Photocatalytic degradation of acesulfame K: Optimization using the Box–Behnken design (BBD)

• Published in: Process safety and environmental protection 2018-01, Vol.113, p.10-21
• Main Authors: Nam, Seong-Nam, Cho, Hyekyung, Han, Jonghun, Her, Namguk, Yoon, Jaekyung
• Format: Article
• Language: English
• Subjects: Acesulfame K; Artificial sweeteners; Box–Behnken design (BBD); Confidence limits; Design factors; Design optimization; Independent variables; Mineralization; Optimization; Organic matter; Persulfate; pH effects; Photocatalysis; Photodegradation; Reaction time; Regression analysis; Response surface methodology; Response surface methodology (RSM); Statistical analysis; Sweeteners; Variance analysis; Water treatment plants
• ISSN: 0957-5820; 1744-3598
• DOI: 10.1016/j.psep.2017.09.002

•Response surface methodology was applied to photocatalytic degradation of acesulfame.•Independent variables (Co, pH, persulfate and NOM) were coded at three levels (−1, 0 and 1).•A second-order polynomial regression model was derived to predict responses based on 30 experiments.•Linear terms of the model showed the highest contribution. In this research, photocatalytic degradation of acesulfame K, one of the most popular artificial sweeteners, has been carried out under variations of the initial concentration, pH, concentration of persulfate, and amount of natural organic matter (NOM). The removal efficiencies for 30-min, 60-min and 180-min reaction time have been applied to response surface methodology using the experimental responses obtained by a four-factor-three-level Box–Behnken design (BBD). This provided 29 experimental data for the initial concentration of acesulfame K ranging from 300 to 900μg/L, pH of solution ranging from 4 to 10, persulfate concentration ranging from 0 to 10mg/L, and amount of natural organic matter (NOM) ranging from 0 to 5mg/L, which were consecutively coded as A, B, C, and D at three levels (−1, 0, and 1). The analysis of variance (ANOVA) tests with 95% confidence limits determined the significance of independent variables and their interactions consisting of the polynomial regression equation. The optimum values of the selected variables were determined by numerical optimization, and the experimental conditions were found to reach complete mineralization for 30min and thereafter, at initial concentration of 887.2μg/L; pH of 4; persulfate concentration of 9mg/L, and NOM concentration of 5mg/L.