A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process

On-line evaluating the SS removals for chemical coagulation using digital image analysis and artificial neural networks

“…Therefore, it is difficult to evaluate the removal of SS during coagulation using traditional mathematical models. ANN models are very effective in representing the relationships between input and output variables in complex, nonlinear systems, and they have been applied extensively to address the problems of process forecasting and process control in water and wastewater treatment applications (Zhang and Stanley 1999;Choi and Park 2001;Yu et al 2010). In this study, ANN models were used to evaluate the SS removal efficiency using on-line DIA monitoring data, including particle size (ED), gray level, total area, total volume, and the fractal dimension.…”

Developing an Expert System for Predicting Pollutant Dispersion in Natural Streams

“…Correlation of coefficient of real data with predicted values of ANN model in training stage was 0.7 and root mean square error of 193 (Tayfour & Singh, 2005). Although several studies in environmental engineering used artificial intelligence (ASCE, 2000;Choi & Park, 2001;Chang & Chang, 2006;Maier & Dandy, 1996;Dezfoli, 2003;Rajurkar, 2004;Sadatpour et al, 2005;Karamouz et al, 2004;Lu et al, 2003), only Tayfour and Singh (Tayfour & Singh, 2005) used artificial neural network to estimate longitudinal dispersion coefficient in natural rivers so in this study inventionally a new methodology for estimation of longitudinal dispersion coefficient in rivers is developed and results of this new method is compared with previous empirical relations.…”

“…Table 2 shows the range of variation of collected data and its parameters. The data set was collected from several references such as (Li et al, 1998;Pourabadei & Kashefipur, 2007;Tayfour & Singh, 2005;Choi & Park, 2001;Chatila, 1997 From collected data set (73 series) 70% of them used for training of the ANFIS model and remaining 30% used for testing of the ANFIS model. Train and test sets selected randomly and optimum structure of ANFIS model is determined by default conditions in MATLAB commercial software and trial and error procedure.…”

Influential Parameters on Biomethane Generation in Anaerobic Wastewater Treatment Plants

“…They showed that the liquid flow rate and pH of the inlet stream were the major variables in controlling the plant and the neural network presented desirable results in minimizing the plant fluctuations (Gontarski et al, 2000). In a different study, a hybrid technique providing principal component analysis (PCA) together with neural networks was used for optimal control of a wastewater treatment process (Choi & Park, 2001). The application of PCA in that case emerged as a novel idea at the time, since the input dataset could be reduced in order to solve the overfitting problem of the model.…”

“…Regarding the maximum coefficient of correlation (R) and the minimum mean absolute percentage errors (MAPE) of the predictions, the developed model showed a satisfactory performance in comparison with the pure ANN models. Therefore, the model developed could be recommended in order to optimize design considerations of the treatment process (Pai et al, 2009 (Zhu et al, 1998), optimal control of a wastewater treatment process integrated with PCA (Choi and Park, 2001), Kohonen Self-Organizing Feature Maps (KSOFM) to analyze the process data of municipal wastewater treatment plant (Timothy Hong et al, 2003), Unsupervised networks for modeling the wastewater treatment process (Garcia and Gonzalez, 2004;Hong and Bhamidimarri, 2003;Cinar, 2005), Grey Model ANN (GM-ANN) to predict suspended solids (SS) and COD of hospital wastewater treatment reactor effluents (Pai, 2007), on-line monitoring of a reactor (Luccarini, 2010 (Chen, 2003), control and supervise the submerged biofilm wastewater treatment reactor , modeling the nonlinear relationships between the removal rate of pollutants and their chemical dosages in a paper mill wastewater treatment plant Table 5 summarizes the aforementioned models together with the advantages and drawbacks which might be considered for selection in applied projects and utilization in industrial scales. As observed the most extensively used model is ANN.…”