研究目的
To design and optimize an open-channel water ultraviolet disinfection reactor using a methodology that combines computational fluid dynamics (CFD) modeling, design of experiment (DOE), response surface method (RSM), and goal-driven optimization (GDO), focusing on parameters such as lamp positioning and flow rate to achieve minimum UV dose and operational costs.
研究成果
The proposed methodology combining CFD, DOE, RSM, and GDO is effective for optimizing open-channel UV disinfection reactors, reducing experimental trial and error. Optimal lamp positioning (distance between lamps of 0.05 m and distance from bottom of 0.053 m) achieves a RED of 43.20 mJ/cm2 and head loss of 0.052 m. The approach is scalable for industrial applications and can be extended to other water treatment reactors like AOP systems.
研究不足
The study uses steady-state simulations instead of unsteady/transient due to computational resource constraints, which may not capture all dynamic effects. The optimization is based on approximated models (e.g., kriging), which might introduce errors. The range of geometric parameters is bounded based on practical experience, potentially limiting the exploration of all possible designs. The model assumes specific conditions like UVT of 90% and may not generalize to all water qualities.
1:Experimental Design and Method Selection:
The study employs a systemic methodology integrating CFD modeling with DOE, RSM, and GDO for optimization. A 3D geometric model of the open-channel UV reactor is created using CATIA V5R20, meshed with ANSYS ICEM CFD, and simulated using ANSYS Fluent. The VOF model is used for free surface tracking, k-ε turbulence model for flow, and UVCalc3D for fluence rate calculations.
2:Sample Selection and Data Sources:
The reactor configuration includes two banks of low-pressure UV lamps with specified dimensions. Water and air are the two phases considered, with water having a UV transmittance of 90%. Pathogens are modeled as inert particles injected at the inlet.
3:0%. Pathogens are modeled as inert particles injected at the inlet. List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: Software tools include CATIA V5R20 for modeling, ANSYS ICEM CFD for meshing, ANSYS Fluent for CFD simulations, ANSYS DesignXplorer for optimization, and MATLAB for data analysis. Hardware includes a workstation with Intel Xeon CPU X5680 processors.
4:Experimental Procedures and Operational Workflow:
The process involves creating the geometric model, meshing, setting up CFD simulations with steady-state conditions, performing grid independence and particle sensitivity analyses, conducting DOE with optimal Latin hypercube sampling, building response surfaces, and applying GDO to find optimal design parameters.
5:Data Analysis Methods:
Data is analyzed using response surface methodology and statistical comparisons between predicted and simulated values. Objective functions include reduction equivalent dose (RED) and head losses, optimized using screening methods in ANSYS DesignXplorer.
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