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Modeling of the amount of sulfide production in sewage collectors in Mashhad ‎


Ehsan Azimi Ghalibaf‎, Kamran Davari‏‎, Saeed Reza Khodashenas‎, Hossein ‎Ansari‎, Mohammad Zaghian‎, Sanaz Sagha Pirmard‎ ‎ ‎

Abstract

This study is the fourth phase of the project "Investigation of the potential of corrosion of urban sewage networks through investigation of the concentration of hydrogen sulfide and reduction oxidation potential". The purpose of this study was to model and predict the production of sulfide in sewage in Mashhad. Sulfide production is the main cause of the phenomenon of corrosion and instability in operating conditions of sewage installations and, if possible, prediction of it can improve and stable the conditions of exploitation. The annual damage caused by sulfide production in sewage infrastructure installations around the world is over millions of dollars spent on rebuilding the installations. In this study, sewage collectors in west of Mashhad, with a length of 16 km, were selected and evaluated for quantitative and qualitative parameters by selecting 11 monitoring stations during a year. According to the results of the evaluations, there was a significant relationship between sulfide production and quality indices of ORP, COD, pH and temperature, as well as quantitative parameters such as hydraulic radius, retention time and the ratio of surface to volume of sewage collectors. The results of the multivariate regression model allow achieving the sulfide production prediction model based on the quality indices with a correlation coefficient R = 0.77 and the coefficient of determination R2 = 0.61, also using quantitative and qualitative indices together, a model with a correlation coefficient R = 0.88 and coefficient of determination R2 = 0.77 were obtained. Considering the necessity of the model's applicability and the possibility of measuring the indices online, an appropriate model was presented for the operators with a correlation coefficient R = 0.77 and coefficient of determination R2 = 0.59 to predict the amount of sulfide‎‎‎‎‎.




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