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Construction of regularized regression model for chlorine disinfection of rotavirus in secondary treated wastewater

Reuse of treated wastewater is one of the promising options to solve water shortages. Water-borne viruses which can remain in secondary treated wastewater need to be sufficiently inactivated. Log-reduction values of virus is determined by disinfection intensity; however, disinfection efficiency is affected by physicochemical and biological factors of secondary treated wastewater. The objective of this study is to establish a universal method to determine appropriate disinfection intensity to achieve sufficient log-reduction value of virus in secondary treated wastewater using machine learning and mathematical modeling. In this study, I construct a regularized regression model for rotavirus inactivation in secondary treated wastewater, in which the operational parameters of wastewater treatment plant are used as explanatory variables.