Process optimization for microcystin-LR degradation by Response Surface Methodology and mechanism analysis in gas-liquid hybrid discharge system.
A gas-liquid hybrid discharge system was applied to microcystin-LR (MC-LR) degradation. MC-LR degradation was completed after 1 min under a pulsed high voltage of 16 kV, gas-liquid interface gap of 10 mm and oxygen flow rate of 160 L/h. The Box-Behnken Design was proposed in Response Surface Methodology to evaluate the influence of pulsed high voltage, electrode distance and oxygen flow rate on MC-LR removal efficiency. Multiple regression analysis, focused on multivariable factors, was employed and a reduced cubic model was developed. The ANOVA analysis shows that the model is significant and the model prediction on MC-LR removal was also validated with experimental data. The optimum conditions for the process are obtained at pulsed voltage of 16 kV, gas-liquid interface gap of 10 mm and oxygen flow rate of 120 L/h with ta removal efficiency of MC-LR of 96.6%. The addition of catalysts (TiO2 or Fe2+) in the gas-liquid hybrid discharge system was found to enhance the removal of MC-LR. The intermediates of MC-LR degradation were analyzed by liquid chromatography/mass spectrometry. The degradation pathway proposed envisaged the oxidation of hydroxyl radicals and ozone, and attack of high-energy electrons on the unsaturated double bonds of Adda and Mdha, with MC-LR finally decomposing into small molecular products.
['Zhang|Yi|Y|', 'Wei|Hanyu|H|', 'Xin|Qing|Q|', 'Wang|Mingang|M|', 'Wang|Qi|Q|', 'Wang|Qiang|Q|', 'Cong|Yanqing|Y|']
D000704:Analysis of Variance D002384:Catalysis D002853:Chromatography, Liquid D006861:Hydrogen Peroxide D017665:Hydroxyl Radical D007501:Iron D008387:Marine Toxins D013058:Mass Spectrometry D052998:Microcystins D010084:Oxidation-Reduction D010126:Ozone D012044:Regression Analysis D014025:Titanium D014874:Water Pollutants, Chemical D018508:Water Purification
Microcystin-LR degradation Degradation pathway Catalysts addition Gas–liquid hybrid discharge Multivariable optimization