The UV/Cl2 process (also known as chlorine photolysis, which is the combination of chlorine and simultaneous irradiation of UV light) is conventionally applied at acidic mediums for drinking water treatment and further treatment of wastewater effluents for secondary reuse. This is because the quantum yield of HO• from HOCl (ϕHO•, 254 = 1.4) is greater than the one from OCl- (ϕHO•, 254 = 0.278) by approximately 5 times. Moreover, chlorine photolysis in acidic mediums also tends to have lower radical quenching rates than that of their alkaline counterparts by up to 1000 times. The aim of this research is to investigate the applicability of the UV/Cl2 process by assessing its efficacy on the removal of trimethoprim (TMP) at not only acidic to neutral conditions (pH 6-7), but also alkaline mediums (pH 8-9). At alkaline pH, free chlorine exists as OCl- and since OCl- has a higher molar absorption coefficient as compared to HOCl at higher wavelengths, there would be higher reactive chlorine species (RCS) formation and contribution. TMP removal followed pseudo-first order kinetics and depicted that a maximum fluence based constant (kf′ = 0.275 cm2/mJ) was obtained using 42.25 μM (3 mg/L) of chlorine at pH 9, with an irradiation of 275 nm. At alkaline conditions, chlorine photolysis performance followed the trend of UV (275)/Cl2 > UV (265)/Cl2 > UV (310)/Cl2 > UV (254)/Cl2. RCS like Cl•, Cl2−• and ClO• contributed to the degradation of TMP. When the pH was increased from 6 to 8, contribution from hydroxyl radicals (HO• ) was decreased whilst that of RCS was increased. Application of UV (310)/Cl2 had the highest HO• generation, contributing to TMP removals up to 13% to 48% as compared to 5% to 27% in UV (254, 265, 275)/Cl2 systems at pH 6-9. Artificial neural networks modelling was found to be able to verify and predict the contribution of HO• and RCS conventionally calculated via the general kinetic equations in the UV/Cl2 system at 254, 265, 275 and 310 nm.
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- Ultraviolet light-emitting diodes (UV-LED)
- Reactive chlorine species (RCS)
- Machine learning
- Artificial neural network modelling