Talebi Hematabadi P, Zarei Mahmoud Abadi H, Eslami H. Determination of a Statistical Model to Predict COD and TKN from the BOD5 and NH4+ Results
. J Environ Health Sustain Dev 2016; 1 (2) :82-90
URL:
http://jehsd.ssu.ac.ir/article-1-68-en.html
Department of Natural Recourses Engineering, Branch of Environmental Pollution, School of Agriculture and Natural Resources, Islamic Azad University of Meybod, Yazd, Iran.
Abstract: (2315 Views)
Introduction: The development of an appropriate model for the quality control of an industrial wastewater treatment system can save the time as well as the cost. This study was performed to determine an appropriate model in order to predict the COD and TKN parameters by BOD5 and NH4+ in the Meybod industrial estate wastewater treatment plant (WWTP).
Materials and Methods: This descriptive – analytical study was performed on 120 samples of the influent and effluent of the industrial estate wastewater treatment plant in Jahan Abad, Meybod, Yazd in 2015. The studied parameters were BOD5, TKN, COD, and NH4+. After measuring, they were imported to SPSS and Excel software to determine the relationship between them and then the linear regression model of the statistical method was used.
Results: The predictive results of COD values on the basis of BOD5 in the regression model showed that the coefficient of determination was 0.88 and the correlation coefficient was 0.93 (p = 0.00) for this relationship. The prediction of TKN values on the basis of NH4+ in the regression model
showed that for this relationship the determination coefficient of TKN and NH4+ influent parameters was 0.87 and the correlation coefficient was 0.93
(p = 0.00).
Conclusion: This study represented that using the linear regression model for predicting COD and TKN values through BOD5 and NH4+ was in close accordance with the laboratory data and can thus be applied when the Meybod industrial estate WWTP faces time limitations or sampling problems.
Type of Study:
Original articles |
Subject:
Special Received: 2017/08/9 | Accepted: 2017/08/9 | Published: 2017/08/9