applying process modeling with gps-x™ for understanding wasstrip impact on nutrient recovery
TRANSCRIPT
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
0
10000
20000
30000
40000
50000
60000
0 50 100 150 200 250 300Time (days)
WA
S TS
S C
once
ntra
tion
(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
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
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
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
Some Key Mechanisms
Cation bridging Theory