Optimization of operating parameters for efficient photocatalytic inactivation of Escherichia coli based on a statistical design of experiments

Abstract

In this work, the individual and interaction effects of three key operating parameters of the photocatalytic disinfection process were evaluated and optimized using response surface methodology (RSM) for the first time. The chosen operating parameters were: reaction temperature, initial pH of the reaction mixture and TiO2 P-25 photocatalyst loading. Escherichia coli concentration, after 90 minutes irradiation of UV-A light, was selected as the response. Twenty sets of photocatalytic disinfection experiments were conducted by adjusting operating parameters at five levels using the central composite design. Based on the experimental data, a semi-empirical expression was established and applied to predict the response. Analysis of variance revealed a strong correlation between predicted and experimental values of the response. The optimum values of the reaction temperature, initial pH of the reaction mixture and photocatalyst loading were found to be 40.3 °C, 5.9 g/L, and 1.0 g/L, respectively. Under the optimized conditions, E. coli concentration was observed to reduce from 10(7) to about 11 CFU/mL during the photocatalytic process. Moreover, all these results showed the great significance of the RSM in developing high performance processes for photocatalytic water disinfection.

Individual and interaction effects of operating parameters on the photocatalytic degradation under visible light illumination: Response surface methodological approach

“…The RSM based on the central composite experimental design (CCD), which is one of the most usual methods of using response surface methodological approach, was utilized to evaluate the combined effects of the three independent variables using twenty sets of experiments . Moreover, a second‐order polynomial equation was obtained to relate the response variable to the three independent variables, as follows: where Y (%) is the predicted response (degradation efficiency of MO), b 1 , b 2 , and b 3 are the coefficients for linear effects, b 12 , b 13 , and b 23 are the interaction coefficients, b 11 , b 22 , and b 33 are the quadratic terms, b 0 is the interception coefficient and A, B, and C are the independent variables (photocatalyst loading, MO initial concentration, and pH, respectively).…”

“…This is one of the most important drawbacks of TiO 2 which limits its practical applications. Hence, shifting TiO 2 sensitivity to the visible range will significantly increase its photocatalytic efficiency …”

Application of Core-Shell-Structured K2CO3-Based Sorbents in Postcombustion CO2 Capture: Statistical Analysis and Optimization Using Response Surface Methodology

“…This meant that there was only a 0.01% chance that the Fvalue occurred due to noise; thus, the model was highly significant [40,41]. The lack of fit of the F-value was not significant because the corresponding P-value was 0.0700 and higher than 0.0500 [42,43]. The lack of fit of the F-value should not be significant for a successful prediction of the response [43].…”

An intrinsic kinetic model for liquid‐phase photocatalytic hydrogen production

“…As OVAT overlooks interaction effect of variables, it is not a reliable approach for the optimization of the constants of a multivariable model. 49,50 To overcome this issue, the statistical design of experiment can be used as a novel approach that considers the interaction effect of variables and minimizes the number of required experiments. 51 On the other hand, it is expected that the treatment of biodiesel wastewater will soon be a significant issue due to the fast growth of biodiesel industries.…”

“…To obtain the kinetic parameters, most of the previous works on photocatalytic hydrogen production followed the “one‐variable‐at‐a‐time” (OVAT) approach. As OVAT overlooks interaction effect of variables, it is not a reliable approach for the optimization of the constants of a multivariable model . To overcome this issue, the statistical design of experiment can be used as a novel approach that considers the interaction effect of variables and minimizes the number of required experiments …”