Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144554
Title: Feature engineering using homogenization theory with multiscale perturbation analysis for supervised model-based learning of physical clogging condition in seepage filters
Authors: Chew, Alvin Wei Ze
Law, Adrian Wing-Keung
Keywords: Engineering::Civil engineering
Issue Date: 2019
Source: Chew, A. W. Z., & Law, A. W.-K. (2019). Feature engineering using homogenization theory with multiscale perturbation analysis for supervised model-based learning of physical clogging condition in seepage filters. Journal of Computational Science, 32, 21–35. doi:10.1016/j.jocs.2019.02.003
Journal: Journal of Computational Science
Abstract: Aquifer recharge and recovery systems (ARRS), which can broadly be analysed as seepage depth filters, in natural or engineered aquifers are gaining attention worldwide. Engineering predictions of their complex physical clogging behavior, however, continue to be challenging which has hindered the predictive maintenance of these systems for energy and materials savings. To address this problem statement, we leverage the homogenization theory with the multiscale perturbation analysis as the feature engineering step to reduce the complexity of the physical clogging behavior in ARRS. The analytical approach systematically derives a unique homogenized representation which quantifies the clogging condition at the macroscale. A series of physical parameters are identified from the derived homogenized representation to build a pre-processed input layer into our own multi-layered neural network (NN) architecture for predictive analysis. Measured data extracted from the literature is then used to train and verify the NN model. The trained model yields an average error deviation of 20% between the model's predictions and the respective measurements for an optimized set of hyperparameters tested. We then discuss quantitatively how the model can be adhered to predict the timing for a concerned ARRS to reach its breakthrough stage for a range of operational conditions. Finally, we also demonstrate how the homogenized representation can be useful to determine an arbitrary filter's critical reaction rate and diffusion coefficient responsible for its breakthrough stage.
URI: https://hdl.handle.net/10356/144554
ISSN: 1877-7503
DOI: 10.1016/j.jocs.2019.02.003
Rights: © 2019 Elsevier B.V. All rights reserved. This paper was published in Journal of Computational Science and is made available with permission of Elsevier B.V.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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