There is interest in processes that can both contribute to pathogen removal and provide effective pretreatment for reverse osmosis (RO) in potable reuse systems. The most familiar reuse treatment train employs micro-/ultra-filtration (MF/UF) along with cartridge filters (CFs) upstream of RO. However, there are some applications for which other pre-RO processes, such as membrane bioreactors (MBRs), are desirable, and more research is needed on MBR for RO pretreatment. In addition, although CFs are widely used, the literature does not elucidate their capability for pathogen removal and fouling control. In this study, MBR along with absolute-rated 1 µm CFs were demonstrated for RO pretreatment at full scale. This plant included two identical trains, which provide additional robustness to the results. Online MBR and CF data confirmed performance within critical control point limits, and breach testing of the CFs demonstrated a consistent response pattern in differential pressure (dP) data and with novel derived parameters. These observed breach responses and novel parameters are a useful contribution to the online monitoring of CFs as a pathogen barrier. The combined pretreatment processes also achieved remarkably low fouling rates at the downstream RO system, lower than other recently-reported MBR-RO facilities and consistent with 5 -7 month clean-in-place intervals. The observation of significant increase in dP over 2 weeks showed that the CFs were capturing colloidal particles even from low-turbidity influent, and this resulted in lower-than-expected fouling rates at the downstream RO. This study therefore provides a fundamental insight into the into RO fouling propensity of MBR filtrate: namely, colloidal particles in the size range of 1 µm and above are an important contributor to fouling propensity when they are not removed by pre-RO treatment. Overall, these results demonstrated the multiple benefits of a novel MBR-CF pre-RO treatment train.

  • A novel MBR-CF-RO treatment train was demonstrated at the full scale.

  • This system achieved multiple benefits for pathogen credit and fouling control.

  • Cartridge filter challenge testing demonstrated the detectability of breaches with online dP.

  • Stages 1–2 RO fouling rates were remarkably low, with 5–7-month CIP intervals.

  • Colloidal particles in the size range of 1 μm contribute to fouling propensity of MBR filtrate.

Increasing demands on water supplies combined with drought conditions and urbanization have led many water agencies to consider direct or indirect potable reuse (DPR or IPR) (NRC 2012; US EPA 2019). In California, IPR regulations require reverse osmosis (RO) and advanced oxidation, e.g., an ultraviolet light/advanced oxidation process (UV/AOP), to be part of the treatment train (Gerrity et al. 2013; Pecson et al. 2018a). These two treatment processes, plus the travel time for groundwater recharge or dilution requirements of reservoir water augmentation, meet the strict regulatory requirements for the removal of pathogens, salts, and trace organic contaminants (Pecson et al. 2018a, b).

RO for potable reuse applications requires pretreatment to reduce the impact of fouling and scaling (Jiang et al. 2017; Hoek et al. 2022; Liu et al. 2024). Fouling simultaneously reduces the productivity and performance of RO and increases the costs due to energy and maintenance (Jiang et al. 2017; Hoek et al. 2022; Nthunya et al. 2022). The causes of fouling and scaling can differ and may include inorganic scaling, biofilm growth, colloidal fouling, organic fouling, or a combination of inorganic and biological fouling (Li et al. 2007; Kimura et al. 2016; Hoek et al. 2022). The typical approach to RO pretreatment for potable reuse applications has been micro- or ultrafiltration (MF/UF) of secondary or tertiary wastewater (Gerrity et al. 2013; Tang et al. 2018). However, this approach of simple MF/UF-RO is not suitable for some applications, e.g., non-nitrifying wastewater facilities where additional nitrogen management is required; or decentralized potable reuse via ‘scalping plants’ where biological wastewater treatment must be part of the advanced treatment facility. In addition to providing a barrier against fouling, pretreatment can also provide additional pathogen removal credit (Adelman et al. 2024). MF/UF-based pretreatment trains already have well-established pathogen credits, but the use of an alternative train needs to achieve comparable levels of pathogen removal.

Interest is therefore growing in alternative pre-RO treatment processes for certain potable reuse applications. For example, ozone combined with biological activated carbon (ozone–BAC) has been the subject of recent research showing the potential for realizing multiple benefits from alternative pre-RO treatment processes (Trussell & Pisarenko 2024), and further such research on other novel process trains would be beneficial. Membrane bioreactors (MBRs) have recently been explored as direct pretreatment options for RO. The MBR process includes a suspended growth bioreactor followed by solids separation using membranes, with opening size generally in the MF/UF range but operated under high-solid conditions with a surface cake layer. MBRs are advantageous over conventional wastewater treatment due to their reduced footprint, increased solids inventory and cell residence time, greater ability to nitrify/denitrify, and improved solids separation (Comerton et al. 2005; Le-Clech 2010; Zanetti et al. 2010). MBRs are of particular interest in instances where an existing wastewater facility must be retrofitted to manage nitrogen, or where a new biological process must be implemented in a decentralized facility. The membrane separation step in the MBR process removes suspended solids and pathogens by a combination of size exclusion and capture by the cake layer (Chaudhry et al. 2015), so MBR is theoretically at least as effective of a pathogen barrier as MF/UF. If MBR is to be used in a potable reuse system, it would be desirable to use direct MBR-RO treatment and avoid the cost of including additional MF/UF downstream. Currently, there is conflicting evidence on the impact of pretreatment by MBR on RO fouling. Qin et al. (2006) found that MBR-RO was more effective than MF/UF-RO in a pilot-scale system. However, more recent experience has shown that MF/UF pretreatment tends to be more effective in practice, while MBR-RO plants appear to be prone to higher rates of fouling and shorter clean-in-place (CIP) intervals than conventional MF/UF trains (WRF 2023). Additional data are required to better understand the impact of MBR on RO fouling.

While research has primarily focused on the efficacy of various other pretreatment processes, RO systems for potable reuse also include cartridge filters (CFs). CFs are most commonly added to satisfy warranty requirements for the RO membranes, but few studies have explicitly characterized their impacts on RO performance and pathogen removal. CFs used for potable reuse RO systems typically have a nominal pore size rating of around 5 μm, and they are typically monitored only by periodic grab samples for silt density index which likely produces data of insufficient frequency to optimize fouling control and pathogen removal. Hoek et al. (2022) evaluated the performance of typical 5 μm nominally-rated CFs at a brackish groundwater RO plant and found that they increased RO fouling due to the growth and sloughing of biofilms. There is little literature overall on CFs as a unit process in a pre-RO train, and the literature does not elucidate their capability for removal efficiency and fouling control.

In this work, the impact of both MBR and absolute-rated 1 μm CFs was evaluated on the fouling of RO and the achievement of pathogen log reduction value (LRV) credit. This site, located in California, had a novel treatment train configuration consisting of MBR, CF, RO, UV/AOP, and chlorine disinfection followed by post-stabilization. The objective of this study was to evaluate pathogen barrier monitoring and RO fouling control by an MBR process along with an absolute-rated 1 μm CF system at a full-scale advanced treatment plant. If this novel pre-RO alternative treatment train can demonstrate multiple benefits in these areas, there will be many other potential applications for it in advanced treatment facilities.

Treatment plant and process descriptions

This study is focused on the Sustainable Water Infrastructure Project (SWIP) implemented by the City of Santa Monica. The SWIP advanced water treatment facility (AWTF) is a scalping facility that takes approximately 10% of the City's average municipal wastewater supply to produce a maximum of 1.2 million gallons per day (MGD) or 4,540 m3/d of advanced-treated recycled water. The AWTF can accept local stormwater runoff as up to 30% of its influent volume, but during this study period, no stormwater flow was recorded. The advanced-treated recycled water is used for non-potable applications (e.g., irrigation, toilet flushing, cooling towers) by private and public customers, and it will be used for IPR through direct injection of advanced-treated recycled water for groundwater augmentation beginning in spring 2025.

The AWTF is comprised of multiple processes including MBR, CF, RO, UV/AOP, and free chlorine disinfection (Figure 1), followed by post-stabilization of the product water before distribution for end use. The AWTF is designed to treat the City's wastewater to California Code of Regulations Title 22 indirect potable reuse standards and meets all permit requirements set by the California State Water Resources Control Board (SWRCB) Division of Drinking Water (DDW) for groundwater augmentation via direct subsurface injection. The multi-barrier treatment process must achieve the state pathogen LRV requirements of 12-log virus, 10-log Cryptosporidium, 10-log Giardia, while also meeting maximum contaminant levels (MCLs) for drinking water. The SWIP AWTF is the first permitted potable reuse facility whose membrane bioreactor and cartridge filtration processes achieved pathogen log reduction credits in the state of California.
Figure 1

Process flow diagram.

Figure 1

Process flow diagram.

Close modal

There are two MBR trains, each consisting of an anoxic basin, an aerobic basin, and membrane separation. The MBR is intended to achieve carbonaceous biochemical oxygen demand (BOD) removal, full nitrification with partial denitrification, and solids separation. The biological basins operate with 4,000–9,000 mg/L mixed liquor suspended solids (MLSS); a 2 mg/L dissolved oxygen (DO) target in the aerobic zone; and a cell residence time of 1–2 weeks to provide adequate conditions for nitrifiers. The MBR membranes are Suez ZeeWeed 500d-422 membranes with two 52-module cassettes per train. These membranes are ultrafiltration-rated with 0.04 μm nominal pore size, and they operate with 12 min production cycles at gross flux between 7.8 and 17.6 gfd (13–30 LMH) followed by 0.5 min relaxation cycles to control cake formation. Return activated sludge (RAS) flows back to the anoxic basin, with a recycle ratio of 4–5 times the plant flow. Waste activated sludge (WAS) is wasted to control MLSS in the MBR system.

There are two CF-RO trains as part of the process. CFs are provided upstream of RO to protect the membranes from damage by passively filtering MBR effluent. Although conventional RO systems generally employ CFs with nominal ratings around 5 μm to satisfy warranty requirements, the SWIP AWTF selected finer absolute-rated 1 μm cartridges that sought to achieve pathogen removal credit. These CFs are Harmsco HC/170-LT2 filters made of pleated microfiber media. Supplier validation results based on the US EPA Long-Term 2 Enhanced Surface Water Treatment Rule have shown >3.6 log removal of 2 μm Cryptosporidium surrogate beads up to 30 psi (2.1 bar) pressure drop and up to 100 gpm (379 L/min) flow per 125 ft2 (11.6 m2) cartridge, equivalent to 1,152 gfd or 0.8 gal/ft2-min (1,956 LMH) gross flux. Based on this validation testing, these cartridges achieved the US EPA and the National Sanitation Foundation certification for the removal of cyst-sized particles, and they are accepted by DDW for potential pathogen reduction credits. The CF system included two redundant vessels per train operated in a duty/standby configuration, with five cartridges per vessel to remain below the maximum validated flow rate.

The two RO trains, provided by H2O Innovation, are designed as a three-stage process with a 14:6:3 array of 6 L vessels and an intermediate booster at Stage 2. The membranes are Toray TMG20D-400 polyamide brackish water elements with 400 ft2 (37 m2) area each. The RO trains were operated from 9 to 11 gfd (15–19 LMH) overall permeate flux and 78–85% recovery. The RO system removes dissolved inorganic and organic components in the CF filtrate, as well as pathogens. To control scaling and to protect the RO membranes, feed conditioning included antiscalant dosing, pH reduction with sulfuric acid, and chloramine addition.

Operating conditions and experimental approach

The demonstration of the performance of the MBR-CF-RO systems over a full range of operating conditions was performed during a continuous 14-day acceptance test per DDW requirements between 28 March and 10 April 2023. AWTF influent wastewater quality during this evaluation period is summarized in Table 1. This acceptance test phase was followed by a continued commissioning operational phase of the plant for the remainder of 2023. The AWTF was fed with 100% wastewater at an average daily influent flow rate of 1.29 MGD (4,880 m3/day), and the wastewater remained similar to the quality shown in Table 1 although the total dissolved solids ranged as high as 1,200 mg/L during some periods. The effluent water was monitored for compliance with standards for advanced-treated recycled water requirement standards and for potable reuse by groundwater augmentation via subsurface application (State Board 2015).

Table 1

Influent water quality during the evaluation period

ParameterUnitAverageRangeN
BOD mg/L 190 170–210 
Total Kjeldahl nitrogen mg/L as N 42 41–43 
Total suspended solids mg/L 273 220–330 
Turbidity NTU 65 37–120 
Total organic carbon mg/L 43 39–49 
Total dissolved solids mg/L 753 730–790 
ParameterUnitAverageRangeN
BOD mg/L 190 170–210 
Total Kjeldahl nitrogen mg/L as N 42 41–43 
Total suspended solids mg/L 273 220–330 
Turbidity NTU 65 37–120 
Total organic carbon mg/L 43 39–49 
Total dissolved solids mg/L 753 730–790 

CF challenge testing was also performed to verify that online instrumentation could detect breaches of the CFs. The challenge tests were conducted consecutively for 48 h during April 2023, and the filters were damaged by drilling a single ½ inch (12.7 mm) diameter hole at the center of one filter element in each train (Figure 2). The size of the hole was coordinated with DDW to represent a real-world scenario of the filters being damaged by debris such as hardware. The online differential pressure (dP) trends from the control system were recorded before and after damaging the CFs to identify patterns associated with loss of integrity.
Figure 2

CF challenge testing approach – ½ inch (12.7 mm) diameter holes drilled, one per train.

Figure 2

CF challenge testing approach – ½ inch (12.7 mm) diameter holes drilled, one per train.

Close modal

Key critical control points (CCPs) for the MBR-CF-RO process are presented in Table 2, and achievement of LRV credits for pathogen removal was determined by comparison of online data to the CCPs. Each process was intended to achieve LRV credits as shown in Table 2 to meet the minimum 12-log virus, 10-log Cryptosporidium, and 10-log Giardia goals for the AWTF overall. For MBR, filtrate turbidity is the key monitoring parameter for LRV credits, based on the Tier 1 process for MBR validation (WRF 2021). Other performance parameters such as pH, DO, oxidation–reduction potential (ORP), temperature, solids retention time (SRT), hydraulic retention time (HRT), MLSS concentration, transmembrane pressure (TMP), and membrane flux are also utilized as performance indicators for the biological treatment and membrane separation processes. CF monitoring is focused on CCPs for flow rate, filter flux, dP, and filtrate turbidity to stay within the range of the supplier's validation testing.

Table 2

Key process parameter limits for MBR-CF-RO to achieve LRV credits

ProcessLRV credit goal
(virus/Crypto/Giardia)
CCPs to achieve pathogen LRV credits
MBR 1/2.5/2.5 Turbidity <0.2 NTU (95th percentile)
Instantaneous max turbidity <0.5 NTU 
CF 0/2/2.5 Max differential pressure <30 psi (2.1 bar)
Turbidity <0.3 NTU (95th percentile)
Instantaneous max turbidity <1 NTU
Max flow rate per train <500 gpm (1,893 L/min)
Max filtrate flux per train <1,956 LMH
Confirm via challenge testing that dP will detect breaches 
RO 1.5/1.5/1.5 1.5 log reduction of total organic carbon
Alternate: 1.0 log reduction of conductivity for LRV = 1 
ProcessLRV credit goal
(virus/Crypto/Giardia)
CCPs to achieve pathogen LRV credits
MBR 1/2.5/2.5 Turbidity <0.2 NTU (95th percentile)
Instantaneous max turbidity <0.5 NTU 
CF 0/2/2.5 Max differential pressure <30 psi (2.1 bar)
Turbidity <0.3 NTU (95th percentile)
Instantaneous max turbidity <1 NTU
Max flow rate per train <500 gpm (1,893 L/min)
Max filtrate flux per train <1,956 LMH
Confirm via challenge testing that dP will detect breaches 
RO 1.5/1.5/1.5 1.5 log reduction of total organic carbon
Alternate: 1.0 log reduction of conductivity for LRV = 1 

Data acquisition, sampling, and analysis

During the 14-day acceptance test, multiple data sets were collected as shown in Table 3, including continuous monitoring of online process instrumentation, manual data collection through laboratory testing of grab samples, and data reported via the control system. All water quality sampling and performance parameters were sampled, logged, and measured on Days 1, 7, and 14 as a minimum during the 14-day evaluation period. Reported averages were used to determine performance compliance for non-continuous parameters.

Table 3

Monitoring parameters by the process

ProcessParameter
MBR 
  • Filtrate turbidity (individual train and combined)

  • Filtrate pressure

  • MBR tank and filtrate tank water levels

  • Air scour blower speed, run time, and discharge pressure

  • Influent ammonia, total Kjeldahl nitrogen, and BOD

  • Combined filtrate BOD, ammonia, nitrite, nitrate, and chloramines

  • Biological tank MLSS, pH, DO, ORP, and temperature

  • Calculated TMP membrane flux, SRT, and HRT

 
CF 
  • Pressure upstream and downstream of CF

  • Turbidity downstream of CF

  • Flow upstream of CF

  • Calculated dP and flux rates through CF

 
RO 
  • Flow rates and pump speeds

  • Feed and concentrate pressure at each stage

  • Common permeate pressure

  • Feed, permeate, and concentrate conductivity

  • Feed and permeate total organic carbon

  • Antiscalant, sulfuric acid, and chloramine dosing rates

  • Feed pH, ORP, temperature, conductivity, and turbidity

  • Permeate pH and conductivity

  • Calculated recovery, dP, and normalized specific flux

 
ProcessParameter
MBR 
  • Filtrate turbidity (individual train and combined)

  • Filtrate pressure

  • MBR tank and filtrate tank water levels

  • Air scour blower speed, run time, and discharge pressure

  • Influent ammonia, total Kjeldahl nitrogen, and BOD

  • Combined filtrate BOD, ammonia, nitrite, nitrate, and chloramines

  • Biological tank MLSS, pH, DO, ORP, and temperature

  • Calculated TMP membrane flux, SRT, and HRT

 
CF 
  • Pressure upstream and downstream of CF

  • Turbidity downstream of CF

  • Flow upstream of CF

  • Calculated dP and flux rates through CF

 
RO 
  • Flow rates and pump speeds

  • Feed and concentrate pressure at each stage

  • Common permeate pressure

  • Feed, permeate, and concentrate conductivity

  • Feed and permeate total organic carbon

  • Antiscalant, sulfuric acid, and chloramine dosing rates

  • Feed pH, ORP, temperature, conductivity, and turbidity

  • Permeate pH and conductivity

  • Calculated recovery, dP, and normalized specific flux

 

For the MBR and CF processes, online data values in Table 3 were recorded and analyzed by the control system historian at 1-min resolution. In addition, two derived parameters were calculated from the raw online CF data for clearer identification of breaches. The first was a temperature-corrected specific flux (TCSF), as shown in Equation (1):
(1)
where QCF is the flow through the cartridge filter vessel, ACF is the total cartridge filter area, dP is the online differential pressure, and TCF20 is the temperature correction factor to 20 °C, taken as the ratio of the viscosity of water at the measured online temperature to the viscosity at 20 °C. TCSF is the flux through the cartridge per unit pressure input adjusted for temperature, and it expresses the underlying permeability of the cartridges (calculated in a manner comparable to TCSF values used for MF/UF or MBR membranes). During the normal operation of a CF system, the TCSF would be expected to gradually decline from the installation of new cartridges to changeout, reflecting the accumulation of solids. A cartridge breach that leads to a sudden increase in permeability would be reflected as a sudden increase in TCSF. The second parameter was a normalized dP, as shown in Equation (2):
(2)

This dP is normalized to the temperature-corrected flux squared, reflecting the expected minor loss relationship between headloss across a CF system and the interstitial velocity at the cartridge openings. During the normal operation of a CF system, this normalized dP would be expected to increase in a roughly linear fashion as solids accumulation blocks some of the open area and increases interstitial velocity.

For the RO process, the online data was used to calculate normalized specific flux at each stage, according to Equation (3):
(3)
where JStage is the gross permeate flux at a given stage, NDPStage is the net driving pressure at that stage, and TCF25 is the temperature correction factor to 25 °C, taken as the ratio of the viscosity of water at the measured online temperature to the viscosity at 25 °C.
The net driving pressure at each stage was calculated by correcting the average pressure on the feed-concentrate side for osmotic pressure and permeate backpressure, as shown in Equation (4):
(4)
where PFeed is the measured pressure at the feed end of the stage, PConc is the measured pressure at the concentrate end of the stage, PPerm is the measured permeate backpressure, and POsmotic is the estimated osmotic pressure based on the online conductivity.

This normalization process accounts for the variation in specific permeate flux over time as a result of changes in water temperature and feed salinity; as well as variation in specific flux between stages as a result of increasing feed-concentrate osmotic pressure along the elements in series. By accounting for these conditions, the normalized specific flux is essentially a measure of the underlying permeability of the membrane elements, and changes in its value over time reflect the progression of fouling and scaling.

For all treatment processes, a two-sample unequal variance t-test was used to determine whether there was a statistically significant difference between Train 1 and Train 2 at the AWTF. The resulting p-values less than 0.05 were considered significant. In general, it was hypothesized that similar operating conditions and similar performance should be observed between the two identical trains, and comparing these two duplicate trains increases the robustness of the findings.

MBR system performance

The biological treatment system is targeted to reduce nutrient loads through nitrification and denitrification while maintaining MLSS concentration within the membrane permissible limits. Through the duration of the study, the MLSS concentration was maintained above 5,000 and below 10,000 mg/L via sludge wasting, as shown in Figure 3(a). A manual wasting schedule was undertaken during this evaluation period to maintain the MLSS within this range. This manual control of wasting likely accounts for the statistically significant difference between the MLSS for the two trains (p = 0.03). The biomass inventory was consistently somewhat higher at Train 1, which was observed in both online and grab sample data. The nitrogen load in the MBR influent was generally in the form of non-oxidized total Kjeldahl nitrogen ranging from 20 to 40 mg/L as N, while the MBR filtrate concentrations steadily maintained around 10 mg/L as N in the nitrate form throughout the testing period, as shown in Figure 3(b). The ammonia and nitrite concentrations were consistently <0.04 mg/L as N after MBR, validating the continual occurrence of full nitrification and partial denitrification in the reactors.
Figure 3

Bioreactor performance across a 2-week period, including (a) MLSS and (b) nitrification–denitrification.

Figure 3

Bioreactor performance across a 2-week period, including (a) MLSS and (b) nitrification–denitrification.

Close modal
The effect of operating parameters such as TMP and flow rate was evaluated across multiple time scales to check the turbidity performance. The online data are provided over the course of five production cycles (with a duration of approximately 1 h) in Figure 4, and in the form of hourly averages over the 2-week evaluation period in Figure 5. Note that the TMP value is negative as expected, because the filtrate pump draws flow through the submerged membranes and the hydraulic head on the filtrate side of the membranes is therefore lower than the water level in the membrane tank. Figure 4 shows that the relaxation cycles resulted in corresponding reductions in the absolute value of the TMP, as the slowing down of the flow through the membranes during this relaxation cycle also manifested as reduced suction pressure across the membranes. There was no statistically significant difference in the filtrate flow between the two MBR trains (p = 0.09), but the TMP at MBR 1 was consistently slightly higher (p= 0.00), which likely reflects the higher biomass inventory at Train 1. There was also a statistically significant difference between the filtrate turbidities of the two trains (p = 0.01) although this likely reflects the fact that for very low turbidity values, the inherent variability of the turbidimeter increases relative to the absolute value of the measurement. In any case, the average turbidity was similar between the two trains, and the turbidity performance was maintained well below 0.2 NTU both cycle-to-cycle and in long-term operation. A statistical analysis of the measured online values of the CCPs throughout the evaluation period is shown in Table 4 for the two MBR trains. The system remained well within all CCP limits and therefore achieved the pathogen LRV credit goals in Table 2.
Table 4

Online MBR CCP values during the evaluation period

Turbidity (NTU)
Instantaneous flux (LMH)
MBR 1MBR 2CombinedMBR 1MBR 2
Minimum 0.04 0.04 0.04 13.4 13.3 
Median 0.04 0.05 0.07 29.1 29.1 
Mean 0.05 0.05 0.06 26.5 26.2 
95th Percentile 0.12 0.08 0.08 29.5 29.5 
Maximum 0.12 0.10 0.08 29.8 29.8 
Turbidity (NTU)
Instantaneous flux (LMH)
MBR 1MBR 2CombinedMBR 1MBR 2
Minimum 0.04 0.04 0.04 13.4 13.3 
Median 0.04 0.05 0.07 29.1 29.1 
Mean 0.05 0.05 0.06 26.5 26.2 
95th Percentile 0.12 0.08 0.08 29.5 29.5 
Maximum 0.12 0.10 0.08 29.8 29.8 
Figure 4

Typical MBR performance from online datalogs across five production cycles, including (a) flow and turbidity and (b) TMP.

Figure 4

Typical MBR performance from online datalogs across five production cycles, including (a) flow and turbidity and (b) TMP.

Close modal
Figure 5

MBR performance, including (a) turbidity, (b) flow, and (c) TMP, across 2 weeks.

Figure 5

MBR performance, including (a) turbidity, (b) flow, and (c) TMP, across 2 weeks.

Close modal

Cartridge filter performance and online breach analysis

Trends in the online data for the CF system are shown in Figure 6 for normal operation across one changeout cycle for a set of cartridges, lasting approximately 2 weeks. The flow to each train was not significantly different (p = 0.20) until the last 4 days of the cycle when the flow per train varied by about 5 gpm (19 L/min). The dP across the CFs increased from about 9 psi (0.6 bar) to 18–20 psi (1.2–1.4 bar) in this interval, with a non-linear pattern of increase over time consistent with the capture of colloidal particles by surface removal (Crittenden et al. 2023). This pattern of increase shows that colloidal particles in the size range of 1 μm and above are present in the MBR filtrate despite its low turbidity, and the CFs captured them to at least some extent. A statistically significant difference did emerge between the two trains (p = 0.01) in the accumulated differential pressure, even as the pattern of increase was similar. The effluent turbidity from each system stayed fairly consistent over the changeout cycle, in the 0.02–0.1 NTU range. Online monitoring values of the CCPs throughout the evaluation period are shown in Table 5, confirming that the system remained within the CCP limits in Table 2. Note that because dP increased with solids accumulation throughout the changeout cycle, the maximum dP reported in the table simply reflects the end of the cycle.
Table 5

Online CF CCP values during the evaluation period

dP (bar)
Flow (L/min)
Turbidity (NTU)
CF 1CF 2CF 1CF 2CF 1CF 2
Minimum 0.5 0.5 491 491 0.02 0.09 
Median 0.9 0.9 1,858 1,858 0.02 0.10 
Mean 1.0 0.9 1,858 1,856 0.02 0.10 
95th Percentile 1.3 1.2 1,864 1,862 0.03 0.10 
Maximum 1.4 1.3 1,942 1,939 0.26 0.94 
dP (bar)
Flow (L/min)
Turbidity (NTU)
CF 1CF 2CF 1CF 2CF 1CF 2
Minimum 0.5 0.5 491 491 0.02 0.09 
Median 0.9 0.9 1,858 1,858 0.02 0.10 
Mean 1.0 0.9 1,858 1,856 0.02 0.10 
95th Percentile 1.3 1.2 1,864 1,862 0.03 0.10 
Maximum 1.4 1.3 1,942 1,939 0.26 0.94 
Figure 6

Cartridge filter performance from online datalogs across one changeout cycle, including (a) differential pressure and turbidity and (b) flow rate.

Figure 6

Cartridge filter performance from online datalogs across one changeout cycle, including (a) differential pressure and turbidity and (b) flow rate.

Close modal

Confirmation of stable online performance and compliance with the CCPs partially satisfied the requirements in Table 2 to achieve LRV credit. To address the final requirement, the cartridge filter online performance was also observed during two intentional breach experiments. The online dP is the directly measured parameter that is expected to respond most quickly to a breach. However, dP across a cartridge filter will also vary as a result of changing flow through the cartridges, changing water temperature and viscosity, and the general progression of solids accumulation as shown in Figure 6. Therefore, to illustrate a potentially more robust way to maintain pathogen monitoring, the calculated parameters of TCSF per Equation (1) and normalized dP per Equation (2) were also plotted.

The response of the CF to breach testing is shown in Figure 7, which includes both the raw online dP trend along with these two calculated parameters. Data are provided for the two separate intentional breach events on separate cartridges, alongside a period with no breach for comparison. The non-breach data showed the expected trends in both the TCSF and normalized dP. Both breaches were detectable as a small sudden decrease in raw dP, and they were easily distinguished as sudden sharp increases in TCSF against a decreasing trend or sudden sharp decreases in normalized dP against an increasing trend. This response pattern was consistent between partially fouled CFs that had been in service over 2 weeks, as shown in Figure 2(b), and with new filters, as shown in Figure 2(c). The underlying patterns of the raw dP and the normalized trends, as well as the observed responses to breach conditions, were therefore similar across the full changeout cycle.
Figure 7

Cartridge filter challenge testing data, including raw dP along with calculated TCSF and normalized dP, for (a) a 3-day period with no breach for comparison, (b) first breach, and (c) second breach.

Figure 7

Cartridge filter challenge testing data, including raw dP along with calculated TCSF and normalized dP, for (a) a 3-day period with no breach for comparison, (b) first breach, and (c) second breach.

Close modal

RO fouling performance

The two RO trains were monitored for progression of fouling. The normalized specific flux at Stages 1 and 2 of each RO train are shown in Figure 8 from the beginning of the 14-day experiment through the first CIP cycle approximately seven months later. While Stage 3 would potentially be subject to mineral scaling (which was observed under some feed pH conditions), the colloidal, organic, and biological fouling that would be a concern with MBR filtrate would be expected to affect Stages 1 and 2. There was some variation between stages in the gross permeate flux as the RO trains operated under different total flow and recovery conditions, and as the Stage 2 membranes were subjected to higher feed-concentrate osmotic pressure compared to Stage 1. However, the normalized specific flux started at a very similar value at each stage. This is expected because the same membrane elements were used in each stage, and they should therefore have similar permeate production per unit area and per unit driving pressure once corrected for stage-to-stage variations in feed-concentrate quality. The confirmation of this showed that the normalization approach was effective.
Figure 8

Normalized RO flux over time at (a) Train 1 and (b) Train 2.

Figure 8

Normalized RO flux over time at (a) Train 1 and (b) Train 2.

Close modal

As expected, the fouling observed in Figure 8 was characteristic of non-mineral fouling. Mineral scaling leads to the rapid decline of normalized flux over the time scale of days, while non-mineral fouling leads to a more gradual decline over the time scale of days to weeks (Crittenden et al. 2023). A gradual decline in normalized flux indicates some form of organic fouling – through some combination of dissolved particles, colloidal particles, and biofilm growth – that apparently affected the feed-concentrate side of both stages. Both the actual permeability and the rates of fouling were similar between the two RO trains as no statistically significant difference was found between the two trains for the normalized flux values at Stage 1 (p = 0.33) or Stage 2 (p = 0.28). Importantly, similar rates of fouling were also observed between Stages 1 and 2 of each train. This is notable because although fouling of multiple stages by dissolved organics is a familiar observation, colloidal fouling is conventionally thought to mainly impact Stage 1 (Hydranautics 2014). The observed fouling was also observed to be reversible, as the normalized specific flux was restored to between 96 and 100% of its initial value in the evaluation period following CIP.

Evaluation of the novel pretreatment approach

The MBR system performance (Table 4) remained within the required envelope for turbidity and gross flux and achieved 1 LRV credit for viruses and 2.5 LRVs for Giardia and Cryptosporidium. Online CF turbidity, flow, and dP (Table 5) also remained within the validated envelope for the absolute-rated cartridges which allowed the CF system to achieve 2.0 LRV credit for Cryptosporidium and 2.5 LRVs for Giardia. The CF system challenge testing demonstrated that breaches could be detected based on the monitoring of online dP. This AWTF is the first in California to achieve regulatory approval for such credits, and the contribution of both MBR and CFs to the overall LRVs is an important benefit of this pre-RO treatment alternative. This benefit has economic value as well – if MBR and CFs can achieve pathogen LRV credit, then the capital and operating cost of adding MF/UF can be avoided in facilities that select MBR-based biological treatment.

The fouling performance of the RO system downstream of the MBR-CF pretreatment was also remarkable. Figure 9 shows a comparison of measured fouling rates from this study against comparable potable reuse demonstration plants, including both an MBR-RO plant (WRF 2023) and a non-MBR plant including conventional MF/UF both with and without ozone–BAC (Pearce et al. 2015). In these comparable facilities, the MBR filtrate produced greater rates of fouling, consistent with current industry experience. The observed fouling rates in this study were much closer to the non-MBR facility, consistent with CIP intervals around 5–7 months and occasionally as low as the treatment train that included ozonation. The other comparable MBR-RO facility had much higher fouling rates consistent with 2–3-month CIP intervals. The achievement of much lower RO fouling rates in this study, along with the observation of dP increase at the CF system over the time scale of days, suggest that the CF step was effectively capturing at least some of the colloidal particle load in the range of 1 μm and above which would otherwise drive fouling propensity in MBR filtrate. The combined MBR-CF process appears to have performed significantly better as RO feed than MBR alone. This combined pretreatment process has multiple effective barriers to colloidal particles, operating by multiple mechanisms: the CF barrier is exclusively a size exclusion process while the MBR membranes combine size exclusion with depth removal in the cake layer.
Figure 9

RO fouling rates vs. comparable reuse facilities.

Figure 9

RO fouling rates vs. comparable reuse facilities.

Close modal

Monitoring off-spec conditions and future optimization

One key operational consideration for SWIP AWTF – like any potable reuse facility – is the detection and management of off-spec conditions. The definition of CCPs at each process must be accompanied by online monitoring at sufficiently short time scales to detect deviations in process performance; along with provisions to divert off-spec water (Figure 10). Although the current monitoring practices at the AWTF proved adequate for the LRV credits achieved here, it may be possible to improve the sensitivity of monitoring (and therefore the LRV credits granted) for both barriers in this study. Further research to validate the relationship between MBR pathogen removal and online turbidity, TMP, or other parameters may allow this process to achieve LRV credits commensurate with the higher values measured in bench and pilot studies (Adelman et al. 2024). Similarly, for CF systems, the demonstration of alarm logic algorithms based on the derived parameters shown in this study combined with additional challenge testing could validate higher LRV credits as well, or at least contribute to the more widespread use of appropriate CFs to achieve LRV credit. This type of analysis is especially relevant for CFs following a membrane filtration process.
Figure 10

CCPs and out-of-compliance (OOC) diversion points at SWIP AWTF.

Figure 10

CCPs and out-of-compliance (OOC) diversion points at SWIP AWTF.

Close modal

The mitigation of RO fouling in MBR-based treatment systems is also a topic of interest for further optimization and research. The results at this facility suggest that absolute-rated cartridges could be tested at other advanced treatment plants struggling with colloidal fouling, as control of the colloidal particle load in the size range of 1 μm and above led the MBR filtrate in this study to have lower fouling propensity compared with other MBR-RO facilities. Varying cartridge pore size rating and MBR biological process conditions could provide further insight into the relative contributions of the dissolved and colloidal organic particle load that drives RO fouling and elucidate the conditions that best control it.

In this study, an MBR system followed by an absolute-rated 1 μm CF system was demonstrated at a full-scale advanced treatment plant as the pretreatment processes for RO. This advanced treatment plant included two identical treatment trains, which provide additional robustness to the study results. The MBR system achieved effective biological treatment and low filtrate turbidity, and the CFs stayed within their flow, dP, and turbidity CCP limits. Breach testing of the CFs demonstrated a consistent response pattern in flux and dP data. Novel TCSF and normalized dP parameters were shown to provide easier detection of breach conditions using the online dP data, and the breach response patterns were consistent across the CF change-out cycle. These observed breach response patterns and the novel derived parameters are a useful contribution to the online monitoring of CFs as a pathogen barrier.

The combined pretreatment processes of MBR and CFs also achieved remarkably low non-mineral fouling rates at the downstream RO system. These observed fouling rates were lower than reported for another comparable MBR-RO treatment train and on par with non-MBR reuse treatment trains, and the overall normalized flux decline was consistent with 5–7-month CIP intervals. Regularly achieving such CIP intervals with MBR filtrate would be of great benefit for RO operability. The observation of a significant increase in dP over a 2-week time scale showed that the CFs were capturing colloidal particles even from low turbidity influent, and this resulted in a lower-than-expected fouling rate at the downstream RO. This study, therefore, provides a fundamental insight into the nature of the particle load that drives RO fouling when treating MBR filtrate: namely, colloidal particles in the size range of 1 μm and above are an important contributor to fouling propensity when they are not removed by pre-RO treatment.

The findings of this study allowed the Santa Monica SWIP AWTF facility to achieve the first permitted pathogen log removal credit for both MBR and CFs in California by online confirmation of compliance with CCPs alongside the CF challenge testing results. Achieved pathogen LRV credit included 1-log for virus and 2.5-log each for Cryptosporidium and Giardia across MBR, as well as 2-log for Cryptosporidium and 2.5-log for Giardia across the CFs. The results discussed herein will be of interest for utilities in any location considering RO-based treatment trains and how to effectively acquire LRV credits for MBR and CF pretreatment processes. For many potable reuse applications, particularly those where MBR is desirable for nitrogen management or decentralized treatment, the multiple benefits of pathogen removal credit and fouling control make MBR-CF-RO a potentially attractive alternative.

The authors thank the many people at Kiewit, Arcadis, PACE, PERC Water, Vertech, Stantec, and the City of Santa Monica who have been part of the Sustainable Water Infrastructure Project. Special thanks to Tyler Hadacek, Andrew Devries, Yamrot Amha, Anna Philipp, Michael Scott, Jim Borchardt, Breanne Padilla, Eric Gonzales, Gilbert Perez, Mario Meza, and Terrell Thompson.

The data analysis and writing of this paper was funded by the City of Santa Monica and the Stantec Institute for Water Technology and Policy. The Sustainable Water Infrastructure Project was financed by the City of Santa Monica. Funding assistance for this project came from the California State Water Resources Control Board Clean Water State Revolving Fund, along with California Prop 1 Stormwater, Los Angeles County Measure W, and Metropolitan Water District of Southern California Local Resources Program grant funds.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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