title: Sequential modelling of a full-scale wastewater treatment plant using an artificial neural network.
abstract: This work proposes a sequential modelling approach using an artificial neural network (ANN) to develop four independent multivariate models that are able to predict the dynamics of biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solid (SS), and total nitrogen (TN) removal in a wastewater treatment plant (WWTP). Suitable structures of ANN models were automatically and conveniently optimized by a genetic algorithm rather than the conventional trial and error method. The sequential modelling approach, which is composed of two parts, a process disturbance estimator and a process behaviour predictor, was also presented to develop multivariate dynamic models. In particular, the process disturbance estimator was first employed to estimate the influent quality. The process behaviour predictor then sequentially predicted the effluent quality based on the estimated influent quality from the process disturbance estimator with other process variables. The efficiencies of the developed ANN models with a sequential modelling approach were demonstrated with a practical application using a data set collected from a full-scale WWTP during 2 years. The results show that the ANN with the sequential modelling approach successfully developed multivariate dynamic models of BOD, COD, SS, and TN removal with satisfactory estimation and prediction capability. Thus, the proposed method could be used as a powerful tool for the prediction of complex and nonlinear WWTP performance.
authors.affiliation: Department of Civil and Environmental Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea. kalsuma1@kaist.ac.kr
authors.forename: Joong-Won
authors.initials: JW
authors.lastname: Lee
authors.forename: Changwon
authors.initials: C
authors.lastname: Suh
authors.forename: Yoon-Seok Timothy
authors.initials: YS
authors.lastname: Hong
authors.forename: Hang-Sik
authors.initials: HS
authors.lastname: Shin
mesh_terms: D057919:Biological Oxygen Demand Analysis / Q000379:methods*; D008954:Models, Biological; D008956:Models, Chemical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000032:analysis; D017711:Nonlinear Dynamics; D010100:Oxygen / Q000032:analysis; D025341:Principal Component Analysis / Q000379:methods; D012722:Sewage / Q000032:analysis; D014865:Waste Disposal, Fluid / Q000379:methods*; D014874:Water Pollutants, Chemical / Q000032:analysis; D014894:Weights and Measures
chemical_list: D012722:Sewage; D014874:Water Pollutants, Chemical; D009584:Nitrogen; D010100:Oxygen