title: A systematic approach to data-driven modeling and soft sensing in a full-scale plant.
abstract: The well-known mathematical modeling and neural networks (NNs) methods have limitations to incorporate the key process characteristics at the wastewater treatment plants (WWTPs) which are complex, non-stationary, temporal correlation, and nonlinear systems. In this study, a systematic methodology of NNs modeling which can be efficiently included in the key modeling information of the WWTPs is performed by selecting the temporal effect of the hydraulics based on multi-way principal components analysis (MPCA). The proposed method is applied for modeling wastewater quality of a full-scale plant, which is a Daewoo nutrient removal (DNR) process. Through the experimental results in a full-scale plant, the efficiency of the proposed method is evaluated and the prediction capability is highly improved by the inclusion of the hydraulics term due to the optimized structure of neural networks.
authors.affiliation: College of Environmental and Applied Chemistry, Center for Environmental Studies/Green Energy Center, Kyung Hee University, Seocheon-dong 1, Gyeonggi-Do 446-701, South Korea.
authors.forename: M H
authors.initials: MH
authors.lastname: Kim
authors.forename: Y S
authors.initials: YS
authors.lastname: Kim
authors.forename: A A
authors.initials: AA
authors.lastname: Prabu
authors.forename: C K
authors.initials: CK
authors.lastname: Yoo
mesh_terms: D000465:Algorithms; D001673:Biodegradation, Environmental; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D012498:Sanitary Engineering; D012722:Sewage / Q000382:microbiology; D012984:Software; D013997:Time Factors; D014865:Waste Disposal, Fluid / Q000379:methods; D018508:Water Purification / Q000379:methods*
chemical_list: D012722:Sewage