2. Wastewater treatment process

which can conduct an appropriate action to realize the accurate monitor and adjust to the

Conventionally, the measurement of the effluent quality indices can be performed by off-line or online instruments [8, 9]. However, the measurement time of the off-line or online measurement is long, for it requires several minutes to hours [10, 11]. The dynamic conditions in biological treatment processes such as the complex activated sludge process make the measurement challenging [12]. Therefore, prediction modeling method based on online sensors causes great attention. Wen et al. used an equation, derived from the material balance, to calculate the suspended solid concentration, and then employed to predict the treatment results through the sludge [13]. Yu et al. proposed two mechanism models, which were based on linear regression analyses of experimental results from two anaerobic filters, to predict the effect of recirculation on effluent quality of anaerobic filters [14]. The prediction ability was verified by several experiments, and superior results were realized. Bhowmick et al. presented a mathematical model based on the dynamic wave method, to simulate the effluent quality of the treatment system [15]. The abovementioned methods have realized the online prediction of the effluent quality. However, considering the complexity and nonlinearity of WWTP, it is reasonable to design the adaptive prediction model to improve the accuracy of the online

To improve the adaptive ability of the online prediction model, intelligent method, based on data-driven approach, has caused extensive concern [16, 17]. Zhao et al. presented a partial least-squares-based extreme learning machine to enhance the estimate performance in terms of accuracy and reliability for effluent quality indices [18]. The experimental results showed that the proposed prediction model could effectively capture the input-output relationship with favorable performance. Pai et al. applied five types of gray models to predict suspended solids, chemical oxygen demand and pH in the effluent from a wastewater treatment plant [19]. The results revealed that the gray models could predict the industrial effluent variation successfully. To improve the model accuracy, Perendeci et al. used a neural fuzzy model, based on an adaptive network-based fuzzy inference system, to estimate the effluent chemical oxygen demand by the related process variables [20]. Acceptable correlation coefficient (0.8354) and root mean square error (0.1247) were found between estimated and measured values of the system output variable, effluent chemical oxygen demand. However, considering the dynamic properties of WWTP, it is difficult to determine the reasonable fuzzy rules in this adaptive network-based fuzzy inference system. Aimed at this problem, Han et al. designed a flexible structure radial basis function neural network (FS-RBFNN) and applied it to estimate the water quality [21]. This FS-RBFNN could vary its structure dynamically in order to maintain the prediction accuracy, but it had poor

Considering the learning ability of neural network and the interpretability of rule-based fuzzy systems, an intelligent method, based on self-organizing fuzzy neural network (SOFNN), is developed to realize the online prediction of the effluent indices. The main advantages of this prediction model are summarized as follows. First, an efficient secondorder algorithm is designed to adjust the parameters of SOFNN, which enables to improve

dynamic operational stations, is still a challenging work [4, 7].

prediction.

92 Wastewater and Water Quality

interpretability.

WWTP is a large nonlinear system subject to large perturbations in influent flow rate and pollutant load, together with uncertainties concerning the composition of the incoming wastewater. It is also a complex reaction process, which contains biological, physical and chemical reactions. The most popular technology for wastewater treatment is the activated sludge process (ASP). The simplified flow chart of ASP is shown in Figure 1, where a primary sedimentation tank, a biochemical reaction tank and a secondary sedimentation tank are consisted. First of all, the dynamically changing influent flows into the primary sedimentation tank to remove the suspended solids. Then, the wastewater gets further processed in the biochemical reaction unit. In this unit, nitrification and denitrification are composed to achieve biological nitrogen removal. After that, the standard wastewater is discharged from the top of the secondary sedimentation tank, and the sludge is returned to the biochemical reaction unit from the bottom of the secondary sedimentation tank. During the reaction process, numerous process variables are contained to influence the treatment performance.

Effluent quality, taken as an important performance evaluation to reflect the treatment results, can provide a basis for water treatment plant management decisions to minimize the microbial risks and optimize the treatment operation. Standard effluent quality requires that the effluent organisms, such as effluent ammonia nitrogen, effluent total nitrogen and effluent suspended solid, remain in the required limits. Although the effluent quality indices can be measured directly by laboratory analysis, a significant time delay problem, which may range from a matter of minutes to a few days, is always unavoidable. This lack of suitable real-time process variable information limits the effective operation of effluent quality. Therefore, an online prediction model is essential to support water quality parameters. Since an approach based on neural networks does not make any assumptions about the functional relationship between the dependent and independent variables, it is suitable for capturing functional relationships between bacterial levels and other variables.

Figure 1. Simplified flow chart of ASP.
