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Several papers in the literature on Advanced Oxidation Processes (AOPs) confirm the process as a viable alter- native for the treatment of a variety of industrial effluents. In many of these works, modeling the variations of Chemical Oxygen Demand (COD) as a function of different experimental conditions was performed by techniques such as Design of Experiments, Artificial Neural Networks and Multivariate Analysis. These techniques require both a large number of parameters and a large quantity of experimental data for a systematic study of the model parameters as a function of ex- perimental conditions. On the other hand, the study of Stochastic Differential Equations (SDE) is presently well devel- oped with several practical applications noted in the literature. This paper presents a new approach in studying the varia- tions of COD in AOPs via SDE. Specifically, two effluents, from the manufacture of paints and textiles were studied by combined treatment of the photo-Fenton process and catalytic ozonization.

Modeling the Photocatalytic Process of Variation in Chemical Oxygen Demand via Stochastic Differential Equations