- Title
- Prediction of clogging in stormwater filters using artificial neural network
- Creator
- Lin, Junlin; Kandra, Harpreet; Choudhury, Tanveer; Barton, Andrew
- Date
- 2018
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/166691
- Identifier
- vital:13481
- Identifier
-
https://doi.org/10.1109/ISIE.2018.8433758
- Identifier
- ISBN:9781538637050 (ISBN)
- Abstract
- Stormwater filtration technologies play a significant role in improving water quality and making treated water available for non-potable uses. However, during treatment processes, contaminants such as suspended solids would lead to clogging in storm water filters, especially those with high infiltration rates. There are several parameters that affect clogging of filters, and a major challenge is to understand the parameter interdependencies, correlations and their individual effects. A robust methodology is, thus, required to accurately predict clogging which would contribute to the development of filtration technologies and in predictive maintenance. This research employs the use of Artificial Neural Network (ANN) model to predict clogging performance of stormwater filters under different operational conditions using experimental data from previous work. A single hidden layer ANN model with 19 hidden layer neurons was developed in this preliminary work.; IEEE International Symposium on Industrial Electronics
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 27th IEEE International Symposium on Industrial Electronics, ISIE 2018; Cairns, Australia; 13th-15th June 2018 Vol. 2018-June, p. 771-776
- Rights
- Copyright © 2018 IEEE.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Artificial neural network; Back propagation; Clogging prediction; Filter; Hydraulic performance; Storm water
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