Prediction of the effluent from a domestic wastewater treatment plant of CASP using gray model and neural network

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.

Modeling effluent heavy metal concentrations in a bioleaching process using an artificial neural network technique

“…The application of ANN to issues related to wastewater treatment and water resources conservation is rapidly gaining popularity due to their immense power and potential in the mapping of nonlinear system data. In the context of hydrological forecasting, recent studies have reported that ANN technique may offer a promising alternative for bioleaching (Acharya et al, 2006;Ozkaya et al, 2008;Liu et al, 2008;Jorjani et al, 2007;Nurmi et al, 2010;Laberge et al, 2000), rainfall-runoff modeling (Lin and Chen, 2004), stream-flow prediction (Raman and Sunilkumar, 1995;Kisi, 2004a), suspension of sediments (Kisi, 2004b), water resources (Cobaner et al, 2008), reservoir inflow forecasting (Coulibaly et al, 2005) and treatment of wastewater (Elmolla et al, 2010;Pai et al, 2009;Chen and Lo, 2010). The variation in the characteristics of a bioleaching system may be non-linear and multivariate, and the variables involved may have complex inter-relationships.…”

Development and application of reduced‐order neural network model based on proper orthogonal decomposition for BOD5 monitoring in river systems: Uncertainty analysis

“…So, most of studies have focused on NN models to use online measurable variables for prediction of BOD 5 [11,12]. Chen and Lo [19] predict the effluent BOD, COD, and suspended solids from a domestic wastewater treatment plant by means of three types of gray model and then results compared with those obtained using back-propagation neural network (BPNN) model. Onkal-Engin et al [14] developed a relationship between sewage odor and BOD by NN model and reported that NN model can be used to classify the sewage samples collected from deferent 2013 American Institute of Chemical Engineers locations of a wastewater treatment plant.…”

“…For prediction of dissolved oxygen and BOD concentrations in the Gomti River, two NN models were identify, validated and tested by Singh et al [18]. Chen and Lo [19] predict the effluent BOD, COD, and suspended solids from a domestic wastewater treatment plant by means of three types of gray model and then results compared with those obtained using back-propagation neural network (BPNN) model. The simulation results indicated that fitness was higher when using BPNN model for prediction of BOD, but it required a large quantity of data for constructing model.…”

Research on Effect and Mechanism of Sewage Treatment by SBR with Concrete Bio-Films

“…Due to its high pollutant removal rate, little floor area and other good characters, SBR has been widely applied in practice. Recently the development of SBR may be concluded as two respects, i.e., the improvement of equipment, such as mend of aeration devices and water decanter [1]; the process improvement, i.e., the development of intermittent water series such as technology of CASS [2], CAST [3] and CASP [4], the discovery of continuous water series such as technology of ICEAS, IDAL, IDEA, and the invention of stable liquid continuous flow series such as technology of MSBR, DAT-IAT, UNITANK, ASBR and their combination processes.…”