Factors influencing the ultrasonic degradation of emerging compounds: ANN analysis

The Ultrasound technology is proven to be effective for destroying organic pollutants in water. The degradation process has been recently exploited as an innovative option in the treatment of persistent emerging compounds. Although the consistent number of experimental studies in the field, practical information for process analysis and optimization is still needed in order to make ultrasound an effective wastewater treatment process. This paper provides a predictive model for the ultrasonic degradation of organic pollutants. The model is able to consider different parameters in contemporaneity and it is realized through an Artificial Neural Network (ANN) analysis. Some fundamental variables have been individuated among the easily-measurable ones for describing the phenomenology of sonication and a multilayer ANN has been built up and trained for predicting the kinetic constant of several emerging compounds. One of the main peculiarity of the proposed model is the possibility of choosing the ANN input neurons among both operating variables and physical-chemical characteristics of the pollutants. In this way, it has been created a tool for virtually predicting, optimizing and controlling any kind of oxidative process by the means of ultrasound. By linking the model to the pollutant characteristics, the simulation results are ad hoc for the application of interest. The results of the ANN training and the simulations of some factor influences are presented in this paper.