Prediction of the effluent from a domestic wastewater treatment plant of CASP using gray model and neural network
Resource
ENVIRONMENTAL MONITORING AND ASSESSMENT, 162(1-4), 265-275
Journal
Environmental Monitoring and Assessment
Pages
265-275
Date Issued
2010
Date
2010
Author(s)
Chen, Home-Ming
Abstract
When a domestic wastewater treatment plant (DWWTP) is put into operation, variations of the wastewater quantity and quality must be predicted using mathematical models to assist in operating the wastewater treatment plant such that the treated effluent will be controlled and meet discharge standards. In this study, three types of gray model (GM) including GM (1, N), GM (1, 1), and rolling GM (1, 1) were used to predict the effluent biochemical oxygen demand (BOD), chemical oxygen demand (COD), and suspended solids (SS) from the DWWTP of conventional activated sludge process. The predicted results were compared with those obtained using backpropagation neural network (BPNN). The simulation results indicated that the minimum mean absolute percentage errors of 43.79%, 16.21%, and 30.11% for BOD, COD, and SS could be achieved. The fitness was higher when using BPNN for prediction of BOD (34.77%), but it required a large quantity of data for constructing model. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were analogous to those of BPNN, even lower than that of BPNN when predicting COD (16.21%) and SS (30.11%). According to the prediction, results suggested that GM could predict the domestic effluent variation when its effluent data were insufficient. © 2009 Springer Science+Business Media B.V.
SDGs
Other Subjects
Back propagation neural networks; Constructing models; Conventional activated sludges; Domestic effluents; Domestic wastewater treatment plants; GM (1 , 1); Gray Model; Gray models; Mean absolute percentage error; Simulation result; Suspended solids; Treated effluent; Wastewater treatment plants; Backpropagation; Bioactivity; Biochemical oxygen demand; Effluent treatment; Effluents; Mathematical models; Neural networks; Oxygen; Sewage pumping plants; Wastewater; Wastewater reclamation; Wastewater treatment; Water recycling; Water treatment plants; Activated sludge process; activated sludge; artificial neural network; back propagation; biochemical oxygen demand; domestic waste; effluent; numerical model; wastewater; activated sludge; analytical error; article; artificial neural network; back propagation; biochemical oxygen demand; chemical oxygen demand; controlled study; effluent; intermethod comparison; mathematical model; prediction; suspended particulate matter; waste water management; waste water treatment plant; Models, Theoretical; Neural Networks (Computer); Sewage; Water Supply
Type
journal article
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