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Comparing four mixing zone models with brine discharge measurements from a reverse osmosis desalination plant in Spain
Desalination 286 (2012) 217–224
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Comparing four mixing zone models with brine discharge measurements from a reverse osmosis desalination plant in Spain Ángel Loya-Fernández ⁎, Luis Miguel Ferrero-Vicente, Candela Marco-Méndez, Elena Martínez-García, José Zubcoff, José Luis Sánchez-Lizaso Department of Marine Sciences and Applied Biology, University of Alicante, PO Box 99, E-03080 Alicante, Spain
a r t i c l e
i n f o
Article history: Received 28 July 2011 Received in revised form 7 November 2011 Accepted 8 November 2011 Available online 30 November 2011 Keywords: SWRO Brine discharge Ocean outfall Diffuser Negatively buoyant jet Near-field mixing zone models Conservative
a b s t r a c t Nuevo Canal de Cartagena (Spain) desalination plants discharge hypersaline effluent through a submarine outfall pipeline, creating a negatively buoyant brine jet. Many near-field mixing models are used in the prediction and management of brine discharges, but they have rarely been compared with field salinity measurements obtained directly inside the brine jet. Two divers obtained these field measurements and compared them with CORMIX1, CORJET, MEDVSA and UM3 mixing zone model predictions. In general, each model was quite conservative in its results, except UM3, whose prediction presented the best approximation to measured data. It is concluded that direct field measurements should be essential when testing the accuracy of current models or developing new near-field mixing models. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Desalination plants have been associated with some potential environmental effects; these are mainly attributed to the concentrate and chemicals discharges into the marine environment which may impair coastal water quality and affect marine life [17]. Furthermore, recent technology has reduced the cost of the desalination process [1], enhancing desalination capacity; therefore there is an increased need to accurately assess the potential environmental effects of such plants. These effects have been documented previously [11], including our sampling area near the discharge point of the Nuevo Canal de Cartagena desalination plants [20,21]. The nature of the brine differs significantly depending on the process employed in seawater desalination. Thermal desalination processes generate a hypersaline and high-temperature effluent with a neutral or positively buoyant flux, causing the plume to rise and spread on the sea-surface [4]. On the other hand, the seawater reverse osmosis (SWRO) process, which is used at the Nuevo Canal de Cartagena (Spain) desalination plants, generates a negatively buoyant flux or dense jet, which spreads as a density current on the seabed.
⁎ Corresponding author. Tel.: + 34 96 590 3473; fax: + 34 96 590 9897. E-mail address: [email protected] (Á. Loya-Fernández). 0011-9164/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2011.11.026
In Spain, 70% of desalinated water is produced by SWRO technology [18]. Methods employed for the disposal of brine discharges vary widely, but the most common procedure in SWRO desalination plants located close to the sea is discharging via submarine outfall, which usually consists of a submerged pipeline and a diffuser section. Diffusers are devices used to speed up the dilution process with the nearby seawater in highly concentrated brine jets [22] to enhance jet exit velocity and therefore the turbulence and mixing process. The outfall at Nuevo Canal de Cartagena has only one diffuser at the end of the pipeline which creates an inclined dense jet. This kind of jet was recommended for the discharge of brine from desalination plants to achieve maximum mixing process efficiency [25]. The mixing process of effluents discharged from a submerged outfall consist of near field and far field mixing processes, which occur at different spatial and time scales [26]. Thus, two mixing regions were defined when working with discharges of brine into the receiving water body: the near-field and far-field region. The near-field region is located in the vicinity of the discharge point and is affected by turbulent jet mixing, which depends critically on discharge parameters, brine physical properties and environmental physical properties. This mixing area extends from the effluent's point of release to its interaction with a physical boundary (the seafloor, in the present case). The end of the near-field is considered to be the point at which the turbulence collapses [3]. At this point, the far-field region begins and the brine jet is now named brine plume. The far-field plume
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forms a gravity driven current moving along the seafloor and mixing is only affected by the physical processes of advection and diffusion [22]. Logically, the more diluted the jet is in the near-field region complimented by a correct discharge configuration, the more diluted it will be in far field region, and thus reducing the affected area. The behaviour of brine jet trajectory and its dilution process in the near field have been commonly studied through laboratory experiments, mixing zone models or field observation. Many studies with controlled laboratory experiments were found in the literature, but drawbacks have been identified in this research [23]. In recent years, computer technology has developed mixing zone models as a predictive tool which provided simulations of brine discharges behaviour in the seawater receiving body [10]. This technology results in savings of time and expenses compared with laboratory experiments, but it is a complex and difficult task [2]. CORMIX and VISUAL PLUMES are some of the most notable commercial software for brine discharge modelling developed and supported by EPA. These models predict brine behaviour, including trajectory, dimensions and degrees of dilution, by considering the effluent properties, the disposal configuration and the ambient conditions [7,13]. These two programmes were used for this study as well as a new online modelling tool focused on negatively buoyant effluents called MEDVSA [19] in order to compare their simulations with field salinity data. Many comparison and verification studies have been carried out between field measurements and different mixing models. In studies with positively buoyant discharges, numerical modelling has been well known for some time and it has been utilised in many verification works, but it was found that some models had many limitations when they were compared with field measurements [24]. On the other hand, most of the validation studies with negatively buoyant discharges were made in laboratories with experimental effluents. Some of the validation works indicated that existing models significantly underestimated the dilution and plume location of these experimental effluents [17]. Very few validation studies compared mixing model simulations with field measurements, because in situ field measurements of operating SWRO desalination plants outfalls are quite difficult and rarely well known (particularly with submerged plumes) because of corresponding environmental conditions such as currents and density stratification [23]. This paper focuses on two objectives. The first was to record in situ salinity measurements inside the brine jet using divers. The second was to compare these measurements with simulations made by four near field mixing models (CORMIX1, CORJET, MEDVSA and UM3) with the intention of determining their prediction accuracy.
2. Material and methods The Nuevo Canal de Cartagena desalination plants are placed 680 meters from the Mediterranean coast in San Pedro del Pinatar, Murcia, SE Spain. These two plants discharge 62 Hm3 year− 1 of brine, sharing a submarine outfall pipeline. The onshore section of the pipeline is 680 m from the desalination plant to the coast, where it fits with the submarine section, which is 5100 m long. Fig. 1 shows the brine jet scheme at the end of the pipeline, 34.9 m depth, where a 60º inclined single diffuser elevated 4.5 m from seabed can be found. The diffuser location is presented in Fig. 2 (UTM coordinates x = 701683 and y = 4189682). Its orientation is 90º from North towards east. 2.1. In-situ measurements A 600 KHz ADCP Workhorse Sentinel Doppler was previously deployed around 100 m away from the end of the outfall pipe at 34.1 meters depth (UTM coordinates x = 701706 and y = 4189571. Fig. 2) for a period of one month, obtaining current average direction and current average velocity at vertical intervals of 2 m, from the bottom to the surface. Working with the current data, previous discharge configuration data (discharge excess salinity, discharge velocity, discharge density and discharge depth) and previous measured environmental data (wind speed, current speed, environmental density, port diameter, port height, vertical angle and horizontal angle) in CORMIX1, the simulated area of near-field region was obtained. This information was used in order to calculate the field sampling area (Table 1). After the near-field area simulated by CORMIX1 was known, it was decided that the best option was to take actual salinity data by scuba diving directly inside the hypersaline jet at 34.9 m depth. In October 2010, A CTD (RBR-XR 620 CTD+; resolution ±0.01 psu and ±0.005 °C) was used to take temperature, conductivity and depth measurements in situ, logging one sample each second. This CTD is suitable for use outside the usual limits of the PSS-78 practical salinity scale, in applications such as desalination brine monitoring. Once the outfall was located, the first diver placed the beginning of a tape measure at the beginning of the jet, (“A” in Fig. 1). The second diver followed the x-axis diffuser direction, keeping the same depth (z = 0) and reaching the different sampling distances. Once diver two had reached a specific distance, he came up into the brine jet (“B” in Fig. 1) following a perpendicular to x-axis trajectory (helped by a buoy) with the CTD sensor to register temperature, conductivity and depth until he reached 15 m away, where it is supposed (by CORMIX1) the near field would end.
Fig. 1. The position of the divers during the measurements of an inclined dense jet. A: First diver position. B: Second diver position. θ: Jet Inclination angle with regard to the bottom. C: Jet centreline. Ho: Diffuser height from the bottom. Xi: Distance to bottom hit (at the centreline). Zmax: Maximum height reached by the jet. Ua: Ambient current.
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Fig. 2. Area map showing site near desalination outfall region. BD: Brine discharge point; DP: Doppler position.
Samples were taken at one meter intervals from the diffuser to 7 meters distance, and at two meters intervals from 9 to15 m. Position of divers as well as brine jet parameters (jet inclination angle, jet centreline, diffuser height, distance to bottom hit, maximum height reached, ambient current, depth and coordinate system) are showed in Fig. 1. The same CTD sensor was also used to describe water column physical characteristics from the surface to the sea bottom. Another CTD inside desalination plant (previous to outfall inside desalination plant) registered salinity and temperature measurements each 10 minutes from 2005 to present; it is possible to know salinity and temperature of the brine discharge in the moment salinity was taken in the field. Flow details were obtained from SWRO desalination plant electromagnetic flow meter (Endress + Hauser Promag50).
form reasonable hypothesis about complex physical situations that can be tested experimentally. 2. Jet integral models are models based on the integration of differential equations along the cross section of flow which are applied to brine discharges and solve the hydrodynamics and transport equations (adapted to a negatively buoyant effluent). These equations can be set up by a Lagrangian or Eulerian system. In the Lagrangian system, the effluent is represented by a collection of particles moving in time and changing their properties. In the Eulerian system, the space is represented by a mesh of fixed points defined by their spatial coordinates, on which differential equations are solved. 3. Hydrodynamic models. These are the most general and rigorous models for effluent discharge simulation. They solve differential equations with complete partial derivates.
2.2. Mixing models There are three types of numerical models according to their mathematical approach: 1. Models based on a dimensional analysis, which are based on a number of important simplifying assumptions. They are used to
Below there is a short description of the four numerical models used in this work: CORMIX1 (“Submerged Single Port Discharge”) is a secondary model included in the CORMIX v6 package [8] for a positive, neutral or negative buoyancy submerged jet, which is discharged into the receiving environment through individual submerged jet or individual
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Table 1 The following parameters were used in CORMIX1 to simulate near field sampling area. DS: Discharge excess salinity. DV: Discharge velocity. DD: Discharge density. DDp: Discharge depth. WS: Wind speed. Ua: Current speed. ED: Environmental density. PD: Port diameter. Ho: Port height. θ: Vertical angle. σ: Horizontal angle. Discharge configuration
Environmental conditions
DS (psu)
DV (ms− 1)
DD (Kgm− 3)
DDp (m)
WS (ms− 1)
Ua (ms− 1)
ED (Kgm− 3)
PD (m)
Ho (m)
θ(º)
σ(º)
30.65
3.64
1,048.56
34.9
11
0.08
1,024.02
0.71
4.5
60
26.12
emerged jet near the surface. CORMIX1 does not solve any differential equation. It is based on dimensional analysis for classifying the studied flow into one of the 35 existing categories. In each category, the sub-model has some calculation modules associated with it to describe the behaviour of the effluent. The main limitation of CORMIX1 is the lack of validation studies for dilution rates in negatively buoyant effluents. It works with jets that interact with contours as surface, lateral boundaries or seafloor. CORJET (Cornell Buoyant Jet Integral Model) is an Eulerian threedimensional integral secondary model included in CORMIX v6 package [3,8], which is also used for submerged jet discharges with positive, neutral or negative buoyancy. It is a postprocessor model for detailed near-field analysis for single-port and multi-port diffusers in unbounded environments. It is used only when CORMIX prediction indicates that a jet does not interact with the sea surface or with any obstacle nearby [15,16]. The model simulation lasts until the jet hits the sea surface or bottom, as it is restricted only to the near-field. The coefficients appearing in integrated equations in this model were obtained from experiments with positive buoyancy tests. This model allows working with vertical velocity and density profiles. The MEDVSA-IJETG model corresponds to another Eulerian approach of governing differential equations, which are integrated in the cross section of the jet, assuming unlimited receiving environment, self-similarity between sections and Gauss-type distribution of the variables in the cross section [19]. The theoretical basis of this model is the same as CORJET, so it has been programmed with the “entrainment” closure model used in CORJET with some coefficient modifications [19]. Because it is assumed that the receiving environment is unlimited, it is not possible to simulate the interaction of the effluent with any boundaries, so that in the case of a hypersaline discharge, the simulation is limited to the section before the jet hits the sea bottom (only if there has been no impact before any other contour). The UM3 (Updated Merge 3D) model is included in Visual Plumes package, and is a Lagrangian three-dimensional model for simulating single and multi-port submerged discharges [12-14]. This model features the projected-area-entrainment (PAE) hypothesis, which has been generalised to include an entrainment term corresponding to the third-dimension: a cross-current term. The UM3 model does not detect the presence of sea bottom, so the UM3 user rules out the results beyond an impingement point. This model transforms the system of partial differential equations to another system of ordinary differential equations, which the software solves using the Runge– Kutta fourth order numerical method. As CORJET model, UM3 model is able to simulate the interaction of the jet with different current and density data from several depth levels. A sensitivity analysis was executed in each mixing model to identify the most influential input parameters on the simulation results. This operated by varying the value of one parameter while holding the rest constant and then checking whether dilution results change. It is also useful to find out which are the best discharge configurations to minimise marine impact. Nine parameters shared by the four models were selected to carry out this analysis: ambient density, current velocity, effluent density, flow rate, diffuser diameter, horizontal angle, vertical angle, discharge depth and diffuser height. In statistics, Kolmogorov-Smirnov is a nonparametric test used to determine whether our field measurement distribution is significantly different from the normal (Gaussian) distribution. In this case, it
also serves as a goodness of fit test. K–S tests standardise samples and compare them with the standard Gaussian distribution, which provides an adequate representation of the distributions of a large number of physical variables [5]. A logarithmic regression was also applied to salinity data logged with CTD in order to verify whether the models followed the same salinity trend. A chi-square test was applied to detect differences between our observed field measurements and the model simulations. This test calculates the chisquared statistic and a p-value that indicates if observed and expected frequencies are significantly different or not [9]. Moreover, a multivariate analysis was made using the statistical package Primer Version 5 [6]. Bray-Curtis similarity coefficients were calculated to formulate a similarity matrix used to plot a hierarchical cluster dendrogram. 3. Results Retrieved Doppler data showed that average current velocity was 77.9 ± 0.4 mms − 1 and average current direction was 116.12 ± 0.36° (90 + 26.12°) measured from north increasing clockwise, as the current rose in Fig. 3 shows. These data were taken in the 31–29 m depth range (the closest range to discharge point). A vertical density distribution profile was made using a CTD (Fig. 4), finding small changes in density when increasing depth. Previous environmental characteristics and discharge parameters from the two desalination plants were analysed by CORMIX1. Simulated results by the model indicated that maximum jet height is reached
Fig. 3. Current rose. Current direction distribution and percent of different current velocity measurements. (31–29 m depth).
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Density (rho) 25 0
Salinity vs Distance from Diffuser
80
28
221
CTD CORMIX1
70
Salinity (psu)
CORJET
5
10
MEDVSA
60
UM3 Log Reg (CTD)
50
40
Depth (m)
15
30 0
5
10
15
20
Distance (m) 20 Fig. 5. Brine jet salinity (psu) measured at different distances from the diffuser (♦). Salinity was simulated by the four models used in this study (CORMIX1, CORJET, MEDVSA and UM3).
25
the pollutant concentration at different sampling points. [10]. Dilution rate is obtained with the equation:
30
S¼
ðCo−CaÞ ðC−CaÞ
35
40 Fig. 4. Density distribution profile of the water column near discharge point.
at 7.71 m from the diffuser. It also indicated that the jet would hit the bottom (impingement point) at about 15.77 m from the diffuser. This data was obtained before diving in order to determine an adequate distance from the diffuser that would cover the sampling area. Divers submerged at the discharge point in order to reach the end of the outfall pipeline and to take salinity measurements with the CTD logger inside the brine jet. Registered background ambient salinity surrounding the diffuser was 37.8 psu, while discharged salinity was 68.76 psu. The four models were run with discharge and environmental parameters taken on the same day in desalination plants and field. Table 2 shows these parameters. Salinity data from CTD at different distances were plotted in a histogram, then a logarithmic regression was applied, achieving a strong correlation (R 2 = 0.875). Salinity output from the four models were also plotted together, producing the graph in Fig. 5, which showed that model prediction trends agreed with our actual data curve trend. UM3 trends are the results that kept closest to our observed data logarithmic curve. Every model used in this work overvalued salinity data from the sampling point located in the fifth metre to the end of the near field. Table 3 summarises the salinity and dilution rate values from the CTD and also from the four mixing model outputs at different sampling points. Dilution rate is defined as the dimensionless ratio of pollutant (brine) concentration in the effluent prior to its discharge to
where S is dilution rate; Co is brine salinity (psu) prior to discharge; Ca is background (ambient) salinity (psu) and C is brine jet salinity (psu) at different distances. Kolmogorov–Smirnov test showed that the salinity data measured inside the brine jet by divers at different sampling points fits a Gaussian distribution (p = 0.279), CORJET and MEDVSA output data also fit this distribution, but CORMIX1 and UM3 case did not. The measured dilution rate also fits a Gaussian distribution (p = 0.283) as do the CORMIX1, CORJET and MEDVSA models, but not the UM3 (Table 4). In order to detect the differences between observed (CTD samples) and expected (models output) salinity values, these results were plotted. Observed salinity was represented against predicted salinity in Fig. 6, where it was noticed that most of the expected results were placed in the overestimation area. CORMIX1, CORJET and MEDVSA models overestimated every salinity value, therefore becoming conservative models in this specific type of discharge. The UM3 model was the only one in which salinity prediction was higher than measured salinity at the beginning of the jet (distances 1, 2 and 4 m) when working with low dilution values. Chi-square test results in Table 5 indicated that the UM3 model simulated the closest prediction to our measured salinity (p = 0.840) and also that the UM3 prediction was different from the CORMIX1, CORJET and MEDVSA. It can be concluded that the CORMIX1, CORJET and MEDVSA predictions did not show any significant difference from each other (p = 1). These chi-square results coincide with the cluster-plot similarity results (Fig. 7), in which CORMIX, CORJET and MEDVSA models created a segregated group due to high similarity shared on their salinity simulations (99.35%). UM3, separated from other simulations in the cluster-plot, became the model whose prediction was most similar to the CTD salinity data, with a similarity of 96.44% with it.
Table 2 Parameters used in the four mixing models simulations. DS: Discharge excess salinity. DV: Discharge velocity. DD: Discharge density. DDp: Discharge depth. WS: Wind speed. Ua: Current speed. ED: Environmental density. PD: Port diameter. Ho: Port height. θ: Vertical angle. σ: Horizontal angle. Discharge configuration
Environmental conditions
DS (psu)
DV (ms− 1)
DD (Kg m− 3)
DDp (m)
WS (ms− 1)
Ua (ms− 1)
ED (Kg m− 3)
PD (m)
Ho (m)
θ (º)
σ (º)
30.96
3.67
1,054.04
34.9
2
0.08
1,026.66
0.71
4.5
60
26.12
222
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Table 3 Salinity expressed in psu and dilution rate (Dil Rate) obtained from CTD measurements and simulated by the four models at each sampling point. CTD
CORMIX1
CORJET
MEDVSA
UM3
Distance (m)
Salinity
Dil rate
Salinity
Dil rate
Salinity
Dil rate
Salinity
Dil rate
Salinity
Dil rate
0 1 2 3 4 5 6 7 9 11 13 15
68.76 67.70 52.80 41.61 49.15 40.87 42.61 38.66 41.41 38.96 38.55 38.77
1.00 1.04 2.06 8.13 2.73 10.08 6.44 36.00 8.58 26.69 41.28 31.92
68.76 68.76 64.70 56.80 53.20 50.80 49.40 48.10 46.52 44.77 43.06 41.47
1.00 1.00 1.15 1.63 2.01 2.38 2.67 3.01 3.55 4.44 5.89 8.44
68.76 68.76 64.50 56.90 54.00 51.20 49.60 48.20 46.78 44.87 43.09 41.50
1.00 1.00 1.16 1.62 1.91 2.31 2.62 2.98 3.45 4.38 5.85 8.37
68.76 68.76 65.65 56.35 52.09 49.69 48.20 47.29 45.84 44.30 42.77 41.83
1.00 1.00 1.11 1.67 2.17 2.60 2.98 3.26 3.85 4.76 6.23 7.68
68.76 52.68 47.62 45.39 44.09 43.22 42.73 42.46 42.03 41.30 40.47 39.80
1.00 2.08 3.15 4.08 4.92 5.71 6.28 6.64 7.32 8.86 11.60 15.50
65
Measured salinity
Table 6 summarises sensitivity analysis results, i.e., the effect of input parameter variations in the four simulated models results. Changes in dilution rate at the impingement point are presented as a percent of change from the base case. Every model had notably sensitive results when changes in diffuser diameter, flow rate and current velocity were made. CORMIX1, CORJET and UM3 also had very sensitive results with diffuser height variations, but in the MEDVSA case, diffuser height only accepted a variation range between 1 and 4 m; in any case MEDVSA was insensitive to this parameter variation. MEDVSA was the most sensitive model whether changes in flow rate, effluent density or ambient density were made. UM3 was the most sensitive of the four models when varying diffuser diameter, diffuser height, vertical angle and current velocity. The lowest changes in dilution for each model appeared when discharge depth and horizontal angle (and also ambient density, in UM3 case) were varied.
CORMIX1
CORJET
MEDVSA
UM3
55
45
35
35
45
55
65
Salinity Predictions
4. Discussion A large number of laboratory experiments and mathematical model have previously been developed in order to understand positively buoyancy discharges. Current mixing models for brine discharges are physically based on the same theoretical development as models designed for positively buoyancy discharges (e.g. wastewater discharges) [17]. Our experience suggests that such development does not provide accurate dilution process predictions in hypersaline effluent plumes, because important physical processes are neglected by such an approach. The main drawback of mixing models for negatively buoyancy discharges is that they have not been tested with actual RO desalination plant discharge data. Many near field studies for brine discharge into a marine environment compare mixing models with laboratory experiment results (Cipollina et al., 2004). Other authors compare these models with field data taken from boats or platforms [22], but never with actual field data taken inside the brine jet by divers. It should be essential to compare in situ measurements with model results in order to verify the accuracy of mixing models and also to develop new mixing models for desalination plant discharges.
Table 4 P-value for K–S test with excess salinity and dilution data from CTD and the four mixing models. Model
K–S test (salinity)
K–S test (dilution)
CTD CORMIX1 CORJET MEDVSA UM3
0.279 0.009 0.130 0.010 0.000
0.283 0.460 0.747 0.877 0.020
Fig. 6. Comparison of salinity data measured with CTD against salinity predictions simulated by models. White diamonds are CORMIX1 results, black squares are CORJET results, white triangles are MEDVSA results and black circles are UM3 results.
Data collection inside the brine jet is a difficult task. The main objective while diving was to look for the maximum salinity value in each sampling point, theoretically located in the brine jet centreline. As Fig. 5 shows, this maximum salinity value is difficult to obtain (having higher and lower values) because the first metres of the jet are characterised by high velocity and a small spreading area. The jet centreline is easy to locate, but it is very hard to maintain the CTD at a constant position. On the other hand, in the final metres of the near-field, jet velocity is weak but and has a large spreading area, making it difficult to find the centreline. The turbulence that involves the discharge of brine through a diffuser also made it difficult, creating an irregular shape and a more complex jet structure. As we can see in Fig. 5, the dilution processes in CORMIX, CORJET and MEDVSA started some metres away from the outfall. In the UM3 case, the dilution process started at discharge point. This is due to the physical development of UM3, because the model does not redirect
Table 5 Summary for Chi-Square test p-value between observed (CTD) and expected data (models). p-value
CORMIX1
CORJET
MEDVSA
UM3
CTD CORMIX1 CORJET MEDVSA
0.276
0.250 1.000
0.360 1.000 1.000
0.840 0.047 0.039 0.064
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Fig. 7. Similarity dendrogram obtained from Bray–Curtis similarity matrix for salinity data.
the momentum flux vertically. UM3 directs momentum flux downstream, and does not account for the upstream discharges [24]. This means that UM3 is the only model used in this work that applies governing equations immediately after the brine jet leaves the diffuser and contacts with environmental water. This could be the main factor that explains the salinity overestimation made by CORMIX1, CORJET and MEDVSA. Field measurements were only taken up to 15 m, corresponding with the impingement point. CORJET, MEDVSA and UM3 models do not work effectively from this point because they do not consider interactions with boundaries. Working with these discharge conditions, MEDVSA resulted in the most conservative model at the end of the near-field followed by CORMIX1 and CORJET. UM3 is the only model that simulated undervalued salinity data in the first metres of the jet, becoming the least conservative model. From the fifth metre, it becomes more conservative, but its prediction stayed close to our field measurements. The UM3 model presented the biggest dilution rate variations in sensitivity analysis results, followed by CORMIX1, MEDVSA and CORJET. The most important input parameters in these variations were
flow rate and diffuser diameter. Simulating the same brine discharge through a 20 cm smaller diffuser, models increased dilution rate at impingement point by 24.0–125.3%. Doubling the flow rate, model simulations increased their dilution rate by 63.1–99.5%. This increase in dilution rate is because both parameters are correlated to discharge velocity: Decreasing diffuser diameter and increasing flow rates result in higher discharge velocity. This also means enhancing turbulence, mixing process and dilution rate in the brine jet. Each model also showed that as the difference between ambient and effluent density becomes bigger, the mixing process becomes more complicated (lower dilution rate). Results for ambient current parameters revealed high sensitivity in each model when varying ambient current velocity, but not when varying its direction; current velocity becomes a very important parameter to take future current measurements, not needing such accurate current direction data. 5. Conclusions The shortage of near-field actual measurements in mixing model validation works hindered the verification of whether these models
Table 6 Base case, variation range and results of sensitivity analysis for the four mixing models. Data are presented as percent of change in dilution rate at impingement point from the base case. Sensitivity analysis. Percent of variation in dilution rate from base case Model Parameter
Base case
Parameters range
CORMIX1
CORJET
MEDVSA
UM3
Ambient density (kg/m3)
1026.66
Current velocity (m/s)
0.08
Effluent density (kg/m3)
1054.04
Flow rate (m3/s)
1.453
Diffuser diameter (m)
0.71
Horizontal angle (º)
26.12
Vertical angle (º)
60
Discharge depth (m)
34.9
Diffuser height (m)
4.5
1024 1028 0.05 0.15 1045 1055 1 3 0.51 0.91 10 40 45 75 25 45 1 10
− 6.2 2.5 − 6.8 22.1 11.4 − 7.8 − 20.6 63.1 106.9 − 16.0 0.0 − 0.6 − 5.2 − 2.1 0.0 0.0 − 21.0 36.8
− 5.4 1.8 − 6.3 27.6 11.5 − 9.0 − 20.3 74.1 27.6 − 14.5 − 1.1 0.4 − 3.4 − 2.1 0.0 0.0 − 20.6 27.6
− 6.3 19.2 − 9.6 28.3 16.1 − 11.2 − 29.3 99.5 24.0 − 14.4 − 1.2 − 1.2 − 5.0 − 6.5 0.0 0.0 0.0 0.0
− 5.7 4.2 − 8.2 32.8 14.4 − 10.1 − 24.1 83.8 125.3 − 43.8 − 5.0 3.5 40.0 − 4.7 0.0 0.0 − 22.7 40.0
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were working accurately when predicting hypersaline jet behaviour or not. This work registered the dilution process measured in situ inside the brine jet by divers. Actual field measurements should be essential in each validation work or in the development of new mixing models. Based on our own experience, CORMIX1, CORJET and MEDVSA are very conservative models when working with this specific brine discharge. It was not possible to find any salinity data inside the brine jet that was higher than their simulations. In the other hand, UM3 is the only model in which the simulated salinity was higher than the measured salinity in three sampling points (corresponding to 1, 2 and 4 m from the diffuser), i.e., UM3 is not a conservative model at the beginning of this particular dilution process. Fig. 5 shows that simulations made by CORMIX1, CORJET and MEDVSA closely followed each other and did not show any significant difference between their output data. Moreover, UM3 model output data followed our field measurements in a good approximation, sharing higher similarity. We concluded that UM3 was the most realistic mixing model used in this paper. As can be seen in sensitivity analysis results, every model tested in this work was very sensitive to input parameters correlated with jet velocity (flow rate and diffuser diameter). Brine jet exit velocity should be seriously considered when managing SWRO desalination plant discharges in coastal areas through outfall pipes in order to reduce adverse impacts on the marine environment.
[7] [8]
[9] [10] [11]
[12] [13] [14]
[15] [16]
[17]
[18]
Acknowledgements We would like to thank Dr. Walter E. Frick for helping us with UM3 aspiration coefficient explanations. The language help provided by Nicholas Marchant, Peadar O'Connell, Mags Flaherty and Muriel Ennis during the research is gratefully acknowledged. This work was funded by “Mancomunidad Canales del Taibilla.”
[19]
[20]
[21]
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Contents lists available at SciVerse ScienceDirect
Desalination journal homepage: www.elsevier.com/locate/desal
Comparing four mixing zone models with brine discharge measurements from a reverse osmosis desalination plant in Spain Ángel Loya-Fernández ⁎, Luis Miguel Ferrero-Vicente, Candela Marco-Méndez, Elena Martínez-García, José Zubcoff, José Luis Sánchez-Lizaso Department of Marine Sciences and Applied Biology, University of Alicante, PO Box 99, E-03080 Alicante, Spain
a r t i c l e
i n f o
Article history: Received 28 July 2011 Received in revised form 7 November 2011 Accepted 8 November 2011 Available online 30 November 2011 Keywords: SWRO Brine discharge Ocean outfall Diffuser Negatively buoyant jet Near-field mixing zone models Conservative
a b s t r a c t Nuevo Canal de Cartagena (Spain) desalination plants discharge hypersaline effluent through a submarine outfall pipeline, creating a negatively buoyant brine jet. Many near-field mixing models are used in the prediction and management of brine discharges, but they have rarely been compared with field salinity measurements obtained directly inside the brine jet. Two divers obtained these field measurements and compared them with CORMIX1, CORJET, MEDVSA and UM3 mixing zone model predictions. In general, each model was quite conservative in its results, except UM3, whose prediction presented the best approximation to measured data. It is concluded that direct field measurements should be essential when testing the accuracy of current models or developing new near-field mixing models. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Desalination plants have been associated with some potential environmental effects; these are mainly attributed to the concentrate and chemicals discharges into the marine environment which may impair coastal water quality and affect marine life [17]. Furthermore, recent technology has reduced the cost of the desalination process [1], enhancing desalination capacity; therefore there is an increased need to accurately assess the potential environmental effects of such plants. These effects have been documented previously [11], including our sampling area near the discharge point of the Nuevo Canal de Cartagena desalination plants [20,21]. The nature of the brine differs significantly depending on the process employed in seawater desalination. Thermal desalination processes generate a hypersaline and high-temperature effluent with a neutral or positively buoyant flux, causing the plume to rise and spread on the sea-surface [4]. On the other hand, the seawater reverse osmosis (SWRO) process, which is used at the Nuevo Canal de Cartagena (Spain) desalination plants, generates a negatively buoyant flux or dense jet, which spreads as a density current on the seabed.
⁎ Corresponding author. Tel.: + 34 96 590 3473; fax: + 34 96 590 9897. E-mail address: [email protected] (Á. Loya-Fernández). 0011-9164/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2011.11.026
In Spain, 70% of desalinated water is produced by SWRO technology [18]. Methods employed for the disposal of brine discharges vary widely, but the most common procedure in SWRO desalination plants located close to the sea is discharging via submarine outfall, which usually consists of a submerged pipeline and a diffuser section. Diffusers are devices used to speed up the dilution process with the nearby seawater in highly concentrated brine jets [22] to enhance jet exit velocity and therefore the turbulence and mixing process. The outfall at Nuevo Canal de Cartagena has only one diffuser at the end of the pipeline which creates an inclined dense jet. This kind of jet was recommended for the discharge of brine from desalination plants to achieve maximum mixing process efficiency [25]. The mixing process of effluents discharged from a submerged outfall consist of near field and far field mixing processes, which occur at different spatial and time scales [26]. Thus, two mixing regions were defined when working with discharges of brine into the receiving water body: the near-field and far-field region. The near-field region is located in the vicinity of the discharge point and is affected by turbulent jet mixing, which depends critically on discharge parameters, brine physical properties and environmental physical properties. This mixing area extends from the effluent's point of release to its interaction with a physical boundary (the seafloor, in the present case). The end of the near-field is considered to be the point at which the turbulence collapses [3]. At this point, the far-field region begins and the brine jet is now named brine plume. The far-field plume
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forms a gravity driven current moving along the seafloor and mixing is only affected by the physical processes of advection and diffusion [22]. Logically, the more diluted the jet is in the near-field region complimented by a correct discharge configuration, the more diluted it will be in far field region, and thus reducing the affected area. The behaviour of brine jet trajectory and its dilution process in the near field have been commonly studied through laboratory experiments, mixing zone models or field observation. Many studies with controlled laboratory experiments were found in the literature, but drawbacks have been identified in this research [23]. In recent years, computer technology has developed mixing zone models as a predictive tool which provided simulations of brine discharges behaviour in the seawater receiving body [10]. This technology results in savings of time and expenses compared with laboratory experiments, but it is a complex and difficult task [2]. CORMIX and VISUAL PLUMES are some of the most notable commercial software for brine discharge modelling developed and supported by EPA. These models predict brine behaviour, including trajectory, dimensions and degrees of dilution, by considering the effluent properties, the disposal configuration and the ambient conditions [7,13]. These two programmes were used for this study as well as a new online modelling tool focused on negatively buoyant effluents called MEDVSA [19] in order to compare their simulations with field salinity data. Many comparison and verification studies have been carried out between field measurements and different mixing models. In studies with positively buoyant discharges, numerical modelling has been well known for some time and it has been utilised in many verification works, but it was found that some models had many limitations when they were compared with field measurements [24]. On the other hand, most of the validation studies with negatively buoyant discharges were made in laboratories with experimental effluents. Some of the validation works indicated that existing models significantly underestimated the dilution and plume location of these experimental effluents [17]. Very few validation studies compared mixing model simulations with field measurements, because in situ field measurements of operating SWRO desalination plants outfalls are quite difficult and rarely well known (particularly with submerged plumes) because of corresponding environmental conditions such as currents and density stratification [23]. This paper focuses on two objectives. The first was to record in situ salinity measurements inside the brine jet using divers. The second was to compare these measurements with simulations made by four near field mixing models (CORMIX1, CORJET, MEDVSA and UM3) with the intention of determining their prediction accuracy.
2. Material and methods The Nuevo Canal de Cartagena desalination plants are placed 680 meters from the Mediterranean coast in San Pedro del Pinatar, Murcia, SE Spain. These two plants discharge 62 Hm3 year− 1 of brine, sharing a submarine outfall pipeline. The onshore section of the pipeline is 680 m from the desalination plant to the coast, where it fits with the submarine section, which is 5100 m long. Fig. 1 shows the brine jet scheme at the end of the pipeline, 34.9 m depth, where a 60º inclined single diffuser elevated 4.5 m from seabed can be found. The diffuser location is presented in Fig. 2 (UTM coordinates x = 701683 and y = 4189682). Its orientation is 90º from North towards east. 2.1. In-situ measurements A 600 KHz ADCP Workhorse Sentinel Doppler was previously deployed around 100 m away from the end of the outfall pipe at 34.1 meters depth (UTM coordinates x = 701706 and y = 4189571. Fig. 2) for a period of one month, obtaining current average direction and current average velocity at vertical intervals of 2 m, from the bottom to the surface. Working with the current data, previous discharge configuration data (discharge excess salinity, discharge velocity, discharge density and discharge depth) and previous measured environmental data (wind speed, current speed, environmental density, port diameter, port height, vertical angle and horizontal angle) in CORMIX1, the simulated area of near-field region was obtained. This information was used in order to calculate the field sampling area (Table 1). After the near-field area simulated by CORMIX1 was known, it was decided that the best option was to take actual salinity data by scuba diving directly inside the hypersaline jet at 34.9 m depth. In October 2010, A CTD (RBR-XR 620 CTD+; resolution ±0.01 psu and ±0.005 °C) was used to take temperature, conductivity and depth measurements in situ, logging one sample each second. This CTD is suitable for use outside the usual limits of the PSS-78 practical salinity scale, in applications such as desalination brine monitoring. Once the outfall was located, the first diver placed the beginning of a tape measure at the beginning of the jet, (“A” in Fig. 1). The second diver followed the x-axis diffuser direction, keeping the same depth (z = 0) and reaching the different sampling distances. Once diver two had reached a specific distance, he came up into the brine jet (“B” in Fig. 1) following a perpendicular to x-axis trajectory (helped by a buoy) with the CTD sensor to register temperature, conductivity and depth until he reached 15 m away, where it is supposed (by CORMIX1) the near field would end.
Fig. 1. The position of the divers during the measurements of an inclined dense jet. A: First diver position. B: Second diver position. θ: Jet Inclination angle with regard to the bottom. C: Jet centreline. Ho: Diffuser height from the bottom. Xi: Distance to bottom hit (at the centreline). Zmax: Maximum height reached by the jet. Ua: Ambient current.
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219
Fig. 2. Area map showing site near desalination outfall region. BD: Brine discharge point; DP: Doppler position.
Samples were taken at one meter intervals from the diffuser to 7 meters distance, and at two meters intervals from 9 to15 m. Position of divers as well as brine jet parameters (jet inclination angle, jet centreline, diffuser height, distance to bottom hit, maximum height reached, ambient current, depth and coordinate system) are showed in Fig. 1. The same CTD sensor was also used to describe water column physical characteristics from the surface to the sea bottom. Another CTD inside desalination plant (previous to outfall inside desalination plant) registered salinity and temperature measurements each 10 minutes from 2005 to present; it is possible to know salinity and temperature of the brine discharge in the moment salinity was taken in the field. Flow details were obtained from SWRO desalination plant electromagnetic flow meter (Endress + Hauser Promag50).
form reasonable hypothesis about complex physical situations that can be tested experimentally. 2. Jet integral models are models based on the integration of differential equations along the cross section of flow which are applied to brine discharges and solve the hydrodynamics and transport equations (adapted to a negatively buoyant effluent). These equations can be set up by a Lagrangian or Eulerian system. In the Lagrangian system, the effluent is represented by a collection of particles moving in time and changing their properties. In the Eulerian system, the space is represented by a mesh of fixed points defined by their spatial coordinates, on which differential equations are solved. 3. Hydrodynamic models. These are the most general and rigorous models for effluent discharge simulation. They solve differential equations with complete partial derivates.
2.2. Mixing models There are three types of numerical models according to their mathematical approach: 1. Models based on a dimensional analysis, which are based on a number of important simplifying assumptions. They are used to
Below there is a short description of the four numerical models used in this work: CORMIX1 (“Submerged Single Port Discharge”) is a secondary model included in the CORMIX v6 package [8] for a positive, neutral or negative buoyancy submerged jet, which is discharged into the receiving environment through individual submerged jet or individual
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Table 1 The following parameters were used in CORMIX1 to simulate near field sampling area. DS: Discharge excess salinity. DV: Discharge velocity. DD: Discharge density. DDp: Discharge depth. WS: Wind speed. Ua: Current speed. ED: Environmental density. PD: Port diameter. Ho: Port height. θ: Vertical angle. σ: Horizontal angle. Discharge configuration
Environmental conditions
DS (psu)
DV (ms− 1)
DD (Kgm− 3)
DDp (m)
WS (ms− 1)
Ua (ms− 1)
ED (Kgm− 3)
PD (m)
Ho (m)
θ(º)
σ(º)
30.65
3.64
1,048.56
34.9
11
0.08
1,024.02
0.71
4.5
60
26.12
emerged jet near the surface. CORMIX1 does not solve any differential equation. It is based on dimensional analysis for classifying the studied flow into one of the 35 existing categories. In each category, the sub-model has some calculation modules associated with it to describe the behaviour of the effluent. The main limitation of CORMIX1 is the lack of validation studies for dilution rates in negatively buoyant effluents. It works with jets that interact with contours as surface, lateral boundaries or seafloor. CORJET (Cornell Buoyant Jet Integral Model) is an Eulerian threedimensional integral secondary model included in CORMIX v6 package [3,8], which is also used for submerged jet discharges with positive, neutral or negative buoyancy. It is a postprocessor model for detailed near-field analysis for single-port and multi-port diffusers in unbounded environments. It is used only when CORMIX prediction indicates that a jet does not interact with the sea surface or with any obstacle nearby [15,16]. The model simulation lasts until the jet hits the sea surface or bottom, as it is restricted only to the near-field. The coefficients appearing in integrated equations in this model were obtained from experiments with positive buoyancy tests. This model allows working with vertical velocity and density profiles. The MEDVSA-IJETG model corresponds to another Eulerian approach of governing differential equations, which are integrated in the cross section of the jet, assuming unlimited receiving environment, self-similarity between sections and Gauss-type distribution of the variables in the cross section [19]. The theoretical basis of this model is the same as CORJET, so it has been programmed with the “entrainment” closure model used in CORJET with some coefficient modifications [19]. Because it is assumed that the receiving environment is unlimited, it is not possible to simulate the interaction of the effluent with any boundaries, so that in the case of a hypersaline discharge, the simulation is limited to the section before the jet hits the sea bottom (only if there has been no impact before any other contour). The UM3 (Updated Merge 3D) model is included in Visual Plumes package, and is a Lagrangian three-dimensional model for simulating single and multi-port submerged discharges [12-14]. This model features the projected-area-entrainment (PAE) hypothesis, which has been generalised to include an entrainment term corresponding to the third-dimension: a cross-current term. The UM3 model does not detect the presence of sea bottom, so the UM3 user rules out the results beyond an impingement point. This model transforms the system of partial differential equations to another system of ordinary differential equations, which the software solves using the Runge– Kutta fourth order numerical method. As CORJET model, UM3 model is able to simulate the interaction of the jet with different current and density data from several depth levels. A sensitivity analysis was executed in each mixing model to identify the most influential input parameters on the simulation results. This operated by varying the value of one parameter while holding the rest constant and then checking whether dilution results change. It is also useful to find out which are the best discharge configurations to minimise marine impact. Nine parameters shared by the four models were selected to carry out this analysis: ambient density, current velocity, effluent density, flow rate, diffuser diameter, horizontal angle, vertical angle, discharge depth and diffuser height. In statistics, Kolmogorov-Smirnov is a nonparametric test used to determine whether our field measurement distribution is significantly different from the normal (Gaussian) distribution. In this case, it
also serves as a goodness of fit test. K–S tests standardise samples and compare them with the standard Gaussian distribution, which provides an adequate representation of the distributions of a large number of physical variables [5]. A logarithmic regression was also applied to salinity data logged with CTD in order to verify whether the models followed the same salinity trend. A chi-square test was applied to detect differences between our observed field measurements and the model simulations. This test calculates the chisquared statistic and a p-value that indicates if observed and expected frequencies are significantly different or not [9]. Moreover, a multivariate analysis was made using the statistical package Primer Version 5 [6]. Bray-Curtis similarity coefficients were calculated to formulate a similarity matrix used to plot a hierarchical cluster dendrogram. 3. Results Retrieved Doppler data showed that average current velocity was 77.9 ± 0.4 mms − 1 and average current direction was 116.12 ± 0.36° (90 + 26.12°) measured from north increasing clockwise, as the current rose in Fig. 3 shows. These data were taken in the 31–29 m depth range (the closest range to discharge point). A vertical density distribution profile was made using a CTD (Fig. 4), finding small changes in density when increasing depth. Previous environmental characteristics and discharge parameters from the two desalination plants were analysed by CORMIX1. Simulated results by the model indicated that maximum jet height is reached
Fig. 3. Current rose. Current direction distribution and percent of different current velocity measurements. (31–29 m depth).
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Density (rho) 25 0
Salinity vs Distance from Diffuser
80
28
221
CTD CORMIX1
70
Salinity (psu)
CORJET
5
10
MEDVSA
60
UM3 Log Reg (CTD)
50
40
Depth (m)
15
30 0
5
10
15
20
Distance (m) 20 Fig. 5. Brine jet salinity (psu) measured at different distances from the diffuser (♦). Salinity was simulated by the four models used in this study (CORMIX1, CORJET, MEDVSA and UM3).
25
the pollutant concentration at different sampling points. [10]. Dilution rate is obtained with the equation:
30
S¼
ðCo−CaÞ ðC−CaÞ
35
40 Fig. 4. Density distribution profile of the water column near discharge point.
at 7.71 m from the diffuser. It also indicated that the jet would hit the bottom (impingement point) at about 15.77 m from the diffuser. This data was obtained before diving in order to determine an adequate distance from the diffuser that would cover the sampling area. Divers submerged at the discharge point in order to reach the end of the outfall pipeline and to take salinity measurements with the CTD logger inside the brine jet. Registered background ambient salinity surrounding the diffuser was 37.8 psu, while discharged salinity was 68.76 psu. The four models were run with discharge and environmental parameters taken on the same day in desalination plants and field. Table 2 shows these parameters. Salinity data from CTD at different distances were plotted in a histogram, then a logarithmic regression was applied, achieving a strong correlation (R 2 = 0.875). Salinity output from the four models were also plotted together, producing the graph in Fig. 5, which showed that model prediction trends agreed with our actual data curve trend. UM3 trends are the results that kept closest to our observed data logarithmic curve. Every model used in this work overvalued salinity data from the sampling point located in the fifth metre to the end of the near field. Table 3 summarises the salinity and dilution rate values from the CTD and also from the four mixing model outputs at different sampling points. Dilution rate is defined as the dimensionless ratio of pollutant (brine) concentration in the effluent prior to its discharge to
where S is dilution rate; Co is brine salinity (psu) prior to discharge; Ca is background (ambient) salinity (psu) and C is brine jet salinity (psu) at different distances. Kolmogorov–Smirnov test showed that the salinity data measured inside the brine jet by divers at different sampling points fits a Gaussian distribution (p = 0.279), CORJET and MEDVSA output data also fit this distribution, but CORMIX1 and UM3 case did not. The measured dilution rate also fits a Gaussian distribution (p = 0.283) as do the CORMIX1, CORJET and MEDVSA models, but not the UM3 (Table 4). In order to detect the differences between observed (CTD samples) and expected (models output) salinity values, these results were plotted. Observed salinity was represented against predicted salinity in Fig. 6, where it was noticed that most of the expected results were placed in the overestimation area. CORMIX1, CORJET and MEDVSA models overestimated every salinity value, therefore becoming conservative models in this specific type of discharge. The UM3 model was the only one in which salinity prediction was higher than measured salinity at the beginning of the jet (distances 1, 2 and 4 m) when working with low dilution values. Chi-square test results in Table 5 indicated that the UM3 model simulated the closest prediction to our measured salinity (p = 0.840) and also that the UM3 prediction was different from the CORMIX1, CORJET and MEDVSA. It can be concluded that the CORMIX1, CORJET and MEDVSA predictions did not show any significant difference from each other (p = 1). These chi-square results coincide with the cluster-plot similarity results (Fig. 7), in which CORMIX, CORJET and MEDVSA models created a segregated group due to high similarity shared on their salinity simulations (99.35%). UM3, separated from other simulations in the cluster-plot, became the model whose prediction was most similar to the CTD salinity data, with a similarity of 96.44% with it.
Table 2 Parameters used in the four mixing models simulations. DS: Discharge excess salinity. DV: Discharge velocity. DD: Discharge density. DDp: Discharge depth. WS: Wind speed. Ua: Current speed. ED: Environmental density. PD: Port diameter. Ho: Port height. θ: Vertical angle. σ: Horizontal angle. Discharge configuration
Environmental conditions
DS (psu)
DV (ms− 1)
DD (Kg m− 3)
DDp (m)
WS (ms− 1)
Ua (ms− 1)
ED (Kg m− 3)
PD (m)
Ho (m)
θ (º)
σ (º)
30.96
3.67
1,054.04
34.9
2
0.08
1,026.66
0.71
4.5
60
26.12
222
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Table 3 Salinity expressed in psu and dilution rate (Dil Rate) obtained from CTD measurements and simulated by the four models at each sampling point. CTD
CORMIX1
CORJET
MEDVSA
UM3
Distance (m)
Salinity
Dil rate
Salinity
Dil rate
Salinity
Dil rate
Salinity
Dil rate
Salinity
Dil rate
0 1 2 3 4 5 6 7 9 11 13 15
68.76 67.70 52.80 41.61 49.15 40.87 42.61 38.66 41.41 38.96 38.55 38.77
1.00 1.04 2.06 8.13 2.73 10.08 6.44 36.00 8.58 26.69 41.28 31.92
68.76 68.76 64.70 56.80 53.20 50.80 49.40 48.10 46.52 44.77 43.06 41.47
1.00 1.00 1.15 1.63 2.01 2.38 2.67 3.01 3.55 4.44 5.89 8.44
68.76 68.76 64.50 56.90 54.00 51.20 49.60 48.20 46.78 44.87 43.09 41.50
1.00 1.00 1.16 1.62 1.91 2.31 2.62 2.98 3.45 4.38 5.85 8.37
68.76 68.76 65.65 56.35 52.09 49.69 48.20 47.29 45.84 44.30 42.77 41.83
1.00 1.00 1.11 1.67 2.17 2.60 2.98 3.26 3.85 4.76 6.23 7.68
68.76 52.68 47.62 45.39 44.09 43.22 42.73 42.46 42.03 41.30 40.47 39.80
1.00 2.08 3.15 4.08 4.92 5.71 6.28 6.64 7.32 8.86 11.60 15.50
65
Measured salinity
Table 6 summarises sensitivity analysis results, i.e., the effect of input parameter variations in the four simulated models results. Changes in dilution rate at the impingement point are presented as a percent of change from the base case. Every model had notably sensitive results when changes in diffuser diameter, flow rate and current velocity were made. CORMIX1, CORJET and UM3 also had very sensitive results with diffuser height variations, but in the MEDVSA case, diffuser height only accepted a variation range between 1 and 4 m; in any case MEDVSA was insensitive to this parameter variation. MEDVSA was the most sensitive model whether changes in flow rate, effluent density or ambient density were made. UM3 was the most sensitive of the four models when varying diffuser diameter, diffuser height, vertical angle and current velocity. The lowest changes in dilution for each model appeared when discharge depth and horizontal angle (and also ambient density, in UM3 case) were varied.
CORMIX1
CORJET
MEDVSA
UM3
55
45
35
35
45
55
65
Salinity Predictions
4. Discussion A large number of laboratory experiments and mathematical model have previously been developed in order to understand positively buoyancy discharges. Current mixing models for brine discharges are physically based on the same theoretical development as models designed for positively buoyancy discharges (e.g. wastewater discharges) [17]. Our experience suggests that such development does not provide accurate dilution process predictions in hypersaline effluent plumes, because important physical processes are neglected by such an approach. The main drawback of mixing models for negatively buoyancy discharges is that they have not been tested with actual RO desalination plant discharge data. Many near field studies for brine discharge into a marine environment compare mixing models with laboratory experiment results (Cipollina et al., 2004). Other authors compare these models with field data taken from boats or platforms [22], but never with actual field data taken inside the brine jet by divers. It should be essential to compare in situ measurements with model results in order to verify the accuracy of mixing models and also to develop new mixing models for desalination plant discharges.
Table 4 P-value for K–S test with excess salinity and dilution data from CTD and the four mixing models. Model
K–S test (salinity)
K–S test (dilution)
CTD CORMIX1 CORJET MEDVSA UM3
0.279 0.009 0.130 0.010 0.000
0.283 0.460 0.747 0.877 0.020
Fig. 6. Comparison of salinity data measured with CTD against salinity predictions simulated by models. White diamonds are CORMIX1 results, black squares are CORJET results, white triangles are MEDVSA results and black circles are UM3 results.
Data collection inside the brine jet is a difficult task. The main objective while diving was to look for the maximum salinity value in each sampling point, theoretically located in the brine jet centreline. As Fig. 5 shows, this maximum salinity value is difficult to obtain (having higher and lower values) because the first metres of the jet are characterised by high velocity and a small spreading area. The jet centreline is easy to locate, but it is very hard to maintain the CTD at a constant position. On the other hand, in the final metres of the near-field, jet velocity is weak but and has a large spreading area, making it difficult to find the centreline. The turbulence that involves the discharge of brine through a diffuser also made it difficult, creating an irregular shape and a more complex jet structure. As we can see in Fig. 5, the dilution processes in CORMIX, CORJET and MEDVSA started some metres away from the outfall. In the UM3 case, the dilution process started at discharge point. This is due to the physical development of UM3, because the model does not redirect
Table 5 Summary for Chi-Square test p-value between observed (CTD) and expected data (models). p-value
CORMIX1
CORJET
MEDVSA
UM3
CTD CORMIX1 CORJET MEDVSA
0.276
0.250 1.000
0.360 1.000 1.000
0.840 0.047 0.039 0.064
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223
Fig. 7. Similarity dendrogram obtained from Bray–Curtis similarity matrix for salinity data.
the momentum flux vertically. UM3 directs momentum flux downstream, and does not account for the upstream discharges [24]. This means that UM3 is the only model used in this work that applies governing equations immediately after the brine jet leaves the diffuser and contacts with environmental water. This could be the main factor that explains the salinity overestimation made by CORMIX1, CORJET and MEDVSA. Field measurements were only taken up to 15 m, corresponding with the impingement point. CORJET, MEDVSA and UM3 models do not work effectively from this point because they do not consider interactions with boundaries. Working with these discharge conditions, MEDVSA resulted in the most conservative model at the end of the near-field followed by CORMIX1 and CORJET. UM3 is the only model that simulated undervalued salinity data in the first metres of the jet, becoming the least conservative model. From the fifth metre, it becomes more conservative, but its prediction stayed close to our field measurements. The UM3 model presented the biggest dilution rate variations in sensitivity analysis results, followed by CORMIX1, MEDVSA and CORJET. The most important input parameters in these variations were
flow rate and diffuser diameter. Simulating the same brine discharge through a 20 cm smaller diffuser, models increased dilution rate at impingement point by 24.0–125.3%. Doubling the flow rate, model simulations increased their dilution rate by 63.1–99.5%. This increase in dilution rate is because both parameters are correlated to discharge velocity: Decreasing diffuser diameter and increasing flow rates result in higher discharge velocity. This also means enhancing turbulence, mixing process and dilution rate in the brine jet. Each model also showed that as the difference between ambient and effluent density becomes bigger, the mixing process becomes more complicated (lower dilution rate). Results for ambient current parameters revealed high sensitivity in each model when varying ambient current velocity, but not when varying its direction; current velocity becomes a very important parameter to take future current measurements, not needing such accurate current direction data. 5. Conclusions The shortage of near-field actual measurements in mixing model validation works hindered the verification of whether these models
Table 6 Base case, variation range and results of sensitivity analysis for the four mixing models. Data are presented as percent of change in dilution rate at impingement point from the base case. Sensitivity analysis. Percent of variation in dilution rate from base case Model Parameter
Base case
Parameters range
CORMIX1
CORJET
MEDVSA
UM3
Ambient density (kg/m3)
1026.66
Current velocity (m/s)
0.08
Effluent density (kg/m3)
1054.04
Flow rate (m3/s)
1.453
Diffuser diameter (m)
0.71
Horizontal angle (º)
26.12
Vertical angle (º)
60
Discharge depth (m)
34.9
Diffuser height (m)
4.5
1024 1028 0.05 0.15 1045 1055 1 3 0.51 0.91 10 40 45 75 25 45 1 10
− 6.2 2.5 − 6.8 22.1 11.4 − 7.8 − 20.6 63.1 106.9 − 16.0 0.0 − 0.6 − 5.2 − 2.1 0.0 0.0 − 21.0 36.8
− 5.4 1.8 − 6.3 27.6 11.5 − 9.0 − 20.3 74.1 27.6 − 14.5 − 1.1 0.4 − 3.4 − 2.1 0.0 0.0 − 20.6 27.6
− 6.3 19.2 − 9.6 28.3 16.1 − 11.2 − 29.3 99.5 24.0 − 14.4 − 1.2 − 1.2 − 5.0 − 6.5 0.0 0.0 0.0 0.0
− 5.7 4.2 − 8.2 32.8 14.4 − 10.1 − 24.1 83.8 125.3 − 43.8 − 5.0 3.5 40.0 − 4.7 0.0 0.0 − 22.7 40.0
224
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were working accurately when predicting hypersaline jet behaviour or not. This work registered the dilution process measured in situ inside the brine jet by divers. Actual field measurements should be essential in each validation work or in the development of new mixing models. Based on our own experience, CORMIX1, CORJET and MEDVSA are very conservative models when working with this specific brine discharge. It was not possible to find any salinity data inside the brine jet that was higher than their simulations. In the other hand, UM3 is the only model in which the simulated salinity was higher than the measured salinity in three sampling points (corresponding to 1, 2 and 4 m from the diffuser), i.e., UM3 is not a conservative model at the beginning of this particular dilution process. Fig. 5 shows that simulations made by CORMIX1, CORJET and MEDVSA closely followed each other and did not show any significant difference between their output data. Moreover, UM3 model output data followed our field measurements in a good approximation, sharing higher similarity. We concluded that UM3 was the most realistic mixing model used in this paper. As can be seen in sensitivity analysis results, every model tested in this work was very sensitive to input parameters correlated with jet velocity (flow rate and diffuser diameter). Brine jet exit velocity should be seriously considered when managing SWRO desalination plant discharges in coastal areas through outfall pipes in order to reduce adverse impacts on the marine environment.
[7] [8]
[9] [10] [11]
[12] [13] [14]
[15] [16]
[17]
[18]
Acknowledgements We would like to thank Dr. Walter E. Frick for helping us with UM3 aspiration coefficient explanations. The language help provided by Nicholas Marchant, Peadar O'Connell, Mags Flaherty and Muriel Ennis during the research is gratefully acknowledged. This work was funded by “Mancomunidad Canales del Taibilla.”
[19]
[20]
[21]
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