The new resource recovery paradigm that is being increasingly adopted in the wastewater industry is leading to the emergence and adoption of new treatment processes. Process modeling can allow operators to get a good handle on the impact of these technologies and processes on their operations.

1. Applying Process Modeling With GPS-X™
For Understanding WASSTRIP® Impact
On Nutrient Recovery And Net Solids
Production
Malcolm Fabiyi, PhD, MBA (Hydromantis USA)
Ahren Britton, Ostara Nutrient Recovery Technologies
Peter Schauer, Clean Water Services
Andrew Shaw PhD, PE, Black & Veatch
Rajeev Goel, PhD, P Eng., Hydromantis, Inc.

2. Outline
• Process models in WWT
• Nutrient recovery – challenges & opportunities
• WASSTRIP - Durham Case Study
• Conceptual role of models
• Conclusion

3. • Established in 1970
• Sanitary sewer and
Surface Water
Management
provider
• Serves over 530,000 customers
and industries in urban
Washington County, Oregon
• 4 wastewater treatment facilities
• Durham AWTF – 25 MGD
• Key player in P recovery
• Multiple full scale facilities
• BOO business model
• Developer of water &
wastewater process modeling
tools – GPS-X™

4. Aims & Methodology
• Full scale data from January 2009 through October
2015
• Focus on solids handling processes at the Durham
facility.
• flows and compositions of the solids streams from the
secondary and tertiary clarifiers to the solids handling
processes at the plant.
• Figure 2 provides an overview of the key process units
and sampling points at the facility, while Table 1
provides details on the abbreviations used in the process
flow diagram.

5. Durham AWTF

6. Drivers for Nutrient Recovery
Primary
Clarifier
Secondary
Clarifiers
Aeration
Basins
Tertiary
Clarifiers
Tertiary
Filters
Recycled
Flow
• Recycled flow increases the phosphorus load to the
process by 20 – 30 %
• Increased load can lead to process instability

7. Resource Recovery – Challenges &
Opportunities
• Processes concentrate levels of N, P
• Recovery of resources as Struvite (N,P) & CH4
Carbon as CO2Nitrogen as N2
Carbon as CH4
P as Sludge
N, P as Struvite

8. Struvite (NH4PO4Mg) in pipes
Struvite (NH4PO4Mg) recovered as fertilizer
Uncontrolled Struvite Precipitation
Controlled Struvite Precipitation
Challenges of Resource Recovery
Struvite (NH4PO4Mg) in digesters
Solution: Cycle P and Mg from
digesters to P - WASSTRIP
recovery

9. WASSTRIP
Solution
• Diverting Mg from the
digester to the Ostara reactor
reduces the amount of
struvite formed in the
digester and increases the
struvite formed in the reactor
as product and revenue

10. Major Observed Effects

11. Impacts on Process Operations
• Reduction of recycle phosphorus load
• Increased process (EBPR) stability
• Reduction in solids loading
• Reduction in alum needed
• Reduction in lime needed
• Reduction in biosolids dry tonnes
• Impacts dewatering – M:D ratio changes
Tools that can allow operational control & mechanistic understanding Required

12. How Would My Plant Be Impacted?
What is the Cost of Adopting Innovation?

13. How Would My Plant Be Impacted?
Process Understanding
• Run pilots
• Demo at full scale
• Learn from other plants
• Use Process models
(e.g., GPS-X™)
What is the Cost of Adopting Innovation?
Model - representation of a system that can predict some system behavior
Virtual PlantActual Plant

14. 0
10000
20000
30000
40000
50000
60000
0 50 100 150 200 250 300
Time (days)
WASTSSConcentration(mg/L)
Simulated Measured
How Is It Used?
Create
Model
Calibrate to
Known
Performance
Simulate
Different
Scenarios
Simulate “Base
Case”
Compare and
Evaluate
• Hydraulic model
• Biological model (ASM, ADM, Mantis2)
• Aeration model
• Equilibrium chemistry
• Reaction kinetics
• Mechanical & Thermal effects

15. Modeling approach

16. Process Layout

17. Process ASM1 ASM3 Mantis Mantis2 ASM2d New General
Fermentation step
Nitrification/denitrification
Aerobic denitrification
Aerobic substrate storage
COD “Loss”
2-Step Nitrification / Denitrification
NO3- as a N source for cell synthesis
Alkalinity consumption/generation
Alkalinity as a limiting factor for growth
Biological Phosphorus Removal
Precipitation of P with Metal Hydroxides
Inorganic precipitation (Struvite, other Ca
& Mg precipitations)
Temperature dependency * *
pH
Anammox
Methylotroph

18. Process ASM1 ASM3 Mantis2 Mantis3 ASM2d New General
Fermentation step
Aerobic/Anoxic substrate storage
Nitrification/denitrification
Aerobic denitrification
2 steps nitrification
Ammonia as a limiting factor for growth
Nitrate as a nitrogen source for cell
synthesis
Alkalinity change computation
Alkalinity as a limiting factor for growth
Biological Phosphorus Removal
Precipitation of P with Metal Hydroxides
Inorganic precipitation (Struvite, other
Calcium, Magnesium)
Anaerobic Stabilization (COD losses)
Temperature dependency * *
Carbon footprint/GHG (N2O, etc.)

19. Plant Layout

20. Major Operational Periods
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
2011 2012 2013 2014 2015
Normalized Data in Major Operational Periods at Durham AWWTF
Thickening centrifuge feed VFA Feed (gpd)
Figure 3: Normalized data for average values of the thickening centrifuge feed (maximum value is 1.53% solids)
and VFA feed to WASSTRIP™ (maximum value is 47,787 gpd).

21. Data Summary
Stream Flow TSS Ammonia TP SP Sol Mg
Chemical Sludge N = 292
WAS N = 292 N = 239 N = 41
WASSTRIP effluent N = 152 N = 155 N = 196 N = 100
Thickener Underflow N = 292
Absence of model critical data
Absence of model useful data
Limited availability of model critical data
Robust availability of model critical data
Review of 2015 data set. Flow is in gpd, while all other variables have units of mg/L. Note: SP –
Soluble Phosphorus; TSS- Total Suspended Solids; Sol Mg – Soluble Magnesium; TP – Total
Phosphorus.
* P selected as calibration data

22. Facility Layout
• Model was updated with the physical design parameters for the various unit processes.
• WAS influent was modeled as a sludge stream and characterized to the available data.
• Chemical sludge was modeled using the states model, and also characterized to the
available data set.
• The model was then calibrated to the ortho – P data, and used as the basis for the modeling based evaluation of the impact of sludge
pre thickening on the WASSTRIP™ process.
GPS-X™ model layout of the solids handling line at Durham AWWTF

23. Model Calibration
Effluent phosphorus from WASSTRIP™ process. The plant data are the diamonds
while the solid line represents the simulation results.

24. Investigating Impact of Pre-Thickening
• Flow control element was introduced into the model to allow for the partial or complete
bypassing of the mixed WAS and chemical sludge stream around the WAS/CHS thickener
Plant layout with flow control element for enabling bypass of WAS/CHS thickener.

25. • Significant flow bypass enabled by
the WAS/CHS thickener
• Thickener cycles significant flows
back to Basin
• Higher retention time in WASSTRIP
unit & Solids handling solids
• Low level of solids loss in the
recycled sludge from the
WAS/CHS thickener overflow to
the aeration basin
• Tradeoff of HRT vs solids loss is
likely to be minimal
Effect of Pre -Thickening
on Solids
Effect of Pre -Thickening
on Flows
Impact of Pre-thickening: Sankey Plots

26. Impact of Pre-Thickening
Figure 6: Plot depicts the impact of WAS/CHS thickener bypass on solids
concentration in the feed to the WASSTRIP™ process and hydraulic retention time
in the WASSTRIP™ reactor

27. Impact of thickening on concentrations
• Concentration change for propionate ~10X,
• Change in ortho P release ~3X, similar to range of flow diversion
Figure 8: plot depicting impact of pre thickening on VFA
formation and PAO content in the WASSTRIP™ reactor
(concentration basis)
Normalized plot depicting impact of pre thickening on VFA
formation and PAO content in the WASSTRIP™ reactor
(concentration basis). X axis represents normalized flow while Y
axis represents normalized concentration (mg/L)

28. Impact of thickening on flows of variables
Plot depicting impact of pre thickening on VFA formation and
PAO content in the WASSTRIP™ reactor (mass basis)
Normalized plot depicting impact of pre thickening on VFA
formation and PAO content in the WASSTRIP™ reactor (mass
basis). X axis represents normalized flow while Y axis represents
normalized mass (g/day)
• Mass flows of VFA formed in the reactor (acetate and propionate) were more significantly affected by pre thickening.
• Concentration of orthophosphate decreased significantly, overall mass flow of ortho-P did not decrease significantly
• Enhancements to P recovery in the struvite reactor might be mediated partly by the higher concentrations of ortho-P and Mg in the
centrate, as well as by the impact of reduced flow volumes on parameters such as the superficial liquid velocity, dilution rate and the
hydraulic residence time.

29. What Can Models Support?
• Operator training
• Post installation impact
• Quantification of operational effects
• Sludge reduction
• Biogas increase
• Nutrient recycle
• GHG / Carbon footprint
• Make up of solids in digester (Newberyite, Struvite, etc.)
• VFA formation – acetate, propionate
• M:D ratios in solids streams, digestate, etc

30. Conclusions
• GPS-X™ robustly models innovative and emerging
resource recovery technologies
• How technologies integrate into facility
• Reduce risk of implementation
• Basis for training, optimization
• Allow for process drivers and enablers to be
determined
• Causal mechanisms for sludge reduction
• Factors that impact nuisance Struvite (gMgNH4PO4.6H2O/m3),
Newberyite (gMgHPO4.3H2O/m3) precipitation
• Impacts on dewatering (M:D ratios, etc.)
• Carbon footprint
• Future work
• Extend data collection of Mg, K, N

31. Questions
fabiyi@hydromantis.com
773-647-8872

32. Some Key Mechanisms
Cation bridging Theory