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 & VeatchRajeev Goel, PhD, P Eng., Hydromantis, Inc.

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

• 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™

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.

Durham AWTF

Drivers for Nutrient Recovery

Primary Clarifier

Secondary Clarifiers

Aeration Basins

Tertiary Clarifiers

Tertiary Filters

Recycled Flow

• Recycled flow increases the phosphorus load to theprocess by 20 – 30 %

• Increased load can lead to process instability

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

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

WASSTRIPSolution• 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

Major Observed Effects

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

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

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

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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

Modeling approach

Process Layout

Process ASM1

ASM3 Mantis Mantis2

ASM2d New General

Fermentation stepNitrification/denitrificationAerobic denitrificationAerobic substrate storageCOD “Loss”2-Step Nitrification / DenitrificationNO3- as a N source for cell synthesisAlkalinity consumption/generationAlkalinity as a limiting factor for growthBiological Phosphorus RemovalPrecipitation of P with Metal HydroxidesInorganic precipitation (Struvite, other Ca & Mg precipitations)Temperature dependency * *pHAnammoxMethylotroph

Process ASM1

ASM3 Mantis2

Mantis3

ASM2d New General

Fermentation stepAerobic/Anoxic substrate storageNitrification/denitrificationAerobic denitrification2 steps nitrificationAmmonia as a limiting factor for growthNitrate as a nitrogen source for cell synthesisAlkalinity change computationAlkalinity as a limiting factor for growthBiological Phosphorus RemovalPrecipitation of P with Metal HydroxidesInorganic precipitation (Struvite, other Calcium, Magnesium)Anaerobic Stabilization (COD losses)Temperature dependency * *Carbon footprint/GHG (N2O, etc.)

Plant Layout

Major Operational Periods

2011 2012 2013 2014 20150.00

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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).

Data Summary

Stream Flow TSS Ammonia TP SP Sol MgChemical Sludge N = 292WAS N = 292 N = 239 N = 41WASSTRIP effluent N = 152 N = 155 N = 196 N = 100Thickener Underflow N = 292

Absence of model critical dataAbsence of model useful dataLimited availability of model critical dataRobust 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

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

Model Calibration

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

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.

• 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

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

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)

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.

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

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

Questionsfabiyi@hydromantis.com773-647-8872

Some Key Mechanisms

Cation bridging Theory