The document summarizes an analysis of reverse osmosis (RO) membrane fouling at a water purification plant. Researchers developed models to correlate fouling with water quality parameters and better understand the fouling process. Their models explained more variability in fouling than baseline models. Key findings included negative correlations between fouling and turbidity (consistent with particle deposition) and positive correlations with biofilm nutrient levels. The models provided new insights into how different treatment stages and cleaning procedures impact fouling.

1. Modeling RO Fouling in OCWD’s Groundwater Replenishment System
John Cook, Ruby Daamen, and Ed Roehl - Advanced Data Mining International  www.advdmi.com
PROBLEM
• Orange County Water District (OCWD) has operated the Groundwater Replenishment System
(GWRS, Figure 1) since 2008. They want to improve the efficiency of the GWRS’ reverse osmosis
system (RO) to reduce operating costs.
• The RO has 45 membrane filters in 15 parallel 3-stage units. Each unit can produce 5 MGD at 85%
recovery. Each filter separates a feed flow into a cleaner, less saline permeate flow (qperm) and a
dirtier, more saline concentrate flow.
• Specific flux (sflux) is a calculated measure of RO performance, and is qperm after normalizing for
membrane area, drive pressure, and temperature. As shown in Figure 2, the sflux of a clean filter
declines rapidly during a short Phase 1 period, followed by a Phase 2 period of gradual decline that lasts
months. Phase 1 fouling is caused largely by particle deposition and Phase 2 fouling is caused largely
by biofilm and inorganic scale growth.
• A run is ended when a stage’s net sflux becomes unacceptably low. Its filters are then chemically
cleaned to restore permeability. Multiple stages are usually cleaned at the same time.
• OCWD seeks to reduce fouling to increase run times, and wanted to develop a correlation model of the
fouling process from its 7-year process database. This pilot project focused on Stage 1.
APPROACH
• 42 parameters (Table 1) having daily, once and twice per week records collected from the RO and
T1T4 monitoring sites upstream (Figure 1) were selected for analysis. The data were noisy due to
being collected using multiple, uncoordinated sampling schemes. They needed to be interpolated to
estimate missing values.
• Because sflux is not calculated from water quality parameters, the modeling deliberately performed
partial accountings of sflux variability caused by changing water quality parameter concentrations and
qperm rates.
APPROACH  continued
• Phase 1 and 2 baseline models were created with qperm inputs, but not water quality inputs, to establish baseline values of determination coefficients
(R2) to which the R2 of models having water quality inputs could be compared. The Phase 2 model used an additional input, sflux.A3 time-lagged 20 days,
to indicate the flux trend prior to the sflux.A20 calculation window.
• Prototype models having baseline inputs and combinations of water quality inputs were used to systematically determine which water quality parameters
were the best predictors of fouling according to R2 and sensitivity analyses. A sensitivity analysis indicates if an output has a positive (+) or negative (-)
sensitivity (correlation) to each input, and ranks the inputs according to the strengths of the sensitivities. A sensitivity analysis is performed by individually
incrementing inputs across their historical ranges and calculating how much the output changes.
• Phase 1 and 2 final models were models whose inputs generated the highest R2 increase over the baseline models.
RESULTS
• The R2 of the Phase 1 and 2 baseline/final models’ were 0.16/0.31 and 0.13/0.21, equating to percent R2 differences (%diff) of 131% and 48%,
respectively. The low R2 were due to the noisy data and deliberate partial accountings. The lower %diff for Phase 2 was due to the smaller Phase 2 sflux
range relative to the noise. Figure 4 shows graphically that the Phase 1 final model was more accurate than the baseline model. The lower Phase 2 %diff
made the plots less different.
• Table 2 shows the Phase 1 and 2 final models’ sensitivity analyses. Average sensitivity (Avg Sens) is
the relative sensitivity whose absolute values sum to 1.
− Phase 1: sflux.A7 was negatively sensitive to 5 turbidity inputs  consistent with particle deposition
being a major cause of fouling.
− Phase 2: sflux.A20 was negatively sensitive to 6 TOC inputs plus ammonia nitrogen and nitrate
inputs  consistent with biofilm nutrients being a major cause of fouling.
− Phases 1 & 2: sflux.A7 and sflux.A20 were
 positively sensitive to cartridge filter pressure drop  consistent with clogged filters removing more
particles and nutrients.
 oppositely sensitive to total chlorine, with sflux.A7 being negatively sensitive and sflux.A20 being positively sensitive. Chlorine is added at T2
(Figure 1) to suppress fouling, so the opposite effects suggest a possible tradeoff for investigation.
• Figure 5 demonstrates ANN model interpretation. It shows a response surface that visualizes the function fitted by the Phase 1 final model. The T3 turbidity
and cartridge filter pressure drop are plotted on the horizontal axes, and sflux.A7 on the vertical axis. The other 10 unshown inputs (Table 5) were set to
their historical means.
− Triangles [a] and [b] are aligned with the extremes of the cartridge filter pressure drop’s range. Their different heights indicate that the ANN function is
nonlinear. [a] shows that at pressure drop = 1 psi, a turbidity increase from 0.05 to 0.17 ntu decreases sflux.a7 from -0.0008 to -0.0015 gfd/psi (-0.0007).
[b] show that at pressure drop = 8 psi, the same turbidity change decreases sflux.a7 from -0.0008 to -0.0004 gfd/psi (-0.0004).
− The lower sensitivity of sflux.A7 to turbidity at a higher pressure drop was indicated by the sensitivity analysis (Table 5), but the response surface
provides important details about input-output relationships. More information can be obtained by selecting other inputs for the horizontal axes and/or
changing unshown input values to represent alternative scenarios.
CONCLUSIONS
• The modeling ground-truthed cause-effect relationships that were known to OCWD but not ever quantified.
• Most findings were consistent with prevailing wisdom about RO process physics, but not all.
• The modeling produced previously unknown information about interactions between RO stages, the effectiveness of different filter cleaning procedures, and
the opposite effects of chlorine additions on Phase 1 and 2 sflux.
Lower
Savannah
River
Pee Dee
Basin
Figure 1. GWRS process flow diagram.
Figure 2. sflux of Unit #8 runs.
Figure 3. Concatenated sfluxes.
Table 1. Frequently recoded parameters. “Site” is
where monitored. “Freq” is sampling frequency.
Table 2. Phase 1 and 2 final model sensitivity analyses.
Figure 4. Phase 1 runs showing historical sflux.a7 with
predictions by baseline and final models.
• A stacked dataset was compiled by concatenating the
signals associated with the individual units (Figure 3), with
each unit’s data being a layer in the stack. This allowed
models to generalize about the units by treating them all as
one. Unit 2 was omitted as an outlier.
• Multi-layer perceptron artificial neural networks (ANN)
were used to model the Phase 1 and 2 sflux variability
separately as the 7 and 20 -day moving window averages
(MWA) of the 1-day change () in sflux (sflux.A7,
sflux.A20). ANN are a machine learning method that curve-
fits nonlinear functions to multivariate data.
• Signals were input to ANN models as low-frequency MWA
and higher-frequency passbands. Passbands are signal
components that contain frequencies within specified
ranges. The ranges of passbands p1, p3, and p7 were 13,
37, and 720 days.
Figure 5. Phase 1 final model response surface.