A systematic approach to data-driven modeling and soft sensing in a full-scale plant

Water Science and Technology

Volumn 60, Issue 2, 2009, Pages 363-370

  Kim, M H ^a   Kim, Y S ^a   Prabu, A A ^b   Yoo, C K ^a  

^a Kyung Hee University   (South Korea)

^b VIT UNIVERSITY   (India)

Author keywords

Hydraulic characteristics; Multiway principal components analysis (MPCA); Neural networks (NNs); Nonlinear modeling; Soft sensing; Temporal correlation

Indexed keywords

HYDRAULIC CHARACTERISTICS; MULTIWAY PRINCIPAL COMPONENTS ANALYSIS (MPCA); NONLINEAR MODELING; SOFT SENSING; TEMPORAL CORRELATION;

BUOYANCY; HYDRAULICS; NEURAL NETWORKS; NONLINEAR ANALYSIS; NONLINEAR SYSTEMS; WASTEWATER; WASTEWATER RECLAMATION; WASTEWATER TREATMENT; WATER TREATMENT PLANTS;

PRINCIPAL COMPONENT ANALYSIS;

ARTIFICIAL NEURAL NETWORK; MODELING; NUMERICAL MODEL; WASTEWATER; WATER QUALITY; WATER TREATMENT;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; HYDRAULIC CONDUCTIVITY; INFORMATION; PREDICTION; PROCESSING; WASTE WATER MANAGEMENT; WASTE WATER TREATMENT PLANT; WATER QUALITY;

ALGORITHMS; BIODEGRADATION, ENVIRONMENTAL; MODELS, STATISTICAL; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; SANITARY ENGINEERING; SEWAGE; SOFTWARE; TIME FACTORS; WASTE DISPOSAL, FLUID; WATER PURIFICATION;

EID: 69749089289     PISSN: 02731223     EISSN: None     Source Type: Book Series    
DOI: 10.2166/wst.2009.346     Document Type: Article

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