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Development of computational fluid dynamics based artificial neural network metamodels for wastewater disinfection
Dissertation   Open access

Development of computational fluid dynamics based artificial neural network metamodels for wastewater disinfection

Wangshu Wei
Doctor of Philosophy (Ph.D.), Drexel University
Sep 2018
DOI:
https://doi.org/10.17918/rhgm-xt33
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Abstract

Artificial intelligence--Data processing Simulation methods Computational Fluid Dynamics Environmental Engineering Fluid Mechanics Mechanical Engineering
The mechanisms of the mixing of two streams are investigated numerically. The transport processes of both mass and heat transfer in the mixing of either two gases or two miscible liquids are studied with 3-D time-dependent computational fluid dynamics (CFD) simulations. The flow fields are calculated for the T-junction (of two circular cross-section pipes that meet orthogonally at a junction) which is one of the most common mixing devices in the study of mass and heat transfer. For turbulent flow regimes, the large eddy simulation (LES) technique is employed. Second-order differencing scheme is applied in the simulation to minimize numerical diffusion. The results obtained by the numerical simulations are verified with available experimental data in the literature for air-methane mixing in a T-junction. In the wastewater disinfection process, Peracetic acid (PAA) is an effective anti-microbial agent for disinfection and has a significant applicability in wastewater treatment processes. Similar to other chemical disinfection technologies, such as chlorination, and ozonation, the performance of PAA is determined by hydraulics of a disinfection contactor, chemical kinetics of microbial inactivation, and specific water characteristics. On-site physical modelling on pilot scale has been an important and usual method to validate the kinetics and the performance for a particular system. However, physical modelling is expensive and time consuming. CFD technique offers an alternative to physical modelling. With increasing computational power on personal computers (PCs), applying CFD simulation on a full-scale reactor is becoming more and more achievable. With proven accuracy on predicting the performance of full scale facilities, CFD is now playing a significant role in the wastewater treatment industry. A transient CFD simulation of a full-scale contactor with reasonable accuracy can be achieved within hours on PCs, which significantly lower the cost of undertaking physical modelling experiments. Metamodels based on artificial neural network (ANN) are computationally efficient mathematical approximations to a highly complex system with multiple non-linear features. With a parametric study of CFD simulations providing training dataset, the metamodel is able to be calibrated to predict the performance of a variety contactors with different geometries, inlet conditions and chemical kinetics. In this thesis, the results of microorganism inactivation and PAA decay in full scale contactors using three-dimensional CFD models are presented. Then the results of the simulations were applied to develop ANN-based metamodels. The performance of metamodels were validated with both CFD simulations and pilot scale experiments.

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