optimized neural networks

Desalination

Volumn 272, Issue 1-3, 2011, Pages 27-35

  Xi, Xuejie ^a   Cui, Yanjie ^a   Wang, Zhan ^a   Qian, Jianhua ^b   Wang, Jing ^a   Yang, Liying ^a   Zhao, Shanshan ^a  

^a BEIJING UNIVERSITY OF TECHNOLOGY   (China)

^b BOHAI UNIVERSITY   (China)

Author keywords

Artificial neural network; COD concentration; Dead end microfiltration; Flux

Indexed keywords

ACTIVATED SLUDGE; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK APPROACH; COD CONCENTRATION; DEAD END MICROFILTRATION; DYNAMIC BEHAVIORS; HYDRAULIC RETENTION TIME; LINEAR MULTI-REGRESSION MODEL; OPERATING TIME; PERMEATE FLUX; PREDICTIVE MODELS; SEQUENCING BATCH REACTORS; SINGLE-HIDDEN-LAYER NEURAL NETWORKS; TRANSMEMBRANE PRESSURES;

ACTIVATED SLUDGE PROCESS; BATCH REACTORS; CHEMICAL OXYGEN DEMAND; CONCENTRATION (PROCESS); DISSOLVED OXYGEN; MICROFILTRATION; NETWORK LAYERS; OPTIMIZATION; REGRESSION ANALYSIS;

NEURAL NETWORKS;

ACTIVATED SLUDGE; ARTIFICIAL NEURAL NETWORK; BIOREACTOR; CHEMICAL OXYGEN DEMAND; CONCENTRATION (COMPOSITION); FILTRATION; MEMBRANE; MODELING;

EID: 79952620955     PISSN: 00119164     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.desal.2010.12.049     Document Type: Article

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