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CFD and physical models of PM separation for urban drainage hydrodynamic unit operations
Water Research 154 (2019) 258e266
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CFD and physical models of PM separation for urban drainage hydrodynamic unit operations Hua Liu, John Sansalone* Engineering School of Sustainable Infrastructure and Environment, U. of Florida, Gainesville, FL, 32611, United States
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 August 2018 Received in revised form 8 January 2019 Accepted 26 January 2019 Available online 14 February 2019
A hydrodynamic separator (HS) is a common pre-treatment unit operation (UO) for separating coarser particulate matter (PM). While HS designs have disparate internal geometries, surface areas, and volumes; for a given PM granulometry this study hypothesizes there are limits to PM separation. In this study, a large group of full-scale HS units were tested with physical and computational fluid dynamics (CFD) models to evaluate HS functionality based on surface overflow rate (SOR), HS geometrics (diameter, height, baffling), PM granulometry, particle size distribution (PSD), turnover volume, hydraulic capacity, and short-circuiting. Models were loaded with a hetero-disperse PSD, a common testing metric. PM separation results show that PSD variability greater than ±2% from this metric are different (a ¼ 0.05). Comparing PM separation using the PSD metric across the SOR range: (1) all HS results were below the ideal SOR model, (2) above Hazen's tanks-in-series model with N ¼ 1, (3) above plain tank designs, and (4) declined logarithmically in a ±10% range. Integrated across a triangular loadgraph, the SOR model significantly over-estimated PM separation for all HS units. The SOR model deviated from the validated CFD model, as PSD loadings became increasingly mono-disperse. A validated CFD model is shown to be a valuable tool to examine HS design and functionality. © 2019 Published by Elsevier Ltd.
Keywords: Best management practice Computational fluid dynamics Stormwater Particle size distribution
1. Introduction Urbanization after World War II in the USA with the commensurate increase in impervious urban/suburban areas and pavement as well as anthropogenic activity such as motor vehicle transport has significantly increased PM and chemical loads transported in the hydrologic cycle (Nichols et al., 2015; Sansalone et al., 1998). In the USA, the concept of Best Management Practices (BMPs) for urban drainage clarification was introduced, in large part, through the Clean Water Act (CWA, 1972). For the goal of clarification and load reduction, aside from hydrologic control, BMPs (as a unit operation, UO) are now used extensively as a structural UO or nonstructural practice. A hydrodynamic separator (HS) is one class of structural and proprietary UOs that has evolved to fit within the geometric confines of urban drainage networks. As such, HS units are compact, passive, yet do not provide any flow/volume mitigation. Proprietary HS units are contrasted with common structural non-proprietary systems such as detention/retention basins,
* Corresponding author. E-mail address: jsansal@ufl.edu (J. Sansalone). https://doi.org/10.1016/j.watres.2019.01.057 0043-1354/© 2019 Published by Elsevier Ltd.
wetlands, filter strips, infiltration, swales, and combinations thereof. (Tran and Kang, 2013). All of these UO systems provide varying levels of clarification for PM and any PM-associated chemicals (Lee et al., 2010). Over 50,000 HS units have been installed in North America (Lee et al., 2014) for the purpose of pretreatment. Thus, in recent decades, there has been increasing interest in examining the factors that impact the clarification functionality of HS units. For UOs designed for clarification, separation of PM (and debris) is the most common assessment metric. HS units are comparatively small structural UOs that are most effective for temporarily separating coarse PM and debris (Schmitt et al., 2014). All HS units function predominately as small-footprint sedimentation units despite internal geometric variations between HS units such as screens or baffles. The predominance of sedimentation, as compared to other physical mechanisms, has also been shown for UO that combine sedimentation and filtration (whether depth or surficial) in the same unit volume (Herr and Sansalone, 2015; Liu et al., 2010). PM is the primary vehicle for chemical and pathogen transport, and separation thereof is the most common index for urban drainage treatment; whether the metric is PM concentration
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Nomenclature
Mi
ASTM BHS BMP Cin Cin,i
MO MT MDI MTFR n N
Cout Cout,i CA-ETV CFD CWA d10 d50 d90 D DN H HS k Me MHS
American Society for Testing and Materials Baffled hydrodynamic separator Best management practice mean inlet concentration, mg/L Eluted PM (measured as SSC) between influent sample points, mg/L Mean effluent concentration, mg/L Eluted PM (measured as SSC) between effluent sample points, mg/L Canadian Environmental Technology Verification Computational fluid dynamics Clean Water Act Diameter at which 10% of the PSD mass is smaller particles, mm Diameter at which 50% of the PSD mass is smaller particles, mm Diameter at which 90% of the PSD mass is smaller particles, mm Unit (HS) diameter, m Number of PM diameter increments (Discretization Number) Unit height, m Hydrodynamic separator Gamma parameters of PSD shape Effluent PM mass (measured across the total treatment volume), kg PM mass separated by HS unit for a treatment run volume, kg
{historically measured as suspended sediment concentration (SSC) or total suspended solids (TSS)}, or more recently as a particle size distribution (PSD) of PM. PM imparts oxygen demand, generates turbidity thereby impacting photosynthesis while providing significant surface area for partitioning of chemicals to and from the aqueous and PM phases (Geronimo et al., 2014; Ma and Zhu, 2014; Thompson et al., 2016; Sansalone and Ying, 2008; Ying and Sansalone, 2010). HS units are most commonly located in urban source area watersheds. These HS units are subject to highly unsteady and uncontrolled urban drainage transport (such as mass or flow-limited transport) of non-stationary PM granulometry and PM concentration which are coupled phenomena (Sansalone and Pathapati, 2009; Kim and Sansalone, 2008). For a UO such as a HS loaded by hetero-disperse PSDs generated from unsteady transport, previous research has demonstrated a positive relationship between PM concentration (as SSC) and PM separation because higher concentrations were generated from predominately coarser PM (Sansalone and Kim, 2008). Examination of PM separation from uncontrolled urban drainage treatment testing is more complex based on variable gravimetric PM concentrations and PSDs, albeit preferably facilitated on a per-particle size analysis based on discretizing the PSD (Spelman and Sansalone, 2018). However, variability of PM concentration and PSD as well as hydraulic unsteadiness requires a more complex evaluation of a UO, or comparison between UOs as is carried out in this study for a series of HS units. In contrast to the uncontrolled and highly variable loadings for in-situ HS units subject to urban drainage conditions, controlled testing of HS units is conducted with an invariable PSD. Flowrate and SOR notwithstanding, the PSD is a primary loading and granulometric parameter to test the response of a UO.
NJDEP NTU PM PSD RPD RT50 Sg SD SHS SOR SSC SUOL UO Vin,i VT VFD
l t50
259
Influent PM mass (measured across the total treatment volume), kg Total PM mass eluted from the HS, kg Total PM mass injected into the HS, kg Morrill dispersion index Maximum manufactured treatment flow rate Number of samples or analyses Hazen's index of non-ideal hydrodynamics of a unit operation New Jersey Department of Environmental Protection Nephelometric turbidity unit Particulate matter Particle size distribution Relative percent difference, % Theoretical median residence time, s Specific gravity Standard deviation Screened hydrodynamic separator Surface overflow rate, L/min.-m2 Suspended sediment concentration, mg/L Stormwater Unit Operations Laboratory Unit operation Volume between influent sample points, L Total flow volume through HS during injection of PM mass, L Variable frequency drive Gamma parameters of PSD diameter scale Theoretical median hydraulic residence time, s
The importance of the PSD applies whether examining separation or scour, as is the use of a PSD metric when comparing UOs subject to the same controlled loading conditions (a fixed PSD, PM concentration and SOR) in a testing protocol. Classical models such as ideal SOR and more recently recent models such as computational fluid dynamics (CFD) are tools that have been suggested to predict, validate or extrapolate the PM separation results of a physical HS model (Schmitt et al., 2014; Sansalone et al., 2009; Ying and Sansalone, 2011; Carbone et al., 2014; Pathapati and Sansalone, 2011). 2. Objectives In order to physically and computationally model the PM separation of HS units, subject to controlled testing, four objectives were undertaken. The first objective was to collect physical test data for two classes of commercial HS units of differing configuration and dimensions. As part of the first objective the commonly used ideal SOR model was applied, as compared to physical and CFD models, to evaluate PM separation for each unit with models of plain cylindrical tank units used as controls. These steady results were extended to loadings based on a triangular SOR loadgraph. Granulometric parameters in urban drainage loadings, in particular the influent PSD, have a significant influence on PM separation for any UO (NJDEP, 2013; CA-ETV, 2013). Therefore, for the second objective, differences in PM separation were modeled as a function of PSD deviations (coarser or finer d50 with similar heterodispersivity) from a standard hetero-disperse PSD used in this study as the PSD testing metric. For the third objective the influence of size dispersivity for the PSD (PSD shape) on PM separation was tested using a mono-, medium- and hetero-disperse PSD with a
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fixed d50 value. The fourth objective compared measured PM separation with a CFD model based on selected discrete particle diameters to determine at which SOR each particle diameter was completely separated by the HS unit. 3. Methodology The physical modeling methods are described herein. The computational and analytical modeling methods are described and detailed in the Supplemental Information (SI). 3.1. Description of physical modeling system The physical model testing configuration for HS units is shown in Fig. 1(A) as a plan view schematic of the water supply, storage, and influent pumping system. The main components are two water tanks (volume: 45,000 L each) supplied by a pressured 50-mm municipal potable water supply line and two Berkeley centrifugal pumps (Model: B3TPMS, 3 HP and Model: B6ZPLS, 10 HP) with a variable frequency drive (VFD). The potable water used during each physical HS test was collected and analyzed to determine eluted PM (as SSC) using ASTM D3977-97 (ASTM, 1997). The background samples SSC did not exceed 20 mg/L (mean: 3.4, standard deviation: 3.2, number of samples, n: 42). The turbidity of background samples did not exceed 5 NTU (mean: 0.6, standard deviation: 0.3,
n: 42). Flow measurements were provided by two Mx UltraMag (Model No. UM06-08-QS) meters with capacity up to 90 L/s (resolution < 0.1 pulse/day). Isometric drawings of the physically modeled HS and CFD modeled are in Fig. 1(B) and (C-E). For each physical HS test at least seven surface overflow rate (SOR) levels were tested: 40, 80, 200, 400, 600, 1000, 1400 L per minute (L/min) per square meter (m2) of effective treatment area to quantify PM separation (CA-ETV, 2013). The effective area is the UO area over which sedimentation occurs. The pumped flow rates associated with each SOR were determined based the effective treatment area of the HS system tested. A mass balance on PM was conducted for each separation test and included independent measurements of influent, effluent, separated PM and required a 90% recovery of PM mass. The temperature of the influent flow to the HS units for all of the physical test did not exceed 26 C (mean: 73 F, standard deviation: 1.0, n: 42). 3.2. Physical modeling methodology The physical monitoring campaign was configured to provide representative comparative data collection of PM separation for BHS units. Testing on each unit at each SOR was initiated with a quiescent clean volume of water (one turnover volume) in the HS unit. At each SOR the flow duration and PM loading was a minimum of 25 min, or the time required for eight turnover volumes,
Fig. 1. The schematic of the physical testing configuration at the Stormwater Unit Operations Laboratory (SUOL) is illustrated in plan view (not to scale). An illustration of one of the hydrodynamic separator (HS) units tested is shown. The arrows indicate the flow direction of the potable water supply to each tank and the subsequent pumped influent flows to and through the HS unit. Samples were taken at the influent drop box and from the free discharge of the effluent. PM was injected at the upstream end of the drop box. The hydrodynamic unit including both baffled hydrodynamic separator (BHS) and screened HS (SHS). For comparison, an open tank (plot D) and a tank with a top deck configuration (plot E) were modeled with a CFD model validated from the physical model results. The basic geometries are shown in plot (B) to (E).
H. Liu, J. Sansalone / Water Research 154 (2019) 258e266
whichever was greater (CA-ETV, 2013). A minimum of 12-kg of PM (dry mass basis) was fed into a HS during each test. PM feed was injected into the potable water influent just upstream of the drop box (three to five pipe diameters upstream of the HS). Six duplicates of portable water and six duplicate influent samples were taken at each SOR. For each SOR at least 12 pair of duplicate effluent samples were taken at evenly spaced time increments throughout a run. If the run duration exceeded 8 h, the run was terminated until the next day when sampling resumed after three volume exchanges of influent (at the nominal 200 mg/L of PM) were pumped through the HS before effluent sampling continued for the run duration. Samples were replicated and taken at regular intervals. SOR results were either examined separately or combined into a 1h triangular SOR loadgraph as shown in Fig. 2(A) for stepwise steady modeling with CFD. All physical model testing was conducted for a series of steady SOR at 200 mg/L (±25 mg/L) of siliceous, hetero-disperse PSD with a specific gravity (Sg) of 2.65. The d50 of the PSD metric shown in Fig. 2(B) was 75 mm with gamma parameters of k ¼ 0.56, and l ¼ 232.64. The sample bottles were 1-L wide-mouth of PP (polypropylene). Gravimetric PM analysis, as SSC, used vacuum filtration with 90 mm glass fiber filters (nominal 1 mm) and the entire sample volume instead of a sub-aliquot of the sample; thus providing more representative results and potential for a mass balance on PM. Because of the gravimetric PM analysis (as SSC) that required an entire 1-L sample, separate paired 1-L samples for PSD were taken
261
with each SSC sample. The analysis to determine PSD consisted of two separate steps. For the influent and effluent samples, PSD analysis was conducted with a Malvern Mastersizer 2000 (Malvern Instruments, 2007) laser diffraction particle size analyzer. In addition to aqueous influent and effluent PM samples, PM separated by the HS was recovered from each SOR run for each HS unit, dried and the PSD determined for separated PM in accordance with ASTM D422 (ASTM, 1993). PM separation for each SOR was determined as summarized in Eq. (1). In this equation, mean Cin is the mean inlet concentration Eq. (2) and mean Cout is the mean effluent concentration Eq. (3). PM separation is determined from Eq. (4). In Eq. (4), Vin,i and Cin,i represents the volume and eluted PM (as SSC) between two influent sample points, and Vout,i and Cout,i represents that of effluent. In Eqs. (2) and (3), MT and MO is the total PM mass injected into the HS and eluted from HS, and VT is the total flow volume through the HS during addition of the PM loading.
ðCin Cout Þ
Removal efficiency ð%Þ ¼
Cin ¼
Cout ¼
Cin
100
(1)
MT VT
(2)
MO VT
(3) Pn
PM separation ð%Þ ¼
Pn i¼1 vin;i cin;i i¼1 vout;i cout;i Pn i¼1 vin;i cin;i
100 (4)
The relative percent difference (RPD) between results or between measured and modeled is summarized in Eq. (5). The mass balance error for an entire PM separation test at a given SOR is summarized in Eq. (6). In Eq. (6), Mi is the influent mass of particles, MHS is the mass of particles captured by the HS unit and particles settled during the quiescence time, and Me is the effluent mass of particles, computed from the measured effluent SSC across the total treatment volume. A mass balance error of less than 10% was required for each physical model run (Dickenson and Sansalone, 2009). Head loss (as a head difference) across the HS unit was based on measurement of the influent water surface at the influent weir side as compared to the downstream effluent side of the weir under no weir overflow bypass; data are reported in Supplemental Information (SI).
RPD ð%Þ ¼
j measured modeled j 100 measured
Mass balance error ð%Þ ¼
(5)
Mi ðMHS þMe Þ 100 10% Mi (6)
3.3. Numerical and analytical modeling
Fig. 2. Plot (A) is the SOR loadgraph as a hydraulic loading metric for results from the HS systems shown in Fig. 3(A). PM separation results for this loadgraph are shown in Table 5. The tolerance range of injection PSD variability without generating a statistically significant difference in PM separation is 2%. Fig. 2 Plot (B) is the variability (2, 4, 6% coarser and finer) of particle size distributions (PSDs) as compared to the PSD metric in order to test PM separation results.
The CFD methodology, ideal SOR model and Hazen's non-ideal SOR model background, synthesis of a 1-h triangular SOR loadgraph from stepwise steady CFD results that provides additional comparison of HS units, as well as the respective computational or analytical methods are described in the Supplemental Information (SI). The modeling used physical data from the BHS testing described herein and published data for the other HS units (Cho and Sansalone, 2013a; Dickenson and Sansalone, 2012; Sansalone
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and Pathapati, 2009; Kim and Sansalone, 2008). Mesh quality was tested using Grid Convergence Method (GCI) method for two BHS units, for an index of the relative discretization error of the computed solution. The results indicated all the values of GCIfine are below 1%, which represent robust mesh quality (Celik et al., 2008).
4. Results and discussion 4.1. Comparison of PM separation results for hydrodynamic separator (HS) units Table 1 summarizes the geometrics and residence time results for the HS units evaluated in this study. For the theoretical median residence time (RT50 in seconds) based on clean bed BHS conditions (no PM storage) the correlation between the BHS tank diameter (D in meters) and the RT50 for the BHS units is represented as RT50 ¼ 67.2D 32.8 (R2 ¼ 0.98). For the BHS and SHS units, units of differing sump and internal geometries, the RT50 also increased as a function of increasing HS diameter. The Morrill Dispersion Index (MDI), the ratio of t90/t10, an index for mixing in a unit operations (Morrill et al., 1932) is tabulated for the HS units in Table 1. MDI, an index of short-circuiting flow and stagnant fluid (dead zones) for ascertaining UO non-ideal flow behavior (Mansouri et al., 2012). MDI values greater than 1 indicates greater mixing. For HS units the MDI has been correlated to the residence time distribution (RTD) (Cho and Sansalone, 2013b). Fig. 3(A) synthesizes results from nine HS units that range in diameter from 1.2 m (4 ft), 1.8 m (6 ft), and 2.4 m (8 ft) BHS units and 0.9 m (3 ft), 1.5 m (5 ft), 1.8 m (6 ft) and 2.1 m (7 ft) SHS units, and also includes a BHS 1700 and BHS 3410 (where these unit names represent the unit turnover volume in L). The PM separation of BHS and SHS units are also compared to plain tanks; an open tank and a tank with a horizontal top (the PM separation by these tanks are CFD modeled results). The basic geometries are illustrated in Fig. 1(B) through (E). The shaded area in Fig. 3(A) approximates the envelope of the database trends for all HS units. Upper and lower bounds of this shaded envelope are logarithmic functions represented in the following equations.
Fig. 3. Plot (A) summarizes the physical model results of PM separation as a percent of influent PM for HS configurations of a baffled hydrodynamic separator (BHS) and screened HS (SHS). All physical model testing was conducted at a series of SOR, and a PM loading of 200 mg/L for a siliceous hetero-disperse PSD metric of specific gravity (Sg) of 2.65. Granulometric indices of the loading PSD are given at the bottom of plot (A). The shaded area in plot (A) represents the area fit of the measured data. Indices for each HS, such as diameter (D), height (H), and hydraulic parameters are given in Table 1. Fig. 3(B) is the average volume weighted velocity magnitude (m/s) from CFD simulations for the HS configurations.
PM separation (%) ¼ 10.4{ln(SOR)} þ 116.9 with (R2 ¼ 0.99), upper bound (7)
PM separation (%) ¼ 11.1{ln(SOR)} þ 109.0 with (R2 ¼ 0.99), lower bound (9)
PM separation (%) ¼ 10.8{ln(SOR)}þ 112.9 with (R2 ¼ 0.99), median of dataset (8)
In these equations, SOR has units of L/min.-m2. The result in
Table 1 Dimensions, geometrics and hydraulic characteristics of each hydrodynamic separator (HS) or tank configuration. Configuration
a
Open tank Tank 1.2 m BHS 1.8 m BHS 2.4 m BHS 0.9 m SHS 1.5 m SHS 1.8 m SHS 2.1 m SHS BHS 1700 BHS 3410
1.52 1.52 1.52 1.52 2.29 0.97 1.80 1.80 1.80 1.52 1.73
H (m)
b
D (m) Sump area diameter (m) D/H ratio
1.22 1.22 1.22 1.83 2.44 0.89 1.52 1.83 2.13 1.22 1.62
1.22 1.22 1.22 1.83 2.44 0.51 0.91 0.91 0.91 1.22 1.62
0.80 0.80 0.80 1.20 1.07 0.92 0.84 1.02 1.18 0.80 0.94
c
Turnover volume (L)
1776 1776 1776 3996 10702 603 3265 4732 6411 1776 3564
PM capacity (mm) ft50 at MTFR (s)
d
e
39.93 39.93 27.19 39.93 108.89 9.50 37.27 37.27 37.27 9.10 18.00
102 102 102 102 102 229 229 229 229 260 102
MTFR [L/s]
5.7 68.2 46.7 90.1 127.3 33.6 90.6 129.2 186.0 197.0 167.1
g
Morrill Dispersion Index
1.4 17.5 4.3 5.1 3.4 11.6 13.3 12.6 11.1 8.3 5.5
Note: a H represents the height of each HS unit. b D represents the diameter of each HS unit. c Turnover volume: the volume of fluid for a hydrodynamic separator or tank. d MTFR: maximum manufactured treatment flow rate. e PM capacity: maximum depth of pre-deposited PM before clean out or scour test. f t50: theoretical median hydraulic residence time based on clean HS conditions with PM storage. (Pathapati and Sansalone, 2012) (Cho and Sansalone, 2013a, b). g Morrill Dispersion Index: the ratio of t90/t10.
H. Liu, J. Sansalone / Water Research 154 (2019) 258e266
Fig. 3(A) illustrate that the plain tank (with a horizontal top) and plain open tank (both used for comparative control as cylindrical tanks) have a statistically significantly (a ¼ 0.05) lower PM separation as compared to the median results (Eq. (8)) from the measured datasets for the BHS and SHS units. The ideal SOR model curve in Fig. 3(A) assumed Type I discrete particle settling and is the ideal limiting PM separation condition. Irrespective of the design and dimensions of the HS units, no HS or plain/open tank design exceeds the PM separation curve of the ideal SOR model. Hazen's model (Eq. S.15) with N ¼ 1 was also applied and plotted in Fig. 3(A), to approximate a lower boundary of the measured datasets for PM separation for a non-ideal and fully-mixed HS condition. To complement Fig. 3(A) and (B) presents the volumeweighted velocity magnitude (m/s) for the different HS unit results in Fig. 3(A) to provide an intuitive connection between velocity magnitude and PM separation.
4.2. Impact of PSD size scaling variability on PM separation results Testing protocols to examine PM separation specify a PSD metric and will sometimes specify the allowable variability around a selected PSD metric (NJDEP, 2013; CA-ETV, 2013). In order to explore the impact of PSD size variability on PM separation results, seven different PSDs (PSD metric and six variants) were tested for the 1.8 m BHS geometry. The physically-validated, CFD model of the BHS was used to examine the PM separation. The six PSD variants tested had similar shape factors (k) for size hetero-dispersivity to the PSD metric. The scaling factor (g), as an index of the central tendency for the PSD, was varied for each PSD variant with respect to the PSD metric. Fig. 2(B) illustrates the PSD variants and the hetero-disperse PSD metric. These PSD variants were tested with the CFD model at 2, 4, and 6% coarser and finer with respect to the PSD metric. The indices for the PSD variants are shown in Table 2. Table 2 also indicates that the d90 at 6% finer or coarser than the PSD metric were relatively higher or lower in magnitude (265e531 mm) as compared to the size variation of the d50 and d10 indices. Fig. 4 summarizes the PM separation results based on CFD modeling of the PSD variants summarized in Table 2 and Fig. 2(B) for the 1.8 m BHS. To compares PM separation results for the PSD variants, Fig. 4 plots the CFD model results of the variants with respect to the PSD metric results along with the measured results in a broad SOR range (40e1400 L/min/m2). The null hypothesis is that the PM separation result of a PSD variant as compared to the PSD metric was not statistically different. The PM separation from the validated CFD model for each different SOR were weighted equally based on the same PM injection mass. The chi-squared test indicates that only the ±2% variants with respect to the PM separation of the PSD metric yielded no statistically difference (p-value less than < a ¼ 0.05). The PM separation results for ±4 and ± 6% PSD variants produced results that were statistically significantly different as compared to the PSD metric. The measured data for
Table 2 Variation of granulometric scaling parameters of PSD variants and PSD metric. For each PSD the cumulative gamma distribution shape factor (k), scale factor (g) and gravimetric size indices are shown. PSD type
k
g
d90 (mm)
d50 (mm)
d10 (mm)
P-value (a ¼ 0.05)
6% finer 4% finer 2% finer PSD metric 2% coarser 4% coarser 6% coarser
0.51 0.53 0.55 0.56 0.57 0.57 0.57
196.55 205.73 217.71 232.64 251.07 275.51 304.61
265 286 312 354 396 447 531
50 55 60 66 71 77 84
2 2 2 3 4 6 8
0.697 0.165 0.001 e 0.001 0.165 0.697
263
Fig. 4. Baffled HS unit (1.8 m, BHS) PM separation as a function of influent PSD variations from the PSD metric.
1.8 m BHS are also plotted in Fig. 4. There is no statistically significant difference between the CFD modeled and measured data (pvalue less than a ¼ 0.05) based on testing with the PSD metric. Within the range of HS units tested, typical of source area heterodisperse PSD loadings of HS units, results indicate that a ±2% variance is the proper PSD variance range for controlled testing and comparison of PM separation by HS units. 4.3. Impact of PSD shape (hetero-dispersivity) variability on PM separation results The PM separation impact of PSD hetero-dispersivity (shape) with a constant scaling factor (indexed as a d50 of 75 mm) for two PSD variants with respect to the hetero-disperse PSD metric were also generated with a physically-validated CFD model of the 1.8 m BHS. The Supplemental Information summarizes the two PSD variants as compared to the hetero-disperse PSD metric (k ¼ 0.56, l ¼ 232.64). One PSD variant was mono-disperse (k ¼ 505.25, l ¼ 0.13) and the other PSD was medium-disperse (k ¼ 2.32, l ¼ 36.27) (Dickenson and Sansalone, 2009). Fig. 5 represents a comparison between the CFD model and the ideal SOR model results for PM separation as a function of SOR for the mono-, medium-, and hetero-disperse PSDs. From Fig. 5(A), the CFD model reproduced the measured PM separation results (RPD ¼ 4.3%) for the hetero-disperse PSD loading of the 1.8 m BHS across the SOR range from 0 to 1400 L/min/m2. The ideal SOR model increasingly over-predicted PM separation results as compared to the validated CFD model. This over-prediction by the SOR model, as compared to the CFD model, increased (8.1, 15.4 and 12.0%) as the PSD was altered from hetero-to medium-to mono-disperse. The increasing over-prediction as the PSD became more mono-disperse occurs in part because the ideal SOR model neglects hydrodynamics and internal geometry of the HS unit. Results indicate that the current hetero-disperse PSD used for regulatory testing (allowing a ±2% variance for finer or coarser based on results in Figs. 2(B) and 5 represents a robust metric for HS units. 4.4. Effluent PSD indices as a function of SOR Table 3 summarizes the trend in measured effluent PSD indices and PM for the 1.8 m BHS. The hetero-disperse influent PSD metric and PM concentration remained constant during the testing at each SOR. The CFD modeled effluent EMCs (event mean concentration) for PM reproduced the trend of measured EMCs within 10% at each SOR, noting that as SOR increased the eluted PM (as SSC) towards the influent PM of 200 mg/L. Size indices of d90, d50, d10 and gamma
264
H. Liu, J. Sansalone / Water Research 154 (2019) 258e266 Table 4 SOR results for 100% PM separation for the five different particle diameter illustrated in Fig. 6. The measured SOR result is the PM separation in a small diameter range (±2%) based on a discretization number (DN) of 101 from the laser diffraction data and 28 for the CFD modeling. SOR (L/min-m2)
25 mm
53 mm
75 mm
100 mm
150 mm
Measured SOR CFD modeled SOR
7 9
18 18
40 50
78 89
224 233
Fig. 6. Measured and CFD modeled PM separation for a baffled HS unit (1.8 m, BHS) at specific particle diameters based on effluent data generated by loadings with the hetero-disperse PSD metric. Ideal SOR predicts 100% separation at the following SORs (L/min-m2) for each particle diameter (mm): 8.9, 18.2 50.3, 89.6, 233.2 L/min/m2. The R2 for the 25, 53, 75, 100 and 150 mm are 98, 94, 93, 95, and 93%.
Fig. 5. PM separation for the baffled HS unit (1.8 m, BHS) PM separation as a function of SOR for the mono-, medium- and hetero-disperse PSD introduced in Fig. 4. The RPD of PM mass separation between CFD modeled and Ideal SOR modeled for (A) (B) and (C) are respectively 8.1, 15.4 and 12.0%. CFD modeled result was used as metric in denominator for RPD calculation between CFD modeled and Ideal SOR modeled result. The RPD of PM separation between measured and CFD modeled in (A) is 4.3%. The dotted vertical line represents the discrete settling velocity of the 75 mm particle.
distribution scaling parameter indicated that the eluted PSD became increasing coarser with increasing SOR (see Table 4). In addition to EMC results for PM, Fig. 6 compares the measured and CFD modeled PM separation results for specific particle diameters within the silt- (settleable) and fine sand-size (sediment) range of the PSD for five specific diameters (25, 53, 75, 100, and 150 mm). These results were developed across the tested SOR range.
The modeled results reasonably reproduce the measured results from the physical model testing, noting that in CFD a discrete particle diameter was modeled while the measured data was based on interpolating the discretized effluent PSD data for the specific particle diameter examined. While particle separation by sedimentation is clearly a function of particle diameter, results in Fig. 6 indicate that the PM separation behavior for the baffled HS unit reached a common inflection point at approximately 450 L/min/m2 for each particle diameter. For lower SOR values up to 450 L/min/m2 the PM separation decreased more rapidly, while for this unit, above 450 L/min/m2 the decrease was more gradual and less sensitive to increasing SOR as the eluted PSD became coarser and SSC higher. For all selected particle diameters the PM separation was decreased significantly as the SOR increased. The SOR at which 100% PM separation occurred for the PM diameters shown in Table 5 indicates that measured results were marginally lower than CFD model result within approximately 10 L/min/m2.
Table 3 PM granulometric indices for influent and effluent samples for 1.8 m baffled HS unit based on measured data; and measured and CFD modeled effluent PM concentration (as SSC). Surface overflow rate [L/min-m2]
k
g
d90 (mm)
d50 (mm)
d10 (mm)
Measured effluent [mg/L]
CFD modeled effluent [mg/L]
Influent PSD
0.56
232.6
250
75
5
200
e
7 9 11 13 18 24 23
1 1 1 2 2 2 2
66 79 91 96 107 126 141
54 65 82 102 112 121 149
Effluent granulometry and PM concentration as a function of SOR 40 80 200 400 600 1000 1400
1.0 0.9 0.9 0.8 0.7 0.6 0.6
10.3 13.8 18.5 27.9 52.2 73.9 82.9
22 31 39 65 115 134 146
H. Liu, J. Sansalone / Water Research 154 (2019) 258e266
265
Table 5 PM separation results for a triangular 1-h SOR loadgraph plotted in Fig. 2 based on PM separation results at each SOR level from Fig. 3A. (The influent PM concentration is constant at 200 mg/L with the hetero-disperse influent PSD metric with k ¼ 0.56, l ¼ 232.6). The products of column 2 and column 3 are constant at 42,000 L/m2. The mean PM separation result of BHS and SHS units is 39.8 and 37.1%; no statistically difference between BHS and SHS results for PM separation based on the 1-h SOR loadgraph. Configuration
Turnover volume (L)
Number of turnover volume per m2
t50 for given hydrograph (s)
CFD modeled result (%)
Ideal SOR result (%)
Effluent PM [mg/L]
Effluent gamma parameters k
l
Open tank Tank
1776 1776
23.6 23.6
6.1 101.5
33.5 30.9
46.8
133.0 138.2
0.58 0.57
111.7 113.5
29.3 32.9
1.2 m BHS 1.8 m BHS 2.4 m BHS 0.9 m SHS 1.5 m SHS 1.8 m SHS 2.1 m SHS BHS 1700 BHS 3410
1776 3996 10702 603 3265 4732 6411 1776 3564
23.6 10.5 3.9 69.7 12.9 8.9 6.6 23.6 11.8
79.6 103.7 115.2 51.0 78.0 96.2 94.1 36.3 68.0
38.2 39.6 41.2 36.0 36.2 37.3 39.0 33.9 46.0
123.6 120.8 117.6 128.0 127.6 125.4 122.0 132.2 108.0
0.73 0.70 0.73 0.63 0.65 0.65 0.72 0.78 0.86
49.7 67.5 53.9 90.2 85.9 83.1 66.7 66.2 69.9
22.2 24.4 24.1 30.4 30.4 30.0 28.5 31.8 35.9
4.5. PM separation with a 1-h triangular SOR loadgraph As summarized in Table 5 the PM separation results for the different HS designs of this study based on the CFD model of the 1-h triangular loadgraph shown in Fig. 2 are within a narrow range from 33.9 to 46.0% and these results are higher than the cylindrical tank designs of 30.9e33.5%. The CFD model results provide the PM separation result at 40, 80, 200, 400, 600, 1000, and 1400 L/min/m2. Trend line function as Eqs. (7)e(9) was generated for each HS unit, and calculated the PM separation result at 100, 300, 500, 700, 900, 1100, and 1300 L/min/m2. The result shown in Table 5 were calculated by multiplying the PM separation result at each SOR and the area percentage of corresponding rectangle. The HS designs provided a mean improvement of approximately 8% for PM separation as compared to a blank cylindrical tank. The ideal SOR result for PM separation during the 1-h SOR loadgraph was 46.8%, which was significantly greater than the PM separation results from the validated CFD model for all HS unit designs. The effluent EMCs for the nine HS design shown in Table 5 was 122.8 mg/L (SD ¼ 7.0, RPD for minimum and maximum value are 12.1 and 7.7%), and the d50 average was 28.6 mm (SD ¼ 4.3, RPD for minimum and maximum value are 22.5 and 25.4%). Table 5 indicated that HS units with larger surface area provided an increase in PM separation of 3% between 1.2 m BHS and 2.4 m BHS. Such improved PM separation comes at an increase in structural unit costs and construction costs. The constrained range of PM separation for all units subject to controlled testing suggests that uncontrolled and relatively poorly maintained in-situ field conditions yield PM separation between HS units that may not be physically or potentially statistically different.
Effluentd50 (mm)
For all HS units this constrained range in PM separation was relatively small when these results were compared to the higher ideal SOR model predictions. Crucial for testing/certification, a validated CFD model demonstrates that PM separation was statistically different at more than ±2% PSD variability with respect to the influent PSD metric. Results demonstrate that a validated CFD model reproduces PM separation results whether on a gravimetric basis for a given PSD or on a particle diameter basis of a PSD which allows portability of results for different PSDs, ranges of SORs or unsteady SOR loadgraphs. In contrast to the CFD model, the SOR model increasing overpredicted PM separation as the PSD shape dispersivity varied from hetero-to mono-disperse. For HS units, the SOR model provides a hypothetical upper bound of ideal PM separation with the SOR model error increasing as the PSD becomes more mono-disperse. For a wide range of HS units a physically-validated CFD model provides a valuable, albeit under-utilized tool to prototype, test, compare, certify unit operations for regulatory approval, and also yield portability of results; in particular compared to the costs of additional physical modeling. Conflicts of interest The authors have no financial and personal relationships with other people or organizations that could inappropriately influence (bias) their work. The authors have no conflicts of interest. Appendix A. Supplementary data
5. Conclusions HS designs have variable internal geometries, surface areas, and volumes. This study examines a range of full-scale physical, validated CFD models and a SOR model to evaluate HS functionality under steady loadings that are extended to an unsteady loadgraph. The conclusions are as follows: Within the range of geometric factors tested, for a given PM granulometry and SOR, PM separation for nine HS units declined with increasing SOR logarithmically in a constrained range (±10%); clearly below the ideal SOR and above the completely mixed tank (N ¼ 1) limit. The ideal SOR model results were statistically significantly higher than results of the validated CFD model for all HS units.
Supplementary data to this article can be found online at https://doi.org/10.1016/j.watres.2019.01.057. References American Society for testing and materials (ASTM), 1997. Standard test methods for determining sediment concentration in water samples. ASTM, West Conshohocken, Pennsylvania. ASTM Designation: D-3922-97. American Society for testing and materials (ASTM), 1993. Standard test methods for determining sediment concentration in water samples. ASTM, West Conshohocken, Pennsylvania. ASTM Designation: D-422-63. CA-ETV (The Canadian Environmental Technology Verification Program), 2013. Procedure for Laboratory testing of oil-grit separators. Carbone, M., Garofalo, G., Piro, P., 2014. Comparison between CFD and surface overflow rate models to predict particulate matter separation in unit operations for combined sewer overflows. J. Environ. Eng. 140 (12), 1e12, 04014041. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000857.
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Celik, I.B., Ghia, U., Roache, P.J., Freitas, C.J., Coleman, H., Raad, P.E., 2008. Procedure for estimation and reporting of uncertainty due to discretization in CFD applications. ASME J. Fluids Eng. 130 (7), 078001. Cho, H.C., Sansalone, J.J., 2013a. Instantaneous stepwise-steady CFD model of BMP response to unsteady PM loadings. J. Environ. Eng. 139 (11), 1350e1360. https:// doi.org/10.1061/(ASCE)EE.1943-7870.0000749. Cho, H.C., Sansalone, J.J., 2013b. Physical modeling of particulate matter washout from a hydrodynamic separator. J. Environ. Eng. 139 (1), 11e22. https://doi.org/ 10.1061/(ASCE)EE.1943-7870.0000556. Clean Water Act (CWA), 1972. Public Law 92-500-OCT, vol. 18, 1972. Dickenson, J.A., Sansalone, J.J., 2009. Discrete phase model representation of particulate matter (PM) for simulating PM separation by hydrodynamic unit operations. Environ. Sci. Technol. 43 (21), 8220e8226. Dickenson, J.A., Sansalone, J.J., 2012. Distribution and disinfection of bacterial loadings associated with particulate matter fractions transported in urban wet weather flows. Water Res. 46 (20), 6704e6714. https://doi.org/10.1016/j.watres. 2011.12.039. Geronimo, F.K.F., Maniquiz-Redillas, M.C., Tobio, J.A.S., Kim, L.H., 2014. Treatment of suspended solids and heavy metals from urban stormwater runoff by a tree box filter. Water Sci. Technol. 69 (12), 2460e2467. Herr, C., Sansalone, J.J., 2015. In situ volumetric filtration physical model to separate particulate matter from stormwater. J. Environ. Eng. 141 (9), 1e11, 04015017. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000946. Kim, J., Sansalone, J.J., 2008. Hydrodynamic separation of particulate matter transported by source area runoff. J. Environ. Eng. 134 (11), 912e922. https:// doi.org/10.1061/(ASCE)0733-9372(2008)134:11(912). Lee, D.H., Min, K.S., Kang, J.H., 2014. Performance evaluation and a sizing method for hydrodynamic separators treating urban stormwater runoff. Water Sci. Technol. 69 (10), 2122e2131. Lee, J.H., Bang, K.W., Choi, C.S., Lim, H.S., 2010. CFD modelling of flow field and particle tracking in a hydrodynamic stormwater separator. Water Sci. Technol. 62 (10), 2381e2388. Liu, B., Ying, G., Sansalone, J., 2010. Volumetric filtration of rainfall runoff. I: eventbased separation of particulate matter. J. Environ. Eng. 136 (12), 1321e1330. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000285. Ma, Y., Zhu, D.Z., 2014. Improving sediment removal in standard stormwater sumps. Water Sci. Technol. 69 (10), 2099e2104. Malvern Instruments, 2007. Sciocco 2000 user manual. Mansouri, Y., Zinatizadeh, A.A., Mohammadi, P., Irandoust, M., Akhbari, A., Davoodi, R., 2012. Hydraulic characteristics analysis of an anaerobic rotatory biological contactor (AnRBC) using tracer experiments and response surface methodology (RSM). J. Chem. Eng. 29 (7), 891e902. Morrill, A.B., Dean, J.B., Orton, J.W., Ellms, J.W., 1932. Sedimentation basin research and design, vol. 24. American Water Work Association, pp. 1442e1463. No. 9. Nichols, P., Lucke, T., Drapper, D., 2015. Field and evaluation method used to test the
performance of a Stormceptor® class 1 stormwater treatment device in Australia. Sustainability 2015 (7), 16311e16323. NJDEP, 2013. New Jersey department of environmental protection laboratory protocol to assess total suspended solids removal by a hydrodynamic sedimentation manufactured treatment device. Pathapati, S., Sansalone, J.J., 2011. Can a stepwise steady flow computational fluid dynamics model reproduce unsteady particulate matter separation for common unit operations? Environ. Sci. Technol. 45 (13), 5605e5613. Pathapati, S., Sansalone, J.J., 2012. Modeling particulate matter resuspension and washout form urban drainage hydrodynamic separators. J. Environ. Eng. 138 (1), 90e100. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000427. Sansalone, J.J., Kim, J., 2008. Transport of particulate matter fractions urban source area pavement surface runoff. J. Environ. Qual. 37 (5), 1883e1893. Sansalone, J.J., Koran, J.M., Smithson, J.A., Buchberger, S.G., 1998. Physical characteristics of urban roadway solids transported during rain events. J. Environ. Eng. 124 (5), 427e440. https://doi.org/10.1061/(ASCE)0733-9372(1998)124:5(427). Sansalone, J.J., Lin, H., Ying, G., 2009. Experimental and field studies of type I settling for particulate matter transported by urban runoff. J. Environ. Eng. 135 (10), 953e963. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000078. Sansalone, J.J., Pathapati, S., 2009. Particle dynamics of a hydrodynamic separator subject to transient rainfall-runoff. Water Resour. Res. 45 (9), 1e14. W09408. https://doi.org/10.1029/2008WR007661. Sansalone, J., Ying, G., 2008. Partitioning and granulometric distribution of metal leachate from urban traffic dry deposition particulate matter subject to acidic rainfall and runoff retention. Water Res. 42, 4146e4162. https://doi.org/10.1016/ j.watres.2008.06.013. Schmitt, V., Dufresne, M., Vazquez, J., Fischer, M., Morin, A., 2014. Separation efficiency of a hydrodynamic separator using a 3D computational fluid dynamics multiscale approach. Water Sci. Technol. 69 (5), 1069e1073. Spelman, D., Sansalone, J.J., 2018. Is the treatment response of manufactured BMPs to urban drainage PM loads portable? J. Environ. Eng. 144 (4), 1e10, 04018013. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001326. Thompson, K., Sattar, A.M.A., Gharabaghi, B., Warner, R.C., 2016. Event-based total suspended sediment particle size distribution model. J. Hydrol 536, 236e246. https://doi.org/10.1016/j.jhydrol.2016.02.056. Tran, D., Kang, J.H., 2013. Optimal design of a hydrodynamic separator for treating runoff from roadways. J. Environ. Manag. 116, 1e9. https://doi.org/10.1016/j. jenvman.2012.11.036. Ying, G., Sansalone, J., 2010. Transport and solubility of hetero-disperse dry deposition particulate matter subject to urban source area rainfall-runoff process. J. Hydrol 383, 156e166. https://doi.org/10.1016/j.jhydrol.2009.12.030. Ying, G., Sansalone, J., 2011. Gravitational settling velocity regimes for heterodisperse urban drainage particulate matter. J. Environ. Eng. 137 (1), 15e27. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000298.
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CFD and physical models of PM separation for urban drainage hydrodynamic unit operations Hua Liu, John Sansalone* Engineering School of Sustainable Infrastructure and Environment, U. of Florida, Gainesville, FL, 32611, United States
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 August 2018 Received in revised form 8 January 2019 Accepted 26 January 2019 Available online 14 February 2019
A hydrodynamic separator (HS) is a common pre-treatment unit operation (UO) for separating coarser particulate matter (PM). While HS designs have disparate internal geometries, surface areas, and volumes; for a given PM granulometry this study hypothesizes there are limits to PM separation. In this study, a large group of full-scale HS units were tested with physical and computational fluid dynamics (CFD) models to evaluate HS functionality based on surface overflow rate (SOR), HS geometrics (diameter, height, baffling), PM granulometry, particle size distribution (PSD), turnover volume, hydraulic capacity, and short-circuiting. Models were loaded with a hetero-disperse PSD, a common testing metric. PM separation results show that PSD variability greater than ±2% from this metric are different (a ¼ 0.05). Comparing PM separation using the PSD metric across the SOR range: (1) all HS results were below the ideal SOR model, (2) above Hazen's tanks-in-series model with N ¼ 1, (3) above plain tank designs, and (4) declined logarithmically in a ±10% range. Integrated across a triangular loadgraph, the SOR model significantly over-estimated PM separation for all HS units. The SOR model deviated from the validated CFD model, as PSD loadings became increasingly mono-disperse. A validated CFD model is shown to be a valuable tool to examine HS design and functionality. © 2019 Published by Elsevier Ltd.
Keywords: Best management practice Computational fluid dynamics Stormwater Particle size distribution
1. Introduction Urbanization after World War II in the USA with the commensurate increase in impervious urban/suburban areas and pavement as well as anthropogenic activity such as motor vehicle transport has significantly increased PM and chemical loads transported in the hydrologic cycle (Nichols et al., 2015; Sansalone et al., 1998). In the USA, the concept of Best Management Practices (BMPs) for urban drainage clarification was introduced, in large part, through the Clean Water Act (CWA, 1972). For the goal of clarification and load reduction, aside from hydrologic control, BMPs (as a unit operation, UO) are now used extensively as a structural UO or nonstructural practice. A hydrodynamic separator (HS) is one class of structural and proprietary UOs that has evolved to fit within the geometric confines of urban drainage networks. As such, HS units are compact, passive, yet do not provide any flow/volume mitigation. Proprietary HS units are contrasted with common structural non-proprietary systems such as detention/retention basins,
* Corresponding author. E-mail address: jsansal@ufl.edu (J. Sansalone). https://doi.org/10.1016/j.watres.2019.01.057 0043-1354/© 2019 Published by Elsevier Ltd.
wetlands, filter strips, infiltration, swales, and combinations thereof. (Tran and Kang, 2013). All of these UO systems provide varying levels of clarification for PM and any PM-associated chemicals (Lee et al., 2010). Over 50,000 HS units have been installed in North America (Lee et al., 2014) for the purpose of pretreatment. Thus, in recent decades, there has been increasing interest in examining the factors that impact the clarification functionality of HS units. For UOs designed for clarification, separation of PM (and debris) is the most common assessment metric. HS units are comparatively small structural UOs that are most effective for temporarily separating coarse PM and debris (Schmitt et al., 2014). All HS units function predominately as small-footprint sedimentation units despite internal geometric variations between HS units such as screens or baffles. The predominance of sedimentation, as compared to other physical mechanisms, has also been shown for UO that combine sedimentation and filtration (whether depth or surficial) in the same unit volume (Herr and Sansalone, 2015; Liu et al., 2010). PM is the primary vehicle for chemical and pathogen transport, and separation thereof is the most common index for urban drainage treatment; whether the metric is PM concentration
H. Liu, J. Sansalone / Water Research 154 (2019) 258e266
Nomenclature
Mi
ASTM BHS BMP Cin Cin,i
MO MT MDI MTFR n N
Cout Cout,i CA-ETV CFD CWA d10 d50 d90 D DN H HS k Me MHS
American Society for Testing and Materials Baffled hydrodynamic separator Best management practice mean inlet concentration, mg/L Eluted PM (measured as SSC) between influent sample points, mg/L Mean effluent concentration, mg/L Eluted PM (measured as SSC) between effluent sample points, mg/L Canadian Environmental Technology Verification Computational fluid dynamics Clean Water Act Diameter at which 10% of the PSD mass is smaller particles, mm Diameter at which 50% of the PSD mass is smaller particles, mm Diameter at which 90% of the PSD mass is smaller particles, mm Unit (HS) diameter, m Number of PM diameter increments (Discretization Number) Unit height, m Hydrodynamic separator Gamma parameters of PSD shape Effluent PM mass (measured across the total treatment volume), kg PM mass separated by HS unit for a treatment run volume, kg
{historically measured as suspended sediment concentration (SSC) or total suspended solids (TSS)}, or more recently as a particle size distribution (PSD) of PM. PM imparts oxygen demand, generates turbidity thereby impacting photosynthesis while providing significant surface area for partitioning of chemicals to and from the aqueous and PM phases (Geronimo et al., 2014; Ma and Zhu, 2014; Thompson et al., 2016; Sansalone and Ying, 2008; Ying and Sansalone, 2010). HS units are most commonly located in urban source area watersheds. These HS units are subject to highly unsteady and uncontrolled urban drainage transport (such as mass or flow-limited transport) of non-stationary PM granulometry and PM concentration which are coupled phenomena (Sansalone and Pathapati, 2009; Kim and Sansalone, 2008). For a UO such as a HS loaded by hetero-disperse PSDs generated from unsteady transport, previous research has demonstrated a positive relationship between PM concentration (as SSC) and PM separation because higher concentrations were generated from predominately coarser PM (Sansalone and Kim, 2008). Examination of PM separation from uncontrolled urban drainage treatment testing is more complex based on variable gravimetric PM concentrations and PSDs, albeit preferably facilitated on a per-particle size analysis based on discretizing the PSD (Spelman and Sansalone, 2018). However, variability of PM concentration and PSD as well as hydraulic unsteadiness requires a more complex evaluation of a UO, or comparison between UOs as is carried out in this study for a series of HS units. In contrast to the uncontrolled and highly variable loadings for in-situ HS units subject to urban drainage conditions, controlled testing of HS units is conducted with an invariable PSD. Flowrate and SOR notwithstanding, the PSD is a primary loading and granulometric parameter to test the response of a UO.
NJDEP NTU PM PSD RPD RT50 Sg SD SHS SOR SSC SUOL UO Vin,i VT VFD
l t50
259
Influent PM mass (measured across the total treatment volume), kg Total PM mass eluted from the HS, kg Total PM mass injected into the HS, kg Morrill dispersion index Maximum manufactured treatment flow rate Number of samples or analyses Hazen's index of non-ideal hydrodynamics of a unit operation New Jersey Department of Environmental Protection Nephelometric turbidity unit Particulate matter Particle size distribution Relative percent difference, % Theoretical median residence time, s Specific gravity Standard deviation Screened hydrodynamic separator Surface overflow rate, L/min.-m2 Suspended sediment concentration, mg/L Stormwater Unit Operations Laboratory Unit operation Volume between influent sample points, L Total flow volume through HS during injection of PM mass, L Variable frequency drive Gamma parameters of PSD diameter scale Theoretical median hydraulic residence time, s
The importance of the PSD applies whether examining separation or scour, as is the use of a PSD metric when comparing UOs subject to the same controlled loading conditions (a fixed PSD, PM concentration and SOR) in a testing protocol. Classical models such as ideal SOR and more recently recent models such as computational fluid dynamics (CFD) are tools that have been suggested to predict, validate or extrapolate the PM separation results of a physical HS model (Schmitt et al., 2014; Sansalone et al., 2009; Ying and Sansalone, 2011; Carbone et al., 2014; Pathapati and Sansalone, 2011). 2. Objectives In order to physically and computationally model the PM separation of HS units, subject to controlled testing, four objectives were undertaken. The first objective was to collect physical test data for two classes of commercial HS units of differing configuration and dimensions. As part of the first objective the commonly used ideal SOR model was applied, as compared to physical and CFD models, to evaluate PM separation for each unit with models of plain cylindrical tank units used as controls. These steady results were extended to loadings based on a triangular SOR loadgraph. Granulometric parameters in urban drainage loadings, in particular the influent PSD, have a significant influence on PM separation for any UO (NJDEP, 2013; CA-ETV, 2013). Therefore, for the second objective, differences in PM separation were modeled as a function of PSD deviations (coarser or finer d50 with similar heterodispersivity) from a standard hetero-disperse PSD used in this study as the PSD testing metric. For the third objective the influence of size dispersivity for the PSD (PSD shape) on PM separation was tested using a mono-, medium- and hetero-disperse PSD with a
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fixed d50 value. The fourth objective compared measured PM separation with a CFD model based on selected discrete particle diameters to determine at which SOR each particle diameter was completely separated by the HS unit. 3. Methodology The physical modeling methods are described herein. The computational and analytical modeling methods are described and detailed in the Supplemental Information (SI). 3.1. Description of physical modeling system The physical model testing configuration for HS units is shown in Fig. 1(A) as a plan view schematic of the water supply, storage, and influent pumping system. The main components are two water tanks (volume: 45,000 L each) supplied by a pressured 50-mm municipal potable water supply line and two Berkeley centrifugal pumps (Model: B3TPMS, 3 HP and Model: B6ZPLS, 10 HP) with a variable frequency drive (VFD). The potable water used during each physical HS test was collected and analyzed to determine eluted PM (as SSC) using ASTM D3977-97 (ASTM, 1997). The background samples SSC did not exceed 20 mg/L (mean: 3.4, standard deviation: 3.2, number of samples, n: 42). The turbidity of background samples did not exceed 5 NTU (mean: 0.6, standard deviation: 0.3,
n: 42). Flow measurements were provided by two Mx UltraMag (Model No. UM06-08-QS) meters with capacity up to 90 L/s (resolution < 0.1 pulse/day). Isometric drawings of the physically modeled HS and CFD modeled are in Fig. 1(B) and (C-E). For each physical HS test at least seven surface overflow rate (SOR) levels were tested: 40, 80, 200, 400, 600, 1000, 1400 L per minute (L/min) per square meter (m2) of effective treatment area to quantify PM separation (CA-ETV, 2013). The effective area is the UO area over which sedimentation occurs. The pumped flow rates associated with each SOR were determined based the effective treatment area of the HS system tested. A mass balance on PM was conducted for each separation test and included independent measurements of influent, effluent, separated PM and required a 90% recovery of PM mass. The temperature of the influent flow to the HS units for all of the physical test did not exceed 26 C (mean: 73 F, standard deviation: 1.0, n: 42). 3.2. Physical modeling methodology The physical monitoring campaign was configured to provide representative comparative data collection of PM separation for BHS units. Testing on each unit at each SOR was initiated with a quiescent clean volume of water (one turnover volume) in the HS unit. At each SOR the flow duration and PM loading was a minimum of 25 min, or the time required for eight turnover volumes,
Fig. 1. The schematic of the physical testing configuration at the Stormwater Unit Operations Laboratory (SUOL) is illustrated in plan view (not to scale). An illustration of one of the hydrodynamic separator (HS) units tested is shown. The arrows indicate the flow direction of the potable water supply to each tank and the subsequent pumped influent flows to and through the HS unit. Samples were taken at the influent drop box and from the free discharge of the effluent. PM was injected at the upstream end of the drop box. The hydrodynamic unit including both baffled hydrodynamic separator (BHS) and screened HS (SHS). For comparison, an open tank (plot D) and a tank with a top deck configuration (plot E) were modeled with a CFD model validated from the physical model results. The basic geometries are shown in plot (B) to (E).
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whichever was greater (CA-ETV, 2013). A minimum of 12-kg of PM (dry mass basis) was fed into a HS during each test. PM feed was injected into the potable water influent just upstream of the drop box (three to five pipe diameters upstream of the HS). Six duplicates of portable water and six duplicate influent samples were taken at each SOR. For each SOR at least 12 pair of duplicate effluent samples were taken at evenly spaced time increments throughout a run. If the run duration exceeded 8 h, the run was terminated until the next day when sampling resumed after three volume exchanges of influent (at the nominal 200 mg/L of PM) were pumped through the HS before effluent sampling continued for the run duration. Samples were replicated and taken at regular intervals. SOR results were either examined separately or combined into a 1h triangular SOR loadgraph as shown in Fig. 2(A) for stepwise steady modeling with CFD. All physical model testing was conducted for a series of steady SOR at 200 mg/L (±25 mg/L) of siliceous, hetero-disperse PSD with a specific gravity (Sg) of 2.65. The d50 of the PSD metric shown in Fig. 2(B) was 75 mm with gamma parameters of k ¼ 0.56, and l ¼ 232.64. The sample bottles were 1-L wide-mouth of PP (polypropylene). Gravimetric PM analysis, as SSC, used vacuum filtration with 90 mm glass fiber filters (nominal 1 mm) and the entire sample volume instead of a sub-aliquot of the sample; thus providing more representative results and potential for a mass balance on PM. Because of the gravimetric PM analysis (as SSC) that required an entire 1-L sample, separate paired 1-L samples for PSD were taken
261
with each SSC sample. The analysis to determine PSD consisted of two separate steps. For the influent and effluent samples, PSD analysis was conducted with a Malvern Mastersizer 2000 (Malvern Instruments, 2007) laser diffraction particle size analyzer. In addition to aqueous influent and effluent PM samples, PM separated by the HS was recovered from each SOR run for each HS unit, dried and the PSD determined for separated PM in accordance with ASTM D422 (ASTM, 1993). PM separation for each SOR was determined as summarized in Eq. (1). In this equation, mean Cin is the mean inlet concentration Eq. (2) and mean Cout is the mean effluent concentration Eq. (3). PM separation is determined from Eq. (4). In Eq. (4), Vin,i and Cin,i represents the volume and eluted PM (as SSC) between two influent sample points, and Vout,i and Cout,i represents that of effluent. In Eqs. (2) and (3), MT and MO is the total PM mass injected into the HS and eluted from HS, and VT is the total flow volume through the HS during addition of the PM loading.
ðCin Cout Þ
Removal efficiency ð%Þ ¼
Cin ¼
Cout ¼
Cin
100
(1)
MT VT
(2)
MO VT
(3) Pn
PM separation ð%Þ ¼
Pn i¼1 vin;i cin;i i¼1 vout;i cout;i Pn i¼1 vin;i cin;i
100 (4)
The relative percent difference (RPD) between results or between measured and modeled is summarized in Eq. (5). The mass balance error for an entire PM separation test at a given SOR is summarized in Eq. (6). In Eq. (6), Mi is the influent mass of particles, MHS is the mass of particles captured by the HS unit and particles settled during the quiescence time, and Me is the effluent mass of particles, computed from the measured effluent SSC across the total treatment volume. A mass balance error of less than 10% was required for each physical model run (Dickenson and Sansalone, 2009). Head loss (as a head difference) across the HS unit was based on measurement of the influent water surface at the influent weir side as compared to the downstream effluent side of the weir under no weir overflow bypass; data are reported in Supplemental Information (SI).
RPD ð%Þ ¼
j measured modeled j 100 measured
Mass balance error ð%Þ ¼
(5)
Mi ðMHS þMe Þ 100 10% Mi (6)
3.3. Numerical and analytical modeling
Fig. 2. Plot (A) is the SOR loadgraph as a hydraulic loading metric for results from the HS systems shown in Fig. 3(A). PM separation results for this loadgraph are shown in Table 5. The tolerance range of injection PSD variability without generating a statistically significant difference in PM separation is 2%. Fig. 2 Plot (B) is the variability (2, 4, 6% coarser and finer) of particle size distributions (PSDs) as compared to the PSD metric in order to test PM separation results.
The CFD methodology, ideal SOR model and Hazen's non-ideal SOR model background, synthesis of a 1-h triangular SOR loadgraph from stepwise steady CFD results that provides additional comparison of HS units, as well as the respective computational or analytical methods are described in the Supplemental Information (SI). The modeling used physical data from the BHS testing described herein and published data for the other HS units (Cho and Sansalone, 2013a; Dickenson and Sansalone, 2012; Sansalone
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and Pathapati, 2009; Kim and Sansalone, 2008). Mesh quality was tested using Grid Convergence Method (GCI) method for two BHS units, for an index of the relative discretization error of the computed solution. The results indicated all the values of GCIfine are below 1%, which represent robust mesh quality (Celik et al., 2008).
4. Results and discussion 4.1. Comparison of PM separation results for hydrodynamic separator (HS) units Table 1 summarizes the geometrics and residence time results for the HS units evaluated in this study. For the theoretical median residence time (RT50 in seconds) based on clean bed BHS conditions (no PM storage) the correlation between the BHS tank diameter (D in meters) and the RT50 for the BHS units is represented as RT50 ¼ 67.2D 32.8 (R2 ¼ 0.98). For the BHS and SHS units, units of differing sump and internal geometries, the RT50 also increased as a function of increasing HS diameter. The Morrill Dispersion Index (MDI), the ratio of t90/t10, an index for mixing in a unit operations (Morrill et al., 1932) is tabulated for the HS units in Table 1. MDI, an index of short-circuiting flow and stagnant fluid (dead zones) for ascertaining UO non-ideal flow behavior (Mansouri et al., 2012). MDI values greater than 1 indicates greater mixing. For HS units the MDI has been correlated to the residence time distribution (RTD) (Cho and Sansalone, 2013b). Fig. 3(A) synthesizes results from nine HS units that range in diameter from 1.2 m (4 ft), 1.8 m (6 ft), and 2.4 m (8 ft) BHS units and 0.9 m (3 ft), 1.5 m (5 ft), 1.8 m (6 ft) and 2.1 m (7 ft) SHS units, and also includes a BHS 1700 and BHS 3410 (where these unit names represent the unit turnover volume in L). The PM separation of BHS and SHS units are also compared to plain tanks; an open tank and a tank with a horizontal top (the PM separation by these tanks are CFD modeled results). The basic geometries are illustrated in Fig. 1(B) through (E). The shaded area in Fig. 3(A) approximates the envelope of the database trends for all HS units. Upper and lower bounds of this shaded envelope are logarithmic functions represented in the following equations.
Fig. 3. Plot (A) summarizes the physical model results of PM separation as a percent of influent PM for HS configurations of a baffled hydrodynamic separator (BHS) and screened HS (SHS). All physical model testing was conducted at a series of SOR, and a PM loading of 200 mg/L for a siliceous hetero-disperse PSD metric of specific gravity (Sg) of 2.65. Granulometric indices of the loading PSD are given at the bottom of plot (A). The shaded area in plot (A) represents the area fit of the measured data. Indices for each HS, such as diameter (D), height (H), and hydraulic parameters are given in Table 1. Fig. 3(B) is the average volume weighted velocity magnitude (m/s) from CFD simulations for the HS configurations.
PM separation (%) ¼ 10.4{ln(SOR)} þ 116.9 with (R2 ¼ 0.99), upper bound (7)
PM separation (%) ¼ 11.1{ln(SOR)} þ 109.0 with (R2 ¼ 0.99), lower bound (9)
PM separation (%) ¼ 10.8{ln(SOR)}þ 112.9 with (R2 ¼ 0.99), median of dataset (8)
In these equations, SOR has units of L/min.-m2. The result in
Table 1 Dimensions, geometrics and hydraulic characteristics of each hydrodynamic separator (HS) or tank configuration. Configuration
a
Open tank Tank 1.2 m BHS 1.8 m BHS 2.4 m BHS 0.9 m SHS 1.5 m SHS 1.8 m SHS 2.1 m SHS BHS 1700 BHS 3410
1.52 1.52 1.52 1.52 2.29 0.97 1.80 1.80 1.80 1.52 1.73
H (m)
b
D (m) Sump area diameter (m) D/H ratio
1.22 1.22 1.22 1.83 2.44 0.89 1.52 1.83 2.13 1.22 1.62
1.22 1.22 1.22 1.83 2.44 0.51 0.91 0.91 0.91 1.22 1.62
0.80 0.80 0.80 1.20 1.07 0.92 0.84 1.02 1.18 0.80 0.94
c
Turnover volume (L)
1776 1776 1776 3996 10702 603 3265 4732 6411 1776 3564
PM capacity (mm) ft50 at MTFR (s)
d
e
39.93 39.93 27.19 39.93 108.89 9.50 37.27 37.27 37.27 9.10 18.00
102 102 102 102 102 229 229 229 229 260 102
MTFR [L/s]
5.7 68.2 46.7 90.1 127.3 33.6 90.6 129.2 186.0 197.0 167.1
g
Morrill Dispersion Index
1.4 17.5 4.3 5.1 3.4 11.6 13.3 12.6 11.1 8.3 5.5
Note: a H represents the height of each HS unit. b D represents the diameter of each HS unit. c Turnover volume: the volume of fluid for a hydrodynamic separator or tank. d MTFR: maximum manufactured treatment flow rate. e PM capacity: maximum depth of pre-deposited PM before clean out or scour test. f t50: theoretical median hydraulic residence time based on clean HS conditions with PM storage. (Pathapati and Sansalone, 2012) (Cho and Sansalone, 2013a, b). g Morrill Dispersion Index: the ratio of t90/t10.
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Fig. 3(A) illustrate that the plain tank (with a horizontal top) and plain open tank (both used for comparative control as cylindrical tanks) have a statistically significantly (a ¼ 0.05) lower PM separation as compared to the median results (Eq. (8)) from the measured datasets for the BHS and SHS units. The ideal SOR model curve in Fig. 3(A) assumed Type I discrete particle settling and is the ideal limiting PM separation condition. Irrespective of the design and dimensions of the HS units, no HS or plain/open tank design exceeds the PM separation curve of the ideal SOR model. Hazen's model (Eq. S.15) with N ¼ 1 was also applied and plotted in Fig. 3(A), to approximate a lower boundary of the measured datasets for PM separation for a non-ideal and fully-mixed HS condition. To complement Fig. 3(A) and (B) presents the volumeweighted velocity magnitude (m/s) for the different HS unit results in Fig. 3(A) to provide an intuitive connection between velocity magnitude and PM separation.
4.2. Impact of PSD size scaling variability on PM separation results Testing protocols to examine PM separation specify a PSD metric and will sometimes specify the allowable variability around a selected PSD metric (NJDEP, 2013; CA-ETV, 2013). In order to explore the impact of PSD size variability on PM separation results, seven different PSDs (PSD metric and six variants) were tested for the 1.8 m BHS geometry. The physically-validated, CFD model of the BHS was used to examine the PM separation. The six PSD variants tested had similar shape factors (k) for size hetero-dispersivity to the PSD metric. The scaling factor (g), as an index of the central tendency for the PSD, was varied for each PSD variant with respect to the PSD metric. Fig. 2(B) illustrates the PSD variants and the hetero-disperse PSD metric. These PSD variants were tested with the CFD model at 2, 4, and 6% coarser and finer with respect to the PSD metric. The indices for the PSD variants are shown in Table 2. Table 2 also indicates that the d90 at 6% finer or coarser than the PSD metric were relatively higher or lower in magnitude (265e531 mm) as compared to the size variation of the d50 and d10 indices. Fig. 4 summarizes the PM separation results based on CFD modeling of the PSD variants summarized in Table 2 and Fig. 2(B) for the 1.8 m BHS. To compares PM separation results for the PSD variants, Fig. 4 plots the CFD model results of the variants with respect to the PSD metric results along with the measured results in a broad SOR range (40e1400 L/min/m2). The null hypothesis is that the PM separation result of a PSD variant as compared to the PSD metric was not statistically different. The PM separation from the validated CFD model for each different SOR were weighted equally based on the same PM injection mass. The chi-squared test indicates that only the ±2% variants with respect to the PM separation of the PSD metric yielded no statistically difference (p-value less than < a ¼ 0.05). The PM separation results for ±4 and ± 6% PSD variants produced results that were statistically significantly different as compared to the PSD metric. The measured data for
Table 2 Variation of granulometric scaling parameters of PSD variants and PSD metric. For each PSD the cumulative gamma distribution shape factor (k), scale factor (g) and gravimetric size indices are shown. PSD type
k
g
d90 (mm)
d50 (mm)
d10 (mm)
P-value (a ¼ 0.05)
6% finer 4% finer 2% finer PSD metric 2% coarser 4% coarser 6% coarser
0.51 0.53 0.55 0.56 0.57 0.57 0.57
196.55 205.73 217.71 232.64 251.07 275.51 304.61
265 286 312 354 396 447 531
50 55 60 66 71 77 84
2 2 2 3 4 6 8
0.697 0.165 0.001 e 0.001 0.165 0.697
263
Fig. 4. Baffled HS unit (1.8 m, BHS) PM separation as a function of influent PSD variations from the PSD metric.
1.8 m BHS are also plotted in Fig. 4. There is no statistically significant difference between the CFD modeled and measured data (pvalue less than a ¼ 0.05) based on testing with the PSD metric. Within the range of HS units tested, typical of source area heterodisperse PSD loadings of HS units, results indicate that a ±2% variance is the proper PSD variance range for controlled testing and comparison of PM separation by HS units. 4.3. Impact of PSD shape (hetero-dispersivity) variability on PM separation results The PM separation impact of PSD hetero-dispersivity (shape) with a constant scaling factor (indexed as a d50 of 75 mm) for two PSD variants with respect to the hetero-disperse PSD metric were also generated with a physically-validated CFD model of the 1.8 m BHS. The Supplemental Information summarizes the two PSD variants as compared to the hetero-disperse PSD metric (k ¼ 0.56, l ¼ 232.64). One PSD variant was mono-disperse (k ¼ 505.25, l ¼ 0.13) and the other PSD was medium-disperse (k ¼ 2.32, l ¼ 36.27) (Dickenson and Sansalone, 2009). Fig. 5 represents a comparison between the CFD model and the ideal SOR model results for PM separation as a function of SOR for the mono-, medium-, and hetero-disperse PSDs. From Fig. 5(A), the CFD model reproduced the measured PM separation results (RPD ¼ 4.3%) for the hetero-disperse PSD loading of the 1.8 m BHS across the SOR range from 0 to 1400 L/min/m2. The ideal SOR model increasingly over-predicted PM separation results as compared to the validated CFD model. This over-prediction by the SOR model, as compared to the CFD model, increased (8.1, 15.4 and 12.0%) as the PSD was altered from hetero-to medium-to mono-disperse. The increasing over-prediction as the PSD became more mono-disperse occurs in part because the ideal SOR model neglects hydrodynamics and internal geometry of the HS unit. Results indicate that the current hetero-disperse PSD used for regulatory testing (allowing a ±2% variance for finer or coarser based on results in Figs. 2(B) and 5 represents a robust metric for HS units. 4.4. Effluent PSD indices as a function of SOR Table 3 summarizes the trend in measured effluent PSD indices and PM for the 1.8 m BHS. The hetero-disperse influent PSD metric and PM concentration remained constant during the testing at each SOR. The CFD modeled effluent EMCs (event mean concentration) for PM reproduced the trend of measured EMCs within 10% at each SOR, noting that as SOR increased the eluted PM (as SSC) towards the influent PM of 200 mg/L. Size indices of d90, d50, d10 and gamma
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H. Liu, J. Sansalone / Water Research 154 (2019) 258e266 Table 4 SOR results for 100% PM separation for the five different particle diameter illustrated in Fig. 6. The measured SOR result is the PM separation in a small diameter range (±2%) based on a discretization number (DN) of 101 from the laser diffraction data and 28 for the CFD modeling. SOR (L/min-m2)
25 mm
53 mm
75 mm
100 mm
150 mm
Measured SOR CFD modeled SOR
7 9
18 18
40 50
78 89
224 233
Fig. 6. Measured and CFD modeled PM separation for a baffled HS unit (1.8 m, BHS) at specific particle diameters based on effluent data generated by loadings with the hetero-disperse PSD metric. Ideal SOR predicts 100% separation at the following SORs (L/min-m2) for each particle diameter (mm): 8.9, 18.2 50.3, 89.6, 233.2 L/min/m2. The R2 for the 25, 53, 75, 100 and 150 mm are 98, 94, 93, 95, and 93%.
Fig. 5. PM separation for the baffled HS unit (1.8 m, BHS) PM separation as a function of SOR for the mono-, medium- and hetero-disperse PSD introduced in Fig. 4. The RPD of PM mass separation between CFD modeled and Ideal SOR modeled for (A) (B) and (C) are respectively 8.1, 15.4 and 12.0%. CFD modeled result was used as metric in denominator for RPD calculation between CFD modeled and Ideal SOR modeled result. The RPD of PM separation between measured and CFD modeled in (A) is 4.3%. The dotted vertical line represents the discrete settling velocity of the 75 mm particle.
distribution scaling parameter indicated that the eluted PSD became increasing coarser with increasing SOR (see Table 4). In addition to EMC results for PM, Fig. 6 compares the measured and CFD modeled PM separation results for specific particle diameters within the silt- (settleable) and fine sand-size (sediment) range of the PSD for five specific diameters (25, 53, 75, 100, and 150 mm). These results were developed across the tested SOR range.
The modeled results reasonably reproduce the measured results from the physical model testing, noting that in CFD a discrete particle diameter was modeled while the measured data was based on interpolating the discretized effluent PSD data for the specific particle diameter examined. While particle separation by sedimentation is clearly a function of particle diameter, results in Fig. 6 indicate that the PM separation behavior for the baffled HS unit reached a common inflection point at approximately 450 L/min/m2 for each particle diameter. For lower SOR values up to 450 L/min/m2 the PM separation decreased more rapidly, while for this unit, above 450 L/min/m2 the decrease was more gradual and less sensitive to increasing SOR as the eluted PSD became coarser and SSC higher. For all selected particle diameters the PM separation was decreased significantly as the SOR increased. The SOR at which 100% PM separation occurred for the PM diameters shown in Table 5 indicates that measured results were marginally lower than CFD model result within approximately 10 L/min/m2.
Table 3 PM granulometric indices for influent and effluent samples for 1.8 m baffled HS unit based on measured data; and measured and CFD modeled effluent PM concentration (as SSC). Surface overflow rate [L/min-m2]
k
g
d90 (mm)
d50 (mm)
d10 (mm)
Measured effluent [mg/L]
CFD modeled effluent [mg/L]
Influent PSD
0.56
232.6
250
75
5
200
e
7 9 11 13 18 24 23
1 1 1 2 2 2 2
66 79 91 96 107 126 141
54 65 82 102 112 121 149
Effluent granulometry and PM concentration as a function of SOR 40 80 200 400 600 1000 1400
1.0 0.9 0.9 0.8 0.7 0.6 0.6
10.3 13.8 18.5 27.9 52.2 73.9 82.9
22 31 39 65 115 134 146
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265
Table 5 PM separation results for a triangular 1-h SOR loadgraph plotted in Fig. 2 based on PM separation results at each SOR level from Fig. 3A. (The influent PM concentration is constant at 200 mg/L with the hetero-disperse influent PSD metric with k ¼ 0.56, l ¼ 232.6). The products of column 2 and column 3 are constant at 42,000 L/m2. The mean PM separation result of BHS and SHS units is 39.8 and 37.1%; no statistically difference between BHS and SHS results for PM separation based on the 1-h SOR loadgraph. Configuration
Turnover volume (L)
Number of turnover volume per m2
t50 for given hydrograph (s)
CFD modeled result (%)
Ideal SOR result (%)
Effluent PM [mg/L]
Effluent gamma parameters k
l
Open tank Tank
1776 1776
23.6 23.6
6.1 101.5
33.5 30.9
46.8
133.0 138.2
0.58 0.57
111.7 113.5
29.3 32.9
1.2 m BHS 1.8 m BHS 2.4 m BHS 0.9 m SHS 1.5 m SHS 1.8 m SHS 2.1 m SHS BHS 1700 BHS 3410
1776 3996 10702 603 3265 4732 6411 1776 3564
23.6 10.5 3.9 69.7 12.9 8.9 6.6 23.6 11.8
79.6 103.7 115.2 51.0 78.0 96.2 94.1 36.3 68.0
38.2 39.6 41.2 36.0 36.2 37.3 39.0 33.9 46.0
123.6 120.8 117.6 128.0 127.6 125.4 122.0 132.2 108.0
0.73 0.70 0.73 0.63 0.65 0.65 0.72 0.78 0.86
49.7 67.5 53.9 90.2 85.9 83.1 66.7 66.2 69.9
22.2 24.4 24.1 30.4 30.4 30.0 28.5 31.8 35.9
4.5. PM separation with a 1-h triangular SOR loadgraph As summarized in Table 5 the PM separation results for the different HS designs of this study based on the CFD model of the 1-h triangular loadgraph shown in Fig. 2 are within a narrow range from 33.9 to 46.0% and these results are higher than the cylindrical tank designs of 30.9e33.5%. The CFD model results provide the PM separation result at 40, 80, 200, 400, 600, 1000, and 1400 L/min/m2. Trend line function as Eqs. (7)e(9) was generated for each HS unit, and calculated the PM separation result at 100, 300, 500, 700, 900, 1100, and 1300 L/min/m2. The result shown in Table 5 were calculated by multiplying the PM separation result at each SOR and the area percentage of corresponding rectangle. The HS designs provided a mean improvement of approximately 8% for PM separation as compared to a blank cylindrical tank. The ideal SOR result for PM separation during the 1-h SOR loadgraph was 46.8%, which was significantly greater than the PM separation results from the validated CFD model for all HS unit designs. The effluent EMCs for the nine HS design shown in Table 5 was 122.8 mg/L (SD ¼ 7.0, RPD for minimum and maximum value are 12.1 and 7.7%), and the d50 average was 28.6 mm (SD ¼ 4.3, RPD for minimum and maximum value are 22.5 and 25.4%). Table 5 indicated that HS units with larger surface area provided an increase in PM separation of 3% between 1.2 m BHS and 2.4 m BHS. Such improved PM separation comes at an increase in structural unit costs and construction costs. The constrained range of PM separation for all units subject to controlled testing suggests that uncontrolled and relatively poorly maintained in-situ field conditions yield PM separation between HS units that may not be physically or potentially statistically different.
Effluentd50 (mm)
For all HS units this constrained range in PM separation was relatively small when these results were compared to the higher ideal SOR model predictions. Crucial for testing/certification, a validated CFD model demonstrates that PM separation was statistically different at more than ±2% PSD variability with respect to the influent PSD metric. Results demonstrate that a validated CFD model reproduces PM separation results whether on a gravimetric basis for a given PSD or on a particle diameter basis of a PSD which allows portability of results for different PSDs, ranges of SORs or unsteady SOR loadgraphs. In contrast to the CFD model, the SOR model increasing overpredicted PM separation as the PSD shape dispersivity varied from hetero-to mono-disperse. For HS units, the SOR model provides a hypothetical upper bound of ideal PM separation with the SOR model error increasing as the PSD becomes more mono-disperse. For a wide range of HS units a physically-validated CFD model provides a valuable, albeit under-utilized tool to prototype, test, compare, certify unit operations for regulatory approval, and also yield portability of results; in particular compared to the costs of additional physical modeling. Conflicts of interest The authors have no financial and personal relationships with other people or organizations that could inappropriately influence (bias) their work. The authors have no conflicts of interest. Appendix A. Supplementary data
5. Conclusions HS designs have variable internal geometries, surface areas, and volumes. This study examines a range of full-scale physical, validated CFD models and a SOR model to evaluate HS functionality under steady loadings that are extended to an unsteady loadgraph. The conclusions are as follows: Within the range of geometric factors tested, for a given PM granulometry and SOR, PM separation for nine HS units declined with increasing SOR logarithmically in a constrained range (±10%); clearly below the ideal SOR and above the completely mixed tank (N ¼ 1) limit. The ideal SOR model results were statistically significantly higher than results of the validated CFD model for all HS units.
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