granular activated carbon testing
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Granular Activated Carbon TestingJonathan G. Pressman
Office of Research and DevelopmentNational Risk Management Research Lab 9/26/19
Contributors
Gulizhaer Abulikemu Thomas Speth Jeff Vogt Maria Mayer Ying Hong Thomas (Mac) Kelley Gulizhaer Abulikemu Toby Sanan Tim Neyer Dionysios Dionysiou Paul Rossman Stephanie Brown David Griffith Deborah Roose Clermont County Ohio Water Resources Department Greater Cincinnati Water Works
Outline
Introduction/GAC 101
Case Studies:Pilot Scale - PFAS
Bench Scale - cVOCs
Bench Scale - Microcystin-LR
Bench Scale - Standardization of NOM
Adsorption and GAC
Adsorption is the accumulation of material at the interface between two phases
Activated carbon is the most common adsorbent because it can economically adsorb a broad spectrum of organic chemicals, a result of its extremely large surface area
https://pacificwater.com.au
Factors Affecting Adsorption
Adsorbate Solubility Polarity MW Charge Concentration
Activated CarbonSolution Conditions
Temperature pH
Binding forces Physical Chemical Specific
Adsorption Equilibrium
In the presence of GAC, chemicals in water will partition between the carbon and water, such that a chemical equilibrium between the two phases is eventually established
Freundlich isotherm – qe=KCe1/n
K = adsorption capacity at unit concentration (mg/g)(L/mg)1/n
1/n = adsorption intensity (dimensionless)
Log
q e(m
g/g)
Log qe = Log K + 1/n LogCe
Log Ce (mg/L)
Kinetics
Transport from bulk liquid to liquid film Transport across liquid film to GAC surface Transport within GAC particle by surface and pore diffusion Adsorption to the GAC surface
Controlling factors GAC diameter GAC type Chemical being adsorbed Chemical concentrationWater quality Competition
Need for Testing
Full-scale
Pilot-scale
Bench-scale
Bench Scale
Rapid Small Scale Column Tests (RSSCTs) A benefit because:
Shorter Duration Small volume of water Data can be scaled up to performance of Full Scale unit Mathematical relationships used to scale
Based on mathematical similitude – dimensionless parameters equal for both systems
Surface/pore diffusion modulus, solute distribution parameter, Stanton number, Reynolds number, etc.
Two primary test design scenarios:
Constant Diffusivity: 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑆𝑆𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐿𝐿𝑆𝑆
= 𝑅𝑅𝑆𝑆𝑆𝑆𝑅𝑅𝐿𝐿𝑆𝑆
2= 𝑡𝑡𝑆𝑆𝑆𝑆
𝑡𝑡𝐿𝐿𝑆𝑆
Proportional Diffusivity: 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑆𝑆𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐿𝐿𝑆𝑆
= 𝑅𝑅𝑆𝑆𝑆𝑆𝑅𝑅𝐿𝐿𝑆𝑆
= 𝑡𝑡𝑆𝑆𝑆𝑆𝑡𝑡𝐿𝐿𝑆𝑆
Modeling
Pore and Surface Diffusion Model (PSDM)
AdDesignS
WBS cost models
https://www.epa.gov/dwregdev/drinking-water-treatment-technology-unit-cost-models-and-overview-technologies
Case Study – Pilot Scale PFAS
Objectives
Evaluate GAC for the removal of PFASs at lower concentrations in the submicrogram per liter range, not currently regulated.
Evaluate adsorptive competition on the GAC between the PFAS and NOM, which could become highly relevant at the low PFAS concentrations of interest.
Calculate carbon usage rates (CUR) for the GAC and PFAS of interest.
Case Study – Pilot Scale PFAS
Pilot Configuration4 in diameter4 ft length0.25 gpm (results in 10-min EBCT)~10 lbs of carbon2 columns in series = 2x EBCT trialExpected duration 4-6 months<30 ppt individual PFASs Partial breakthrough beginning after ~1
monthComplete breakthrough 2+ months
Case Study – Pilot Scale PFAS
0.00
0.50
1.00
1.50
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000
C/C0
Bed Volumes
PFBA (C4) PFPeA (C5) PFHxA (C6) PFHpA (C7)
PFOA (C8) PFNA (C9) PFDA (C10) PFBS (C4)
PFPeS (C5) PFHxS (C6) PFHpS (C7) PFOS (C8)
PFMOAA* PFO2HxA* PFO3OA* PFPrOPrA/GenX
PFO4DA* Nafion BP2*
10-min EBCT
Modelingcourtesy Jonathan Burkhardt
Modeling helps extend the usefulness of Pilot/RSSCT data
Test impact of different influent conditions, bed sizes or flowrates
Compare difference between GACs
Fit Pilot Data
Model
Con
cent
ratio
n (n
g/L)
Predict Full-Scale w/ Multiple PFAS
Total
Days
Tota
l Con
cent
ratio
n (n
g/L)
Model Different Operational Conditions
Current Flow, average conc.Current Flow, lower conc.Current Flow, higher conc.Future Flow, average conc.Future Flow, lower conc.Future Flow, higher conc.
Expected Bed Replacement Frequency (Days)
Case Study: cVOCs
Objectives
Evaluate GAC for the removal of a group of cVOCs at lower concentrations in the submicrogram per liter range, including additional cVOCs not currently regulated.
Evaluate adsorptive competition on the GAC between the cVOCs and DOM, which could become highly relevant at the low cVOCconcentrations of interest.
Calculate carbon usage rates (CUR) for the GAC and cVOCs of interest.
Experimental Apparatus
cVOC Breakthrough
GAC Fouling
NOM Effects
Carbon Usage Rates
Case Study: Microcystin-LR
Objectives
Use RSSCTs to assess the effectiveness of GAC in treating cyanotoxins, particularly MC-LR
Investigate competitive adsorption/interactions of NOM (TOC) and MC-LR
Use RSSCTs to evaluate the adsorption capacity of GAC in treating cyanotoxins when the GAC was preloaded with NOM at different levels
RSSCT Apparatus & Design
• EBCTSC = 0.84 min
• EBCT LC = 10 min
• Hydraulic loading rate (v)= 1.73 m h-1
• Flow Rate= 0.32 mL/min
• Sieve size= 100 × 200
• RSSCT column diameter= 3.74 mm
• Bed Volume= 0.27 mL
• Bed Length= 2.42 cm
• dp LC = 1.29 mm
• dp SC = 0.11 mm
• SF= 11.8
Feed Water and GAC
Feed water, i.e., GAC Influent water (GACI) was collected from the drinking water utility
GAC was also collected from the utility Regenerated with 15% virgin makeup
GAC was ground to meet 0.11 mm average particle size (100 x 200 sieve size)
GAC rinsed to separate “fines” to prevent pressure buildup in column
Varying NOM PreloadingM
C-L
R (µ
g L
-1)
0
2
4
6
8
10
Influent A
Influent C
Influent BEffluent A (0%)
Effluent C (100%)
Effluent B (55%)
0 40000 60000 80000Bed Volumes6min EBCT, 80% at 30000 BV
(Hall et al.2000)
20000
Case Study: Standardized NOM
Objectives
Evaluate water quality conditions of reconstituted GAC influent water (Recon GACI) that are essential for conducting RSSCT studies that compare with original GAC influent water (GACI) RSSCTs.
Compare RSSCT predicted breakthrough behaviors of NOM in GACI and Recon GACI, to evaluate if Recon GACI can be used as a surrogate for source water.
Compare the RSSCT predicted NOM breakthrough profiles obtained using virgin and regenerated GACs to the full-scale breakthrough profile.
RSSCT Apparatus & Design
Pressure Gauge
3-way Valves
Influent Carboy
Glass Wool
Automatic fraction collector
Pulse Dampener
GAC
Pump
Major RSSCT design parameters include :1. GAC mesh size: 230 × 325 2. Column internal diameter: 0.38 cm3. GAC bed height: 4.1 cm4. Empty bed contact time (EBCT):
0.81min5. Flow rate: 0.58 ml/minOther RSSCT design and operation parameters are specified elsewhere (Zhi Weili et al. 2012 ).
GAC Preparation
Grind GAC with mortar and pestle, collect particles between sieves 230 and 325 mesh size (54 µm).
Rinse collected particles with milli-Q water multiple time to remove fines.
Dry at 95 ℃ .
NOM ComparisonTO
C m
g/L
0.5
1
1.5
2
2.5
00 5000 10000 15000 20000 25000
Throughput (Bed Volumes)
GACI Influent
GACI Effluent
Recon GACI Influent
Recon GACI Effluent
RSSCT vs. Full ScaleN
orm
aliz
ed T
OC
, C/C
0
00.10.20.30.40.50.60.70.80.9
1
0 50 100 150 200 250
Large column days
Virgin RSSCT_GACI
Virgin RSSCT_Recon GACI
Regenerated RSSCT_GACI
Regenerated RSSCT_Recon GACI
Full scale
Photo image area measures 2” H x 6.93” W and can be masked by a collage strip of one, two or three images.
The photo image area is located 3.19” from left and 3.81” from top of page.
Each image used in collage should be reduced or cropped to a maximum of 2” high, stroked with a 1.5 pt white frame and positioned edge-to-edge with accompanying images.
Questions? Jonathan G. PressmanPressman.jonathan@epa.gov513-569-7625
DisclaimerThe information in this presentation has been reviewed and approved for public dissemination in accordance with U.S. Environmental Protection Agency (EPA) policy. The views expressed in this presentation are those of the author(s) and do not necessarily represent the views or policies of the EPA. Any mention of trade names or commercial products does not constitute EPA endorsement or recommendation for use.
Office of Research and DevelopmentNational Risk Management Research Lab 9/26/19
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