Design of Experiments Applied to Antibiotics Degradation by Fenton’s Reagent

Abstract

Advanced oxidation technologies (AOTs) are processes affected by a large number of parameters such as iron (Fe 2+ ) and H 2 O 2 concentrations, pH, temperature, light intensity and chemical composition (organics and inorganics). In addition, for different industrial chemical processes, there are different effluents, which greatly vary in chemical composition from each other, not allowing a unique approach for all of them. Thus, it is necessary to adjust AOT parameters to the specific effluent to be treated. In such context, statistical design of experiments (DoE) and response surface methodology (RSM) emerge as important and widely used tools to determine the effects of multiple variables on wastewater treatment processes such as photo-Fenton. A revision of academic studies based on degradation of antibiotics-containing effluents is presented. The chapter also presents commercial cases of AOT and electrical efficiency considerations.

The Grey–Taguchi Method, a Statistical Tool to Optimize the Photo-Fenton Process: A Review

“…Optimization techniques are mainly classified into two groups: univariate and multivariate approach approaches. The univariate approach, known as one factor at a time, involves the evaluation of a single parameter in each experimental run [140,141]. In the 20th century, most of the studies carried out on the optimization of the Fenton process were based on the univariate approach.…”

Design of Experiment for the Optimization of Pesticide Removal from Wastewater by Photo-Electrochemical Oxidation with TiO2 Nanotubes

“…The study of such systems, where a wide number of variables may be influent, can be effectively performed by using the design of experiment (DoE) technique: excellent statistical packages exist to help in data analysis, such as Statistica, Minitab, Action Stat, Design Expert [22]. Compared to the classical experimental optimization methods, which are characterized by a "single process variable at a time" this technique represents a very useful tool which allows to evaluate the effects of multiple variables, with statistical accuracy in response, obtaining reliable results while saving time and resources [22].…”