Eventos Anais de eventos
ENCIT 2022
19th Brazilian Congress of Thermal Sciences and Engineering
Parameter Estimation by Bayesian Statistics of Kinetic Models in Ozonation Processes
Submission Author:
Júlia de Oliveira , RS
Co-Authors:
Júlia de Oliveira
Presenter: Júlia de Oliveira
doi://10.26678/ABCM.ENCIT2022.CIT22-0208
Abstract
Ozonation is an advanced oxidative process that has been studied by researchers to improve water and wastewater treatments. Advanced oxidative processes (AOPs) are based on the generation of the hydroxyl radical (·OH), which is a powerful oxidizing agent, being able to degrade or even mineralize the toxic and persistent components that are not removed during the conventional processes. The kinetic models of ozonation, in which the most used is the pseudo-first order model, provide important information about the process, such as: through which oxidation route is mostly like to occur, which is informed through the parameters present in the models (kO3,P and kOH,P) and by a parameter called exposure ratio (Rt). This work aims to evaluate the kinetic constants of the reactions involving ozone, with data from the literature, in order to facilitate and guide the study of the technique in different pollutants for posteriors works. For the estimation of the parameters, Bayesian statistics was applied, by the method of Monte Carlo Markov Chain (MCMC) through the Metropolis-Hastings algorithm. The results obtained in this work were adequate and satisfactory. Also, through this work it is possible to observe that using the exposure ratio for the kinetic models directly interferes in the parameter values and that the MCMC method is an efficient and reliable mathematical approach.
Keywords
ozonation, Kinetics, Simulation, parameter estimation, Bayesian statistics
