Empirical modeling of turbidity removal in a dissolved air flotation system: application of artificial neural networks

Abstract

Abstract Dissolved air flotation (DAF) is a physical separation process that uses air microbubbles to remove suspended material dispersed in a liquid phase. Even though DAF is considered a well-established unit operation, modeling it is difficult due to the complexity of the phenomena involved, resulting in conceptual models with no practical application. Thereby, the objective of this work was to evaluate empirical modeling efficiency in predicting the turbidity removal dynamic using artificial neural networks applied to a DAF prototype. For the study of the neural network input variables, a two-level, full-factorial design was utilized to verify the statistical significance of the saturation pressure and the saturated water flow rate in relation to the turbidity removal. Using a time-delay recurrent neural network architecture, two empirical models were proposed to simulate the dynamic behavior of the turbidity removal promoted by the DAF prototype. The real-time model provided good predictions with R = 0.9717 and MSE = 1.0482, and the simulation model was also able to predict the process behavior presenting performance criteria equal to R = 0.9475 and MSE = 1.8640.