Abstract
This study examined various controls and operating parameters of a sewage treatment plant by performing multiple regression analyses on the operating parameters and the effluent quality of the sewage treatment plant. For the examination, operating data from April to November, 2012, was collected from a sewage treatment plant using the media anaerobic-anoxic-oxic method. The chemical oxygen demand (CODMn) and total nitrogen (T-N) forecasting models for the secondary sedimentation basin effluent were built through multiple regression analysis and showed Mean Absolute Percentage Error (MAPE) 6.6–9.3 and 16.0–23.7, respectively. All models showed similar results to real observational data. When controlling the operating parameters of CODMn and T-N, by using the regression model and the standardization regression coefficients without violating legal water quality criteria, operating conditions can be set that save an average of 23% of power consumption. Using this result, an operating guide for low energy consumption can be provided to the operators of sewage treatment plants.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Belsley, D. A., Kuh, E., and Welsch, R. E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity, John Wiley and Sons, New York.
Benedetti, L., De Baets, B., Nopens, I., and Vanrollenghem, P. A. (2010). “Multi-criteria analysis of wastewater treatment plant design and control scenarios under uncertainty.” Environmental Modeling and Software, Vol. 25, No. 5, pp. 616–621, DOI: 10.1016/j.envsoft.2009.03.003.
Dellana, S. A. and West, D. (2009). “Predictive modeling for wastewater applications: Linear and nonlinear approaches.” Environmental Modeling and Software, Vol. 24, No. 1, pp. 96–106.
Freund, R. J. and Littell, R. C. (2000). SAS System for regression (3rd ed.), Wiley Inter-Science, SAS Institute.
Fu, G., Butler, D., and Khu, S.-T. (2008). “Multiple objective optimal control of integrated urban wastewater systems.” Environmental Modeling and Software, Vol. 23, No. 2, pp. 225–234, DOI: 10.1016/j.envsoft.2007.06.003.
Hakanen, J., Sahlstedt, K., and Miettinen, K. (2013). “Wastewater treatment plant design and operation under multiple conflicting objective functions.” Environmental Modeling and Software, Vol. 46, pp. 240–249, DOI: 10.1016/j.envsoft.2013.03.016.
Henze, M., Grady, C. P. L. Jr., Gujer, W., Marais, G. V. R., and Matsuo, T. (1987). Activated Sludge Model No. 1, IAWQ Scientific and Technology Report No. 1, London, UK.
Jung, K. M. and Kim, M. G.(2007). Multivariate analysis, Kyo Woo Sa, Seoul.
Kim, J. D. (2008). Linear regression analysis using SAS, Free Academy, Seoul.
Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting, Butterworth Scientific, London.
Machón, I., López, H., Rodriguez-Iglesias, J., Marañón, E., and Vázquez, I. (2007). “Simulation of a coke wastewater nitrification process using a feed-forward neuronal net.” Environmental Modeling and Software, Vol. 22, No. 9, pp. 1382–1387, DOI: 10.1016/j.envsoft.2006.10.001.
Min, S.-Y. Lee, S.-P., Kim, J.-S., Park, J.-U., and Kim, M.-S. (2012). “Development and validation of multiple regression models for the prediction of effluent concentration in a sewage treatment process.” Environ. Eng. Res., Vol. 34, No. 5, pp. 312–315, DOI: 10.4991.
Park, B. J. (2006). Theory and application of modern statistics, Sigma Press, Seoul.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lee, SP., Kim, MS., Kim, JS. et al. Examination of possible energy conservation in a biological water treatment process using a multiple regression model. KSCE J Civ Eng 19, 880–886 (2015). https://doi.org/10.1007/s12205-014-0059-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-014-0059-4