In biological wastewater treatment plants the biomass is separated from the treated wastewater in the secondary settler; thus, efficient operation of the secondary settler is crucial to achieving satisfactory effluent quality in the wastewater treatment process (WWTP). In the present work, system identification and soft-computing techniques were used to formulate a model for predicting the solid volume index (SVI) and classification of the sludge bulking phenomenon in the settler. An adaptive time series model was applied to predict the SVI of the secondary settler; this model uses the recursive least square (RLS) method to update the model parameters. The method for classifying the current state of the secondary settler is based on the strong correlation that was observed between the settler state and the values of the time series model parameters, which enabled the time series model parameters to be used as effective features for monitoring the secondary settler. To classify the current state of the secondary settler, a neural network_x000D_ (NN) was used to classify the adaptive time series model parameters, where a hybrid Genetic Algorithm (GA) was used to decide the number of hidden nodes of the NN classifier. Application of the proposed method to a full-scale WWTP demonstrated the utility of the method for simultaneously predicting the SVI value of the secondary settler and classifying the current state of the settler.

Auto-regressive Exogenous (ARX) Model Bulking Genetic Algorithm (GA) Neural Network (NN) Classifier Recursive Least Square (RLS) Method Solid Volume Index (SVI)

Bang YH, Yoo CK, Choi SW, Lee IB, "Nonlinear PLS Monitoring Applied to an Wastewater Treatment Process," Proceedings of ICCAS2001 Conference, Jeju, Korea, Oct. 17-20 (2001)
Belanche L, Valdes JJ, Comas J, Poch M, Artificial Intelligence Eng., 14, 307 (2000) 
Bishop CM, "Neural Networks for Pattern Recognition," Claprendon Press (1995)
Capodaglio AG, Jones HV, Novotny V, Feng X, Water Res., 25(10), 1217 (1991) 
Choi SW, Yoo CK, Lee KH, Lee IB, J. Chem. Eng. Jpn., 34(10), 1218 (2001) 
Goldberg DE, "Genetic Algorithms in Search, Optimization and Machine Learning," Addison-Wesley, Reading, MA (1989)
Hasselblad S, Xu S, Water Sci. Technol., 34(3-4), 323 (1996) 
Haykin S, "Neural Networks: A Comprehensive Foundation," Prentice Hall International (1999)
Himmelblau DM, Korean J. Chem. Eng., 17(4), 373 (2000)
Ko TJ, Cho DW, Adv. Manufacturing Technol., 12, 5 (1996) 
Lee KH, Lee JH, Park TJ, Korean J. Chem. Eng., 15(1), 9 (1998)
Lin CT, Lee CS, "Neuro-Fuzzy Systems," Prentice-Hall (1996)
Ljung L, "System Identification," PTR Prentice Hall (1987)
Mulligan AE, Brown LC, J. Environ. Eng., 124(3), 204 (1998)
Olsson G, Chapman D, Water Sci. Technol., 37(12), 405 (1988)
Rosen C, Olsson G, Water Sci. Technol., 37(12), 197 (1998) 
Tcholanoglous G, Burton FL, "Wastewater Engineering: Treatment, Disposal and Reuse," MaGraw-Hill Press (1991)
Teppola P, Mujunen SP, Minkkinen P, Chem. Intelli. Lab., 38, 197 (1997) 
Teppola P, Mujunen SP, Minkkinen P, Chem. Intelli. Lab., 41, 95 (1999)
Van Dongen G, Geuens L, Water Res., 32(3), 691 (1998) 
Wang CH, Hong TP, Tseng SS, IEEE Trans. Evolutionary Comput., 2(4), 138 (1998) 
Yoo CK, Choi SW, Park JH, Lee IB, "Time Series Analysis and Neural Network Classification of the Secondary Settler in the Wastewater Plant," Proceedings at 5th International IWA Symposium Systems Analysis and Computing in Water Quality Management, Gent, Belgium, September 18-20 (2000)
Yoo CK, Kim DS, Cho JH, Choi SW, Lee IB, Korean J. Chem. Eng., 18(4), 408 (2001)
Yoo CK, Choi SW, Lee IB, Water Sci. Technol., 45(4-5), 217 (2002)