Development of a CFD-Based Artificial Neural Network Metamodel in a Wastewater Disinfection Process with Peracetic Acid
Publication: Journal of Environmental Engineering
Volume 146, Issue 12
Abstract
Computational fluid dynamics (CFD) have been applied to predict the performance of chemical water treatment disinfection systems in recent decades. However, computation times remain sufficiently long and prevent their use in optimal design. As an alternative, the use of an artificial neural network (ANN) metamodel to simulate CFD results was assessed. The ANN metamodel was trained by a series of CFD simulations of peracetic acid (PAA) disinfection characteristics in a chemical treatment reactor in the wastewater treatment process. The design space was sampled by applying a quasi-random sampling technique. A total of 40 CFD cases with 11 variables were obtained and used as input to the training process of the metamodel development. Metamodels were developed to predict disinfectant residual concentration and a microbial inactivation rate on full-scale reactors. The performance of the ANN-based metamodel is evaluated by comparison to CFD simulation results and pilot-scale experimental measurements. As a mathematical approximation method to a high dimensional nonlinear system, the ANN-based metamodel shows its ability to provide an efficient yet accurate solution to the wastewater disinfection process with PAA.
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Data Availability Statement
In this research, the code deriving all datasets was generated in MATLAB. Results for metamodel development were from a series of CFD simulations. All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This study was partially supported by PeroxyChem LLC and the LD Betz Endowment in Environmental Engineering at Drexel University.
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Published In
Journal of Environmental Engineering
Volume 146 • Issue 12 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Jan 13, 2020
Accepted: Jul 23, 2020
Published online: Oct 7, 2020
Published in print: Dec 1, 2020
Discussion open until: Mar 7, 2021
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