Response surface methodology (RSM) is a collection of statistical and mathematical techniques useful for developing,
improving, and optimizing processes [19]. The most extensive applications of RSM are in the particular situations where several input variables potentially influence some performance measure or quality characteristic of the process. Thus the performance measure or quality characteristic is called the response. The input variables are sometimes called independent variables, and they are subject to the control of the scientist or engineer. The field of response surface methodology consists of the experimental strategy for exploring the space of the process or independent variables; empirical statistical modeling to develop an appropriate approximating relationship between the yield and the process
variables; and optimization methods for finding the values of the process variables that produce desirable values of the response. RSM uses an experimental design such as the central composite design (CCD) to fit a model by the least-squares technique [20,21]. The adequacy of the proposed model is then revealed by using the diagnostic checking tests provided by analysis of variance (ANOVA). The response surface plots can be employed to study the surfaces and locate the optimum. In several industrial processes, RSM is almost routinely used to evaluate the results and efficiency of operations [22–26]. Hence, in this study, the main objective was to optimize ozonation experimental conditions (pH
and temperature) for the production of N-nitrosamines by using the CCD method in wastewater matrices.
Response surface methodology (RSM) is a collection of statistical and mathematical techniques useful for developing,improving, and optimizing processes [19]. The most extensive applications of RSM are in the particular situations where several input variables potentially influence some performance measure or quality characteristic of the process. Thus the performance measure or quality characteristic is called the response. The input variables are sometimes called independent variables, and they are subject to the control of the scientist or engineer. The field of response surface methodology consists of the experimental strategy for exploring the space of the process or independent variables; empirical statistical modeling to develop an appropriate approximating relationship between the yield and the processvariables; and optimization methods for finding the values of the process variables that produce desirable values of the response. RSM uses an experimental design such as the central composite design (CCD) to fit a model by the least-squares technique [20,21]. The adequacy of the proposed model is then revealed by using the diagnostic checking tests provided by analysis of variance (ANOVA). The response surface plots can be employed to study the surfaces and locate the optimum. In several industrial processes, RSM is almost routinely used to evaluate the results and efficiency of operations [22–26]. Hence, in this study, the main objective was to optimize ozonation experimental conditions (pHand temperature) for the production of N-nitrosamines by using the CCD method in wastewater matrices.
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