Chemical Oxygen Demand (COD) Estimation in Petrochemical Industry Wastewater Effluent via Robusted Regression

Document Type : Original Article

Authors

1 M.Sc., Faculty of Environmental Eng., Tehran University, Tehran, Iran.

2 Professor, Faculty of Environmental Engineering, University of Tehran, Tehran, Iran.

3 Assistant Professor, Faculty of Surveying Engineering , University of Tehran, Tehran, Iran.

Abstract

In order to increase the quality of industrial wastewater treatment and better manage of them, their approach should be simple and accurate for estimating process. Treatment processes for black box systems are due to the influence of many factors that involved in the system. Because of problems in using physical models, the use of statistics and regression methods could be helpful. Therefore, whatever model is simpler and less input variables so the model will be more important. Influent of the proposed model includes output data of biological unit and effluent is chemical oxygen demand of the clarifier. To compare the models performance three indicators of R-square, Correlation Coefficient(R) and Mean Square Error (MSE) are used. The aim of this study is creating linear data mining model and comparing them with similar methods for quality data. Finally, a linear robust regression with MSE = 0.089054, R = 0.784727 and R-Square = 0.6096 is proposed.

Keywords

References

 
حسنلو، ح.، مهردادی، ن.، نائب، ح. و گلبابایی، ف.، (1391)، "استفاده از روش تحلیل عاملی در مدل‌سازی عصبی واحد تصفیه پساب با نمک پایین تصفیه‌خانه فجر"، هفتمین همایش ملی و نمایشگاه تخصصی مهندسی محیط‌زیست، تهران، دانشکده محیط‌زیست.
شریعت‌زاده، م.، (1388)، سیمای زیست‌محیطی پتروشیمی فجر، نشریه داخلی پتروشیمی فجر.
مردانی، ن.، (1388)، "معرفی فرایند تصفیه پساب تصفیه‌خانه فجر"، نشریه داخلی پتروشیمی فجر.
Badi, H., and Baltagi, (2002), Econometrics, 3rd Edition, Springer.
Bates, B.C., and Campbell, E.P., (2001), “A Markov Chain Monte Carlo Scheme for parameter estimation and inference in conceptual rainfall-runoff modeling”, Water Recourses Research, 37(4), 937-947.
Bryan, F., and Manly, J., (2009), Statistics for environmental science and management, Taylor and Francis Group, International Standard Book Number-13: 978-1-4200-6147-5 (Hardcover).
Curlin, M., Bevetek, A., Ležajić, Z., Deverić Meštrović, B., and Kurtanjek, Z., (2008), “Modelling of activated sludge wastewater treatment process in municipal plant in Velika Gorica”, Chemistry in Industry, 57(2), 59-67.
Helsel, D.R., and Hirsch, R.M., (1992), Statistical methods in water resource, USGS.
Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R., (2004), “Least angle regression”, Annals of Statistics, 32(2), 409-499.
LeSage, J.P., (1998), Spatial econometrics, Department of Economics, University of Toledo.
LeSage, J.P., (1999), Applied econometrics using MATLAB, Department of Economics University of Toledo.
Li, J., Luo, G., He, L.J., Xu, J., and Lyu, J., (2017), “Analytical approaches for determining chemical oxygen demand in water bodies, A review”, Critical Reviews in Analytical Chemistry, 48(1), 47-65.
Lawless, J.F., and Wang, P., (1976), “A simulation study of Ridge and other regression estimators”, Communications in Statistics, Part A-Theory and Method, 5, 307-323.
Chun, T.S., M. A. Malek, M.A., and Ismail, A.R., (2017), “A review of wastewater treatment plant modelling: Revolution on modelling technology”, American Journal of Environmental and Resource Economics, 2(1), 22-26.
Wei, X., and Kusiak, A., (2015), “Short-term prediction of influent flow in wastewater treatment plant”, Stochastic Environmental Research Risk Assessment, 29(1), 241-249.
Zare Abyaneh, H., (2014), “Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters”, Journal of Environmental Health Science Engineering, 12(40), 1-8.
 
Journal of Water and Wastewater Science and Engineering
Volume 2, Issue 4
January 2018
Pages 14-23

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History

  • Receive Date: 28 May 2017
  • Revise Date: 15 March 2018
  • Accept Date: 06 May 2018

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