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

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...

Full description

Saved in:
Bibliographic Details
Published in:Water science & technology. Water supply 2021-11, Vol.21 (7), p.3946-3959
Main Authors: Souza, Ana Cláudia Oliveira e, Ferreira, Newton Libanio, da Silva, Flávio Vasconcelos
Format: Article
Language:English
Subjects:
Air
Artificial neural networks
Automation
Compressed air
Computer architecture
dissolved air flotation
Efficiency
Factorial design
Flotation
Flow rates
Flow velocity
Liquid phases
Modelling
Neural networks
process modeling
Prototypes
Recurrent neural networks
Removal
Salinity
Saturation
Sedimentation & deposition
Separation processes
Separation techniques
Simulation
Simulation models
Turbidity
Wastewater treatment
Water flow
Water treatment
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1606-9749
1607-0798
DOI:10.2166/ws.2021.152