Modelling clogging dynamism within dual-media pre-treatment rapid filters in seawater desalination
Chew, Alvin Wei Ze
Law, Adrian Wing Keung
Date of Issue2017
School of Civil and Environmental Engineering
Nanyang Environment and Water Research Institute
The dual-media rapid filtration is a popular pre-treatment technology in seawater desalination industry. However, it is still difficult for operators to optimize both the filtration and backwashing processes, particularly during the rapid filtering of moderate and high turbid (MHT) influents, due to the lack of a good understanding of the clogging dynamism incurred during operations. In this study, we attempt to model the incurred clogging dynamism via both experimental and computational means. For the former, experimental filtering of MHT influents was carried out within a lab-scale dual-media rapid pressure filter under varying experimental conditions. Concurrently, dimensionless analysis of the particle removal constant (RC) parameter was performed by considering the initial influent conditions and media characteristics. Good agreement was achieved between the predicted values from the dimensionless formulation of RC and the respective experimental values, which renders the likelihood of employing the formulation to improve the design of pre-treatment rapid filters for filtering MHT influents. Lastly, we attempt to model the clogging dynamism with the homogenization upscaling approach, by computationally resolving both the macroscale and microscale hydraulic gradient under clean and increasing clogging filter condition respectively to approximate the total clogging hydraulic gradient (CHG). Good agreement between the computational and semi-empirical values was achieved for the clean filter condition. Developing the solver in an open-source Open Field Operation and Manipulation (OpenFOAM) CFD software to investigate the particles’ interactions with a singular collector grain for approximating the microscale CHG is currently in progress.
Dual-media Rapid Filters
© 2017 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).