Studies on the treatment of the human urine using Electrochemical Advanced Oxidation Process

Abstract

Poor sanitation and lack of effective sustainable approach have become one of the major environmental issues of concern in developing countries like India. Moreover, with continuous discharge of recalcitrant organic pollutants from urinal wastewater into the surroundings has put human health and environment under the potential risk of threat. Therefore, a sustainable yet affordable solution for the safe disposal of urinal wastewater is required. Electro-oxidation (EO) under electrochemical advanced oxidation processes has drawn great attention for the treatment of a wide variety of recalcitrant toxic mixed effluents via generating in-situ strong reactive oxygen species (ROS) such as hydroxyl radical (OH ) and reactive chlorine species (RCS). In this study, the treatment performance of the EO process for synthetic urine/actual human urine (SU/AHU) and its metabolites in batch and continuous mode of operation using mixed metal oxide anode (MMO) and doped-mixed metal oxide (doped-MMO) anode was studied. Furthermore, best efforts have been made to integrate two processes i.e. photo-catalysis (PC) and EO within the same treatment unit for enhanced degradation of pollutants. For systemic studies, the effect of various selected operational parameters such as pH, current density, electrolyte (i.e. NaCl) dose and treatment time has been studied using response surface methodology (RSM). Box-Behnken design (BBD) under RSM has been employed for experimental designing, data analysis, the interaction between EO parameters, their optimization along with evaluation in terms of percentage Degradation (%Degradation) (Y1) and Energy consumption (Y2). A quadratic model was suggested by exploiting the sequential F-test and other adequacy measures. Furthermore, experimental data were then analyzed by multiple regression analysis of RSM and simultaneous optimization of various operational parameters for the responses was performed by the desirability function approach.