Maximizing the Treatment Potential of Secondary Clarifier Using CFD

Objective Computational
Fluid Dynamics (CFD) has proven to be a reliable and effective method for modeling secondary clarifiers in wastewater treatment plants. By simulating flow dynamics and solids distribution, CFD provides a detailed understanding of clarifier performance under various operational and structural configurations. This powerful tool enables plant operators and engineers to identify targeted solutions that enhance treatment efficiency and optimize plant operations.

Background
The Pomona Water Reclamation Plant (PWRP), operated by the Los Angeles County Sanitation District (LACSD), has encountered challenges related to its secondary clarifiers over the past decade. With the application of CFD the HDR team conducted a comprehensive optimization study to identify cost-effective modifications to improve flow and solids distribution in the mixed liquor (ML) channel and enhance clarifier performance, leading to higher treatment capacity.

Methodology
This study developed both two-dimensional (2D) and three-dimensional (3D) CFD models that reflect the physical characteristics and current performance of the clarifiers through calibration based on historical and field data. 2D CFD model, also known as HDR's high-accuracy clarifier model (HACM©), replicates the physical processes in the clarifiers using mathematical relationships to understand clarifiers performance impact from various modifications, such as flocculation baffle, mid-tank baffle, transversal launders, and channel baffle system. 3D CFD modeling was used in evaluation of hydraulic flow distributions among clarifiers, as well as solids concentration distributions.

2D Process Model Findings
A preliminary 2D HACM© CFD model has been developed using the plant's historical MLSS and flow data and refined through the effluent suspended solids (ESS) data collected in the field. The model provided insights of solids distribution in the clarifier (Figure 1), enabling the team to evaluate various modifications on clarifier performance (Table 1). In the base case with the existing configuration, the secondary clarifier has an ESS concentration of 39 mg/L. The preliminary modeling results indicate that replacing the existing 82ft-long launder with a combination of a 50ft longitudinal launder and five 18ft transversal launders could lead to a 33% reduction in ESS concentrations. Figure 1(b) shows that the shorter launder remediates the end wall effect, reducing turbulences caused by the wall and promotes a more effective settling zone. The proposed modifications, such as the combination of perforated flocculation baffle and mid-tank baffle, could further improve clarifier performance. As shown in Figure 1(c), the flocculation baffle and mid-tank baffle create a zone that promote solids settling and minimize resuspension of settled solids, resulting in a significant ESS concentration of 72% (Table 1).

Figure 1. Flow And Solids Concentration Distributions in Secondary Clarifiers Under Various Modifications

Table 1. Optimization Results with Various Secondary Clarifier Modifications

3D Model Findings
Results from the 3D CFD analysis indicated that solids distribution in the ML channel could be significantly improved with modifications such as a channel baffle. A critical component for satisfactory secondary clarifier performance is even solids distribution to the clarifiers. Under the base case scenario with existing configuration and without air additions in the secondary treatment system, the difference between the highest and lowest solids loadings across clarifiers is around 7%. In addition, as shown in Figure 2(a), the tracer response curve for clarifier 6 has a delayed time to peak and lower peak concentration, likely due to low velocity zones in the ML channel towards clarifier 6 (Figure 3(a)). The channel baffle provides an effective remediation by inducing additional mixing and enhanced local turbulence inside the channel (Figure 3(b)), which result in more uniform tracer responses (Figure 2(b)). The modeling results suggest that installing a baffle inside the ML channel can reduce the variation between the highest and lowest solids loadings from 7% to 2.5%.

Figure 2. Tracer Response Curves in (a) base case; (b) channel baffle case

Figure 3. Cross-sectional Views of the ML channel before Clarifier 6: Velocity Magnitude Distributions in (a) base case; (b) channel baffle case

Significance
The findings from this study highlight the potential for targeted modifications to significantly enhance secondary clarifier performance at the Pomona Water Reclamation Plant. By leveraging CFD modeling, the team identified and evaluated cost-effective solutions that address existing challenges, improve flow and solids distribution, and optimize clarifier efficiency. These proposed improvements not only enhance the plant's ability to manage higher hydraulic and solids loadings but also demonstrate the value of advanced modeling techniques in achieving operational excellence. This approach provides a replicable framework for similar facilities facing comparable challenges, supporting the long-term sustainability and reliability of wastewater treatment systems.lity and reliability of wastewater treatment systems.