화학공학소재연구정보센터
Korean Journal of Chemical Engineering, Vol.27, No.2, 504-510, February, 2010
Settling state detection of SBR based on DO profile analysis using dynamic time warping
E-mail:
Settleability of activated sludge is one of the most important variables for stable solid-liquid separation of the biological wastewater process. Moreover, effective decanting is a sensitive work at sequencing batch reactor (SBR) which has a settleability fault, such as filamentous/non-filamentous bulking, deflocculation and sludge rising. It is not easy to monitor sludge settleability directly without any specified measurement system, but the values of settling phase can be measured by installing basic measuring instruments for monitoring the process in the reaction stage of SBR. In this study, patterns of DO profiles measured at settling phase showing significant difference according to the process status were used to explore whether a problem occurs or not. To use this information, an online algorithm was developed to detect and diagnose the settling fault. A dynamic programming method that is one of the pattern recognition methods was used to detect and classify the patterns of the DO profiles. Based on the discriminant function made by dynamic time warping results and an extracted variable from DO profiles, the classification rules were generated. With the discriminant function, the settleability fault was detected and classified successfully.
  1. Jenkins D, Richard MG, Daigger GT, Manual on the causes and control of activated sludge bulking and foaming, 2nd edn., Lewis Publishers, Boca Raton, USA (1993)
  2. Sekine T, Tsugura H, Urushibara S, Furuya N, Fujimoto E, Matsui S, Wat. Res., 23, 361 (1989)
  3. Yoo CK, Choi SW, Lee IB, Korean J. Chem. Eng., 19(3), 377 (2002)
  4. De Clercq J, Devisscher M, Boonen I, Vanrolleghem PA, Defrancq J, Wat. Sci. Tech., 47, 105 (2003)
  5. Andreottola G, Bortone G, Tilche A, Wat. Sci. Tech., 35, 113 (1997)
  6. Bae H, Choi DW, Cheon SP, Kim S, Kim Y, Korean Institute of Intelligent Systems, 15, 431 (2005)
  7. Poo KM, Im JH, Ko JH, Kim YJ, Woo HJ, Kim CW, Korean J. Chem. Eng., 22(5), 666 (2005)
  8. Kim DS, Jung NS, Park YS, Korean J. Chem. Eng., 25(4), 793 (2008)
  9. Baeza J, Gabriel D, Lafuente J, Environmental Modelling & Software, 14, 383 (1999)
  10. Beck MB, Latten A, Tong RM, Modelling and operational control of the activated sludge process in wastewater treatment, Professional Paper, 78-10, International Institute for Applied Systems Analysis, Laxenberg, Austria (1978)
  11. Sakoe H, Chiba S, IEEE Transactions on Acoustics Speech and Signal Processing, 26, 43 (1978)
  12. Sharma S, Applied multivariate techniques, John Wiley & Sons, USA (1996)