optimal groundwater remediation laura place taren blue
TRANSCRIPT
Optimal Groundwater Remediation
Laura PlaceTaren Blue
Outline
• Background– What is Groundwater Remediation– Major Contaminants and Contamination Areas
• The Remediation Process– Treatment Methods– Mathematical Models
• Optimization
• Completed Work• Fluid Flow Modeling
• Plans and Recommendations for Future Work
Background
• Groundwater Remediation– Removal of contaminants from a water supply
• Standards set by the EPA
– Several methods for treatment• Existing• Experimental
– Optimization• Mathematical models
Background• Sources of contamination
– Industrial & agricultural• Storage tanks• Septic systems• Landfills• Hazardous waste sites• Road salts • Refinery operations• Mining• Other chemicals
Contaminants, Possible Health Affects, EPA Standards
Compound Potential Health Affects Sources of Contamination
Benzene Known CarcinogenDischarge from factories, leaching from gas storage tanks and landfills
†,††
Vinyl Chloride Known CarcinogenLeaching from PVC pipes, discharge from plastic factories
†,††
ArsenicSkin damage or problems with circulatory systems, and may have increased risk of getting cancer
Erosion of natural deposits, runoff from orchards, runoff from glass & electronicsproduction wastes
††
CopperGastrointestinal distress, liver or kidney damage, and more
Corrosion of pipes and household plumbing systems, erosion of natural deposits
††
Lead
Delays in physical and mental development in children, possible deficits in attention span and learning disabilities. Adults can experience kidney problems or high blood pressure
Corrosion of pipes and household plumbing systems, erosion of natural deposits
††
Mercury Kidney damageErosion of natural deposits, discharge from refineries and factories, runoff from landfills and crop lands.
††
TrihalomethanesLiver, kidney or central nervous system problems, increased risk of cancer Biproduct of drinking water disinfection
††, †††
Nitrate
In infants, could cause illness or death; characterized by shortness of breath or blue-baby syndrome.
Runoff from fertilizer, leaching from septic tanks, sewage, and erosion of natural deposits.
††
† Environmental Protection Agency Waterscience†† Environmental Protection Agency Safewater
††† National Water-Quality Assessment ProgramCompound EPA Standard Maximum Concentration
Benzene 5 ppbVinyl Chloride 2 ppbArsenic 10 ppbCopper 1.3 ppmLead 15 ppbMercury 2 ppbTrihalomethanes 80 ppbNitrate 10 ppm
Where are the Problem Areas?
ArsenicNitrates
Hard Water VOCs
Optimization
• Goals– Minimize the remaining contaminants– Minimize cost
• Costs minimized are unique to the model
– Other goals are also unique to the specific model– Optimizing the pump treat inject method (PTI)
• Number of wells• Well configuration
Similarities & Differences of Previous Models
Chan
g, C
hu, H
siao
Min
sker
, Sho
emak
er
Scha
erla
eken
s, C
arm
elie
t, Fe
yen
Roge
rs, D
owla
, Joh
nson
Considers Fixed Cost x xConsiders Operating Cost x x x xDetermines Optimal # of Wells x xOptimizes Well Locations x x xHas Time-Varying Pumping Rates (not strictly on/off) x xOptimizes Pumping Rates x x x xIs a 3D Model xAvoids Local Minimum x x x xContains Contamination Plume x x xConsiders Concentration of the Contaminant(s) x x x xUses Pump and Treat Method x x x xIs Not Specific to Particular Compound x x
What are the Choices?
• “Dilution is not the solution!!!”– Inexpensive but never resolves the problem
• Pump, Treat, Inject method (PTI)– Pump contaminated water from the source (the
plume)– Treat the water– Inject treated water back into the aquifer
PTI – Simple Schematic
Treatments
• Existing treatment methods– Ion exchange chromatography– Membranes– “Point of service” treatment– Bioreactors– Adsorption– In situ bioremediation– Liquid-liquid extraction
• Surfactants
Treatment Method Iron
bac
teria
bact
eria
Gia
rdia
& C
rypt
ospo
ridiu
m c
ysts
Har
d W
ater
, Cal
cium
, Mag
nesi
um
Ars
enic
Asb
esto
s
Chl
orin
e
Cop
per
Flu
orid
e
Iron
and
Man
gane
se
Mer
cury
Lead
Nitr
ates
othe
r in
orga
nics
Dis
infe
ctio
n by
prod
ucts
MT
BE
Pes
ticid
es, H
erbi
cide
s &
Inse
ctic
ides
VO
C
Oth
er O
rgan
ic C
ompo
unds
Chlorination X X XWater Softener X / / /*ion exchange resin / / / / / / / / / / / / / / / /Magnetic ConditioningWhole house sediment Filter XWhole house GAC filter / / / / / /Ozonation Device X X X /Manganese Greensand oxidization filter / X XDistillation X X X X X X X X X X X X / / / / /Reverse Osmosis X X X X X X X X X X X X / / / / /KDF filter X X X X X /Ceramic filter X X XGAC filter X X X / / / / /SBAC filter X X X X X / X X X X XActivated Alumina Filter X XUV disinfection XBoiling X X X X / /
X = Removes Contaminant / = Removes Some Contaminant, * = May Add Other Contaminants
Challenges of Remediation
• Plume– Unknown flow patterns– Unknown concentration profiles
• Nonuniformities in concentration
– Unknown position– Uncertainty in composition– Unknown size– Geological uncertainty
Problems and Affect on Treatment
***Modeling of the aquifer depends on many of these parameters. Therefore, all of these issues also become a problem in mathematical modeling.
Unknown Problems With Information What This Affects Flow Patterns Can't be monitored Concentration ProfileConcentration Profiles
Can't be monitored
Well LocationPumping RatesRemediation Time
Plume Position Can't be monitored Well LocationContaminants Can't be determined with any
accuracy Treatment MethodPlume Size
Can't be measured
Well Location Remediation TimeNumber of Wells
Geological ProfileInformation may be good, bad, plentiful, or not exact
Well Location Remediation TimeNumber of Wells
PTI• Has many parameters
– Number and location of wells• Few large wells• Many small wells
– Pumping Rate– Concentration of contaminants in treated water– Can vary well arrangement with time
• For optimization – Need a model!
Well Position and Treatment
Four different well arrangements.***Concentration profile of the plume is affected by location of pumping and
injection wells.
Steps Completed in Optimization
• Analytical Model• Euler Approximation
– Optimization for minimum cost
• Initial Fluid Flow Modeling and Analysis• Refined Fluid Flow Modeling and
Analysis– Optimization for minimum contamination
Analytical Model
0 20 40 60 80 100 120 1400
0.02
0.04
0.06
0.08
0.1
0.12
Concentration vs Time with Slice as a Parameter
Slice 1Slice 2Slice 3Slice 4Slice 5Slice 6Slice 7
time (s)
Conc
entr
ation
(mol
/L)
tstststs inVtF
inout ceccc,,,,
/0 )(
slices timet 0,1
anytinc
Set Up Euler Approximation
slice
outinp V
ccF
dt
dc
Where • dc/dt is the change in concentration with time• Fp is the pumping rate• cin is the concentration into the slice• cout is the concentration out of the slice• Vslice is the volume of the slice
Euler Method Model
• Calculates total remediation time• Uses inputs for:
– Volume of the plume– Time steps– Flow rate– Initial concentration– Desired end concentration
• Calculation in each cell loops until the change in outlet concentration is < 0.0001
0 500 1000 1500 2000 2500 3000 3500 40000
0.02
0.04
0.06
0.08
0.1
0.12
Concentration vs Time for Slices 1 and 50V = 10,000L, F = 5 L/s
Slice 1Slice 50
Time (s)
Conc
entr
ation
Euler Results t1
t2
Cost Optimization
equipmentdrillinginitial
laborutiliitiesoperating
initialoperatingtotal
ccc
ccc
ccC
Major Factor in Cost DescriptionPumping Rate Affects overall utilities usageRemediation Time Affects labor, utilities, permits, and other operating costsNumber of Wells Affects drilling and pumping costs
Fluid Flow Analysis Arrangement
Example of one arrangement – multiple outlets with one inlet
Fluent
• Calculate mass flow rates in the plume– More accurate approximations of concentration
profiles• Characterize fluid flow in the aquifer
– Vary well arrangement– Vary number of injection and extraction sites– Vary pumping rate
Geometry - Gambit
• 1st “draw” geometry in Gambit• Create injection and extraction locations
which may be turned on or off.– For off – face is treated as a wall– For on – face is designated either mass inlet or
outflow– Each face is labeled by location
Generic Geometry
20
10
10 A
B C D E F G 1
2
3
4
5
6
7
Define Geometry in Fluent
One inlet One outlet
Imaginary Planes
• Fluent analyzes flow patterns through planes
A B C
D E
F G H
I J K
L M N
O P Q
Example of Fluid Flow Field in Fluent
Flow rate of 50 kg/s
Example of Fluid Flow Field in Fluent
Flow rate of 5 kg/s
Example of Velocity Contours
Flow rate of 5 kg/s
Excel• Results from Fluent imported into Excel
Mass Balance
C-18 C-14 C-10
Negative flux or positive flux dictates which concentration to use in the mass balance
Remediation Time and Flow Rate
0 5 10 15 20 25 30 35 40 45 500
50
100
150
200
250
300
350
400
450
Remediation Time vs Flow Rate with Well Configuration as a Parameter
(4,4)(4,1)(2,2)(2,1)(1,2)(1,1)(1,4)
Flow Rate (L/s)
Rem
edia
tion
Tim
e (h
r)
(4,4)
(4,1)
(2,2)
(2,1)
(1,2)
(1,1)
(1,4)
Conclusions of this Model
• Imaginary planes give an accurate estimate of flow through the aquifer
• Flux through the planes can be used to describe concentration profiles with time
• This model allows for understanding of general flow patterns with configuration
• Gives basis of comparison for future modeling techniques
New Modeling Strategy
• Pipes in the top of the aquifer– More realistic injection modeling– Flow characteristics re-evaluated
• Several plume types evaluated– Non-uniform initial concentrations– Different shapes
• Injection and extraction varied with time• More realistic aquifer shape
New Geometry for Wells
Naming the Wells
A
B
C
D
1 2 3 4 5 6 7
Naming Imaginary Planes
pa1 pb1 pc1 pd1 pe1pa2 pb2 pc2 pd2 pe2pa3 pb3 pc3 pd3 pe3pa4 pb4 pc4 pd4 pe4pa5 pb5 pc5 pd5 pe5pa6 pb6 pc6 pd6 pe6pa7 pb7 pc7 pd7 pe7pa8 pb8 pc8 pd8 pe8pa9 pb9 pc9 pd9 pe9
pa10 pb10 pc10 pd10 pe10
Planes Through the x-direction
-18-14
-10
18
…
Horizontal Planes
Horizontal planes also named individually for x, y and z location in
the aquifer.
Configurations and Flow Profiles
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Configurations and Flow Profiles
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Configurations and Flow Profiles
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Configurations and Flow Profiles
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Configurations and Flow Profiles
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Model Aquifer with Non-Uniform Concentration
• 3 plumes analyzedkg/L
kg/L
kg/L
kg/L
0.000200
0.000150
0.000025
0.000100
Schemes for TreatmentPlume 1
a1 b1 c1 d1 a1 b1 c1 d1 a1 b1 c1 d1
a2 b2 c2 d2 a2 b2 c2 d2 a2 b2 c2 d2
a3 b3 c3 d3 a3 b3 c3 d3 a3 b3 c3 d3
a4 b4 c4 d4 a4 b4 c4 d4 a4 b4 c4 d4
a5 b5 c5 d5 a5 b5 c5 d5 a5 b5 c5 d5
a6 b6 c6 d6 a6 b6 c6 d6 a6 b6 c6 d6
a7 b7 c7 d7 a7 b7 c7 d7 a7 b7 c7 d7
Step 1 Step 2 Step 3
Schemes for TreatmentPlume 2
a1 b1 c1 d1 a1 b1 c1 d1 a1 b1 c1 d1
a2 b2 c2 d2 a2 b2 c2 d2 a2 b2 c2 d2
a3 b3 c3 d3 a3 b3 c3 d3 a3 b3 c3 d3
a4 b4 c4 d4 a4 b4 c4 d4 a4 b4 c4 d4
a5 b5 c5 d5 a5 b5 c5 d5 a5 b5 c5 d5
a6 b6 c6 d6 a6 b6 c6 d6 a6 b6 c6 d6
a7 b7 c7 d7 a7 b7 c7 d7 a7 b7 c7 d7
Step 1 Step 2 Step 3
Schemes for TreatmentPlume 3
a1 b1 c1 d1 a1 b1 c1 d1 a1 b1 c1 d1
a2 b2 c2 d2 a2 b2 c2 d2 a2 b2 c2 d2
a3 b3 c3 d3 a3 b3 c3 d3 a3 b3 c3 d3
a4 b4 c4 d4 a4 b4 c4 d4 a4 b4 c4 d4
a5 b5 c5 d5 a5 b5 c5 d5 a5 b5 c5 d5
a6 b6 c6 d6 a6 b6 c6 d6 a6 b6 c6 d6
a7 b7 c7 d7 a7 b7 c7 d7 a7 b7 c7 d7
Step 1 Step 2 Step 3
Plume 1, Step 1
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Plume 1, Step 2a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Plume 1, Step 3a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Plume 2, Step 1
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Plume 2, Step 2
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Plume 2, Step 3a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Plume 3, Step 1a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Plume 3, Step 2a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Plume 3, Step 3a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
3D Velocity Contours
Changing Configuration with TimePlume 1
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
0 10 20 30 40 50 601.50000000000002E-05
2.50000000000002E-05
3.50000000000002E-05
0.0000450000000000002
0.0000550000000000002
0.0000650000000000001
0.0000750000000000001
0.0000850000000000001
0.0000950000000000001
0.000105
Concentration vs TimePlume 1
Configuration 2Configuration 3ChangeConfiguration 1
Time (days)
Cont
amin
ant
Conc
entr
ation
(kg/
L)
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
Visualization of ConcentrationPlume 1
t = 4 days t = 20 days t = 50 days t = 0
10-13-10-8 kg/L10-7-10-6 kg/L10-5-10-4 kg/L10-4-10-3 kg/Lkg/L
kg/L
kg/L
kg/L
0.000200
0.000150
0.000025
0.000100
Changing Configuration with TimePlume 2
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
0 10 20 30 40 50 601.50000000000001E-05
2.50000000000001E-05
3.50000000000001E-05
0.0000450000000000001
0.0000550000000000001
0.0000650000000000001
0.0000750000000000001
0.0000850000000000001
0.0000950000000000001
0.000105
Concentration vs TimePlume 2
Configuration 2Configuration 3ChangeConfiguration 1
Time (days)
Cont
amin
ant
Conc
entr
ation
(kg/
L)
Visualization of ConcentrationPlume 2
t = 4 days t = 20 days t = 50 days t = 0
10-13-10-8 kg/L10-7-10-6 kg/L10-5-10-4 kg/L10-4-10-3 kg/L
kg/L
kg/L
kg/L
kg/L
0.000200
0.000150
0.000025
0.000100
Changing Configuration with TimePlume 3
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
a1 b1 c1 d1
a2 b2 c2 d2
a3 b3 c3 d3
a4 b4 c4 d4
a5 b5 c5 d5
a6 b6 c6 d6
a7 b7 c7 d7
0 10 20 30 40 50 601.50000000000002E-05
2.50000000000002E-05
3.50000000000002E-05
4.50000000000002E-05
0.0000550000000000002
0.0000650000000000002
0.0000750000000000002
0.0000850000000000002
0.0000950000000000002
0.000105
Concentration vs TimePlume 3
Configuration 1
Configuration 2
Configuration 3
Change
Time (days)
Cont
amin
ant
Conc
entr
ation
(kg/
L)
Visualization of ConcentrationPlume 3
t = 4 days t = 20 days t = 50 days t = 0
kg/L
kg/L
kg/L
kg/L
0.000200
0.000150
0.000025
0.00010010-13-10-8 kg/L10-7-10-6 kg/L10-5-10-4 kg/L10-4-10-3 kg/L
Conclusions• Dynamic optimization - Changing well configuration with time
– Allows for fairly good cleaning of contaminants– Can give more efficient– Can create step changes in concentration profile– Reaches a plateau in the cleaning process
• Different plume profiles can be modeled with this technique– Plume profile has a large effect on cleaning– Varying shape– Varying initial concentration profile– Efficiency of cleaning and configuration is highly
dependent upon initial concentration profile
Future Work
• Better analyze non-x-directional flow• Examine more economics• Examine different pumping rates• Vary time of well configuration change• Analyze more plume profiles
– Shape– Concentration
• Produce more accurate results for profile near injection and extraction
Questions
Thank you!
Acknowledgements
• Miguel Bagajewicz• Linden Heflin• Jeffrey Harwell• Benjamin Shiau• Peter Lohateeraparp• Rufei Lu• Roman Voronov