Kinetic fitting of substrate disappearance and total organic carbon (TOC) mineralization of organic pollutants, in water and air, by photocatalytic membranes immobilizing titanium dioxide, was carried out. A model was used in which mineralization of substrate to CO2 is supposed to occur, with kinetic constant k1, through one single intermediate, mediating the behaviour of all the numerous real intermediates formed in the path to CO2, kinetic constant of formation of the latter being k2. A competitive Langmuirian-type adsorption of both substrate and "intermediate" was also supposed to be operative, as expressed by pseudo-thermodynamic constants K1 and K2 respectively, these constants possessing a, partly at least, kinetic significance. Nonlinear models could be fitted to data by using the least-squares method. The very satisfactory matching is shown for the laboratory-scale mineralization kinetics of methane, as model molecule of aliphatic contaminants, both in the gas phase and in aqueous solution. Furthermore, in pilot plant experiments, using phenol, as model molecule of aromatics, modelling of quantum yields was carried out, as a function of concentration and of absorbed radiant power. Kinetics of hydroxyl radicals reacting between themselves, leading to hydrogen peroxide, other than with substrate or intermediates leading to mineralization, was considered, paralleled by a second competition kinetics due to superoxide anion radical and its conjugate acid, equally leading to mineralization. In this model the contribution of hydroxyl radicals to mineralization decreases with irradiance, while the contribution of superoxide anion radical and its conjugate acid increases. If the regression equations of these two contributions are considered together, in linear combination, the surface model perfectly fits the experimental data.
Nonlinear modelling of data in photomineralization kinetics of organic micropollutants by photocatalytic membranes immobilizing titanium dioxide in membrane reactors / I.R. Bellobono, R. Bianchi, G. de Martini, P.M. Tozzi, M. Bonardi, F. Groppi, M. Rossi. - In: JOURNAL OF CHEMOMETRICS. - ISSN 0886-9383. - 22:7(2008), pp. 425-435.
Nonlinear modelling of data in photomineralization kinetics of organic micropollutants by photocatalytic membranes immobilizing titanium dioxide in membrane reactors
I.R. BellobonoPrimo
;M. Bonardi;F. GroppiPenultimo
;
2008
Abstract
Kinetic fitting of substrate disappearance and total organic carbon (TOC) mineralization of organic pollutants, in water and air, by photocatalytic membranes immobilizing titanium dioxide, was carried out. A model was used in which mineralization of substrate to CO2 is supposed to occur, with kinetic constant k1, through one single intermediate, mediating the behaviour of all the numerous real intermediates formed in the path to CO2, kinetic constant of formation of the latter being k2. A competitive Langmuirian-type adsorption of both substrate and "intermediate" was also supposed to be operative, as expressed by pseudo-thermodynamic constants K1 and K2 respectively, these constants possessing a, partly at least, kinetic significance. Nonlinear models could be fitted to data by using the least-squares method. The very satisfactory matching is shown for the laboratory-scale mineralization kinetics of methane, as model molecule of aliphatic contaminants, both in the gas phase and in aqueous solution. Furthermore, in pilot plant experiments, using phenol, as model molecule of aromatics, modelling of quantum yields was carried out, as a function of concentration and of absorbed radiant power. Kinetics of hydroxyl radicals reacting between themselves, leading to hydrogen peroxide, other than with substrate or intermediates leading to mineralization, was considered, paralleled by a second competition kinetics due to superoxide anion radical and its conjugate acid, equally leading to mineralization. In this model the contribution of hydroxyl radicals to mineralization decreases with irradiance, while the contribution of superoxide anion radical and its conjugate acid increases. If the regression equations of these two contributions are considered together, in linear combination, the surface model perfectly fits the experimental data.Pubblicazioni consigliate
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