Environmental Engineering Research

Korean Society of Environmental Engineers (대한환경공학회)
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A Study on the Influence of a Sewage Treatment Plant's Operational Parameters using the Multiple Regression Analysis Model

  • Lee, Seung-Pil (Environmental Technology Institute, Samchully Enbio Co., Ltd.) ;
  • Min, Sang-Yun (Environmental Technology Institute, Samchully Enbio Co., Ltd.) ;
  • Kim, Jin-Sik (Environmental Technology Institute, Samchully Enbio Co., Ltd.) ;
  • Park, Jong-Un (Environmental Technology Institute, Samchully Enbio Co., Ltd.) ;
  • Kim, Man-Soo (Environmental Technology Institute, Samchully Enbio Co., Ltd.)
  • Received : 2013.06.03
  • Accepted : 2013.10.23
  • Published : 2014.03.30
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Abstract

In this study, the influence of the control and operational parameters within a sewage treatment plant were reviewed by performing multiple regression analysis on the effluent quality of the sewage treatment. The data used for this review are based on the actual data from a sewage treatment plant using the media process within the year 2012. The prediction models of chemical oxygen demand ($COD_{Mn}$) and total nitrogen (T-N) within the effluent of the 2nd settling tank based on the multiple regression analysis yielded the prediction accuracy measurements of 0.93 and 0.84, respectively; and it was concluded that the model was accurately predicting the variances of the actual observed values. If the data on the energy spent on each operating condition can be collected, then the operating parameter that conserves energy without violating the effluent quality standards of COD and T-N can be determined using the regression model and the standardized regression coefficients. These results can provide appropriate operation guidelines to conserve energy to the operators at sewage treatment plants that consume a lot of energy.

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References

  1. Korea Ministry of Environment. Sewage statistics. Gwacheon: Ministry of Environment; 2010.
  2. Henze M, Grady CP, Gujer V, Marais GV, Matsuo T. Activated sludge model No. 1. London: International Association on Water Pollution Research and Control; 1987.
  3. Woo DJ. Model based predictive control algorism development and application in A2/O process [master's thesis]. Busan: Pusan National University; 2011.
  4. Choi SY. Fault diagnosis of a biological wastewater treatment plant by multivariate statistical approaches and development of a simplified activated sludge model [master's thesis]. Daegu: Kyungpook National University; 2011.
  5. Woo DJ, Kim H, Kim YJ, et al. Development and evaluation of model-based predictive control algorithm for effluent NH4- N in A2/O process. J. Korean Soc. Environ. Eng. 2011;33:25-31. https://doi.org/10.4491/KSEE.2011.33.1.025
  6. Min SY, Lee SP, Kim JS, Park JU, Kim MS. Development and validation of multiple regression models for the prediction of effluent concentration in a sewage treatment process. J. Korean Soc. Environ. Eng. 2012;34:312-315. https://doi.org/10.4491/KSEE.2012.34.5.312
  7. Benedetti L, De Baets B, Nopens I, Vanrolleghem PA. Multicriteria analysis of wastewater treatment plant design and control scenarios under uncertainty. Environ. Model. Softw. 2010;25:616-621. https://doi.org/10.1016/j.envsoft.2009.06.003
  8. Dellana SA, West D. Predictive modeling for wastewater applications: linear and nonlinear approaches. Environ. Model. Softw. 2009;24;96-106. https://doi.org/10.1016/j.envsoft.2008.06.002
  9. Hakanen J, Sahlstedt K, Miettinen K. Wastewater treatment plant design and operation under multiple conflicting objective functions. Environ. Model. Softw. 2013;46:240-249. https://doi.org/10.1016/j.envsoft.2013.03.016
  10. Fu G, Butler D, Khu ST. Multiple objective optimal control of integrated urban wastewater systems. Environ. Model. Softw. 2008;23:225-234. https://doi.org/10.1016/j.envsoft.2007.06.003
  11. Belsley DA, Kuh E, Welsch RE. Regression diagnostics: identifying influential data and sources of collinearity. New York: John Wiley & Sons; 1980.
  12. Kim JD. Linear regression analysis using SAS. Seoul: Free Academy; 2002.
  13. Park BJ. Theory and application of modern statistics. Seoul: Sigma Press; 2006.
  14. Jung KM, Kim MG. Multivariate analysis. Seoul: Kyo Woo Sa;2007.

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