Interrogating common clarification models for unit operation systems with dynamic similitude | JoVE Visualize

Area of Science:

Environmental Engineering
Fluid Dynamics
Water Treatment

Background:

Traditional models like Surface Overflow Rate (SOR), Plug Flow Reactor (PFR), and Continuously Stirred Tank Reactor (CSTR) are widely used for clarification unit operations (UO).
These models rely on simplified assumptions about system geometry and hydrodynamics, which often diverge from real-world conditions.
The applicability and generalizability of these conventional models are questionable due to the complex nature of actual UO systems.

Purpose of the Study:

To critically examine the predictive capability and generalizability of common UO models for various systems, including hydrodynamic separators, tanks, and clarifiers.
To introduce a novel, physically interpretable model based on dynamic similitude for improved prediction of particulate matter (PM) separation.
To assess the performance of commercial hydrodynamic separators compared to simple tank geometries.

Main Methods:

Evaluation of common UO models using physical model data and high-fidelity large-eddy simulations (LES) for diverse UO systems.
Development and testing of a Similarity Modified Gamma (SMG) model, leveraging dynamic similitude and the dimensionless settling velocity (W, or Hazen number).
Analysis of non-linear characteristics of PM separation and the impact of turbulence vortical structures.

Main Results:

Common UO models exhibit significant errors (30-50%) in predicting PM separation and can lead to oversizing clarifiers by up to 904%.
The proposed SMG model demonstrates robustness and generalizability for PM separation across different particle characteristics and loading conditions in geometrically similar systems.
Commercial hydrodynamic separators showed no performance advantage over simple plain tank geometries; complex turbulence presents design challenges.

Conclusions:

Existing UO models lack the robustness and generalizability required for accurate PM separation prediction, impacting design scalability and economics.
The SMG model, grounded in dynamic similitude, provides a more reliable and interpretable approach for UO design and analysis.
The SMG model can potentially reduce the cost and effort associated with physical model testing and aid numerical simulations.