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PROJECT NAVIGATOR, LTD. Landfill Operations: Designing and Using Digital Data Collection Systems to “P di ti l O t ” L dfill L“Predicatively Operate” a Landfill as a Large-Scale Bioreactor
Presented by
H lil K k PhD P j t N i t LtdHalil Kavak, PhD, Project Navigator, Ltd.Raudel Sanchez, PhD, Project Navigator, Ltd.Ian A. Wester, ScD, Project Navigator, Ltd.Theodore Tsotsis, PhD, University of Southern CaliforniaTheodore Tsotsis, PhD, University of Southern CaliforniaMohammed Shahimi, PhD, University of Southern California
SWANA LANDFILL GAS SYMPOSIUMMarch 11 2009
www.projectnavigator.com
March 11 2009Atlanta, Georgia
Project Navigator, Ltd.Project Navigator, Ltd.
• Houston, TX(713) 468-5004
• Pleasant Hill, CA(925) 969-9574
• Brea, CA(714) 388-1800Project Navigator has six main offices.
• Raleigh, NC(919) 539-1928
• Malvern, PA(610) 251-6851
• Seattle, WA(206) 390-3948
Compliant Landfill Operation: Standards to be AchievedCompliant Landfill Operation: Standards to be Achieved
At Cap Surface< 50 ppm CH4
Landfill Perimeter
<10-4 excess cancer risk
HI < 1
GWMWGP
Compliance standards envelope the waste prism
<5% CH4 <MCL’s
Drive and Control Compliance Via LFG ExtractionDrive and Control Compliance Via LFG Extraction
ThermallyThermally treat
• Goal: Set vacuum to match gas extraction rate to gas generation rate
• Threat:Too much vacuum leads to air intrusion, and can exacerbate EOZ conditionseat oo uc acuu eads o a us o , a d ca e ace ba e O co d o s
Why should we be concerned about EOZs?Why should we be concerned about EOZs?
Reason Cost Impact1. Loss of gas collection wells Replacement cost
2. Reduces gas collection efficiency
Potentially no cost impactefficiency
3. Reduces effectives of “gas PLC” in the area
Additional cost to maintain compliance
4. Increases landfill subsidence Cost for slope & cap repair
5. Increases risk from seismic ti it
Repair and replacement costactivity
6. Health, safety, & fire threat Repair and replacement cost
Knowledge / System Size Vs TimeKnowledge / System Size Vs Time
KnowledgeRemedy Construction Completed in 2000
Today, 2008
em S
ize Existing Gas Extraction System
LFG
Sys
t
Capitalize on this Delta to Achieve Cost Reductions
wle
dge
/ L Achieve Cost Reductions
Kno
w
Time
Main Components of Digital Site
Decision Making System
Site Monitoring Display System
Main Components of Digital Site
Decision Making System
Remote Data Collection • Neural Network• Genetic Algorithms
• ArcGIS• EVS• Global Mapper
Genetic Algorithms
OII Northeast enhanced oxidation problem Map temperature profile and vacuum at extraction wells
• WiFiT l t
p p p Control pressure to get optimal temperature distribution Finding pathways of air intrusion Visual managing data efficiently Providing tools to operators to keep the site at optimum operational conditions
• Telemetry
Remote Data Collection
Extraction, Monitoring Wells, and Gas Probe
Properties p(Location, Depth, ..)
Pilot Project Wi-Fi NetworkPilot Project Wi Fi NetworkGateway (Base Station)
Power grid connection Mounted at the office Connected to Internet Receives sensor network
Wi-Fi Extender (Repeater) Solar-powered 7’-10’ mobile platform Receives data from
wireless sensors
Wireless Sensors Rapidly deployed Transmit to Wi-Fi network
Receives sensor network data carried over Wi-Fi network
wireless sensors
Wireless Sensor Area
Possible Wi-Fi ApplicationsPossible Wi Fi Applications
Evaluation of Data Collection TechniquesEvaluation of Data Collection Techniques
Temperature Sensor InstallationTemperature Sensor InstallationWireless
Transmitter
Probe Head
Installation of Sensor in Well Installed Temperature Probe
Wi-Fi Remote Monitoring System InterfaceReal-Time Online Temperatures (Well IV-5DR) Instrumented Well Locations
Wi Fi Remote Monitoring System Interface
130
135
140
145
150Historical Well Temperatures (09/02/07 to 09/07/07)
Extracted from Wi-Fi Sensor Database (Well IV5-DR)Legend
75
80
85
90
95
100
105
110
115
120
125
Tem
pera
ture
(F)
30 ft below surface
80 ft below surface
55 ft below surface
50
55
60
65
70
9/2/2007 0:00 9/3/2007 0:00 9/4/2007 0:00 9/5/2007 0:00 9/6/2007 0:00 9/7/2007 0:00 9/8/2007
Date
105 ft below surface
Data Analysis by Utilizing Prediction Toolsata a ys s by Ut g ed ct o oo sPrediction Tools Decision Making and
Corrective Action
Description of the ProblemDescription of the ProblemLandfill gas extraction
a
fd Cap settlement
Landfills are highly heterogeneous porous media
Complex phenomena, including flow and transport f d i t bi d d ti d li b
e
c
CH4, CO2, VOC’s
Gas probe
Enhanced Oxidation Void Zone, which Translates itself into Settlement at the Landfill Cap
CH4Native
Landfill Gases Exert a Partial Pressure on the Groundwater Table, Leading to Gas
CH4
of gases and moisture, biodegradation, and nonlinear reactions
Landfills are large-scale bioreactors
Groundwater
Impacted Groundwater (GW)
to Gas Absorption and GW ImpactsThe landfill is presented by a three-dimensional
computational grid
The blocks are cubical, but do not have the same size
Since a landfill is a porous medium, each block has
The model contains a number of extraction/monitoring wells
its own permeability tensor, porosity and tortuosityfactor
Due to compaction, the vertical permeabilities are ll h h h i l d i fsmaller than the horizontal ones, and increase from
the bottom to the top
Landfill Biodegradation ModelingClassification of Wastes Three classes of wastes : readily
biodegradable, moderately biodegradable, least biodegradable
Gas generation rate αk(t) of gaseous species k:
3
Z
Landfill Biodegradation Modeling
least biodegradable Monod equation for biodegradation:
,
sdtd
s Ck : total gas generation potential of gas kAi : fraction of component i in MSW
,1
tii
ikk
ieACt
zf L
Zttt 0
ψ - concentration of the substrate Ф – concentration of micro-organismκ - maximum rate of substrate utilization
s iλi : gas generation constant of Ito : time since cover was placedtf : time to fill the landfill
κs - the half velocity coefficient Lz : landfill depth
Governing Equations and Iterative ProceduresGoverning Equations Four components CH4, CO2, O2 and N2
Darcy law is assumed
Iterative Procedure Finite-volume method is used to solve
the equations
Governing Equations and Iterative Procedures
Darcy law is assumed Convective-diffusion reaction CDR
equation governs concentration of gases The CDR equations for the gas
q FV allow the use of a non-uniform grid Conjugate-gradient method and forward
discretization of time-derivatives are e C equat o s o t e gascomponent k:
zCD
zyCD
yxCD
xtC k
kk
kk
kk
used to solve the equations
)( zCzpk
zC
ypk
yC
xpk
x kkm
zk
m
yk
m
x
Top view of computational grid
CH4
CO2
CH4
CO2
CH4
CO2
Optimization Complexities
The permeability, porosity, tortuosity, and gas generationt ti l ti ll l d f
Optimization Complexities
potentials vary spatially over several orders ofmagnitudes.
Due to a variety of factors, the amount of experimentaldata that characterize the properties of a landfill isdata that characterize the properties of a landfill isseverely limited.
Given a computational grid that represents a landfill, alarge number of transport and reaction equations must besolved.
The equations are highly nonlinear. Serial computation is not effective, and in most cases
i iblimpossible. Parallel computations are needed. The Genetic Algorithm is used for the optimization
problemproblem.
Optimization Based On A Genetic Algorithm
Time-dependent methane concentration
GA has four main elements:
Selection: for generating a solution
Optimization Based On A Genetic Algorithm
pprofiles at some extraction wells are taken asthe data.
Synthetic data are generated to validate thel i h
Selection: for generating a solution
Design of the" genome”: to constrain
the variables, and the generation ofalgorithm.
Massively-parallel computations using 180processors with message-passing interfaceare used
the “phenotype” – the model of
transport and reaction.
Crossover and mutation: forare used. The objective function is,
2
CHCHF
Crossover and mutation: for
generating new solutions and
approaching the optimal one.mod,4exp,4
jjj
CHCHF Eliticism: to select those solutions that
eventually lead to the true optimal
solutionsolution.
Optimization Based On A Genetic Algorithm Random initial guesses for the spatial
distributions of the permeability,
Optimization Based On A Genetic Algorithm
C I i i l D i P l i
GA Flowchart
porosity, tortuosity factor, and total gasgeneration potential.
Solve the governing equations and
Create Initial Design Populations
Evaluate Obj. Function of Designsλ=900λ=700λ=300g g q
compute those properties for which dataare available.
Evaluate the objective functionSelect and Reproduce (Create New Designs)
old
λ=300
Evaluate the objective function
Check whether convergence has beenachieved. If not, use selection,m tation crosso er and eliticism to
( g )
Replace Designs of the Old Populations with New Designs
new
mutation, crossover, and eliticism toupdate the parameter space.
Repeat until the true optimal
p g
Next Generation
distributions are obtained. Stop?
Comparison of Data and Optimal Profiles Over 18,000 parameters are optimized.
It took 180 CPU hours to compute the optimal distributions
Comparison of Data and Optimal Profiles
It took 180 CPU hours to compute the optimal distributions.
The processors were Pentium-4.
Comparison of data and optimal permeability distribution
Comparison of data and optimal gas profiles
Artificial Neural Networks for Landfills?
ANN mimics the human brain (neurons areinter-connected to allow the brain make
Artificial Neural Networks for Landfills?
inter connected to allow the brain makedecision on the input).
Inputs to ANN are inter-connected todiscover relationships between the inputvariables
ANNs are recognized as universalapproximators.
Abl t t t d i i t f Able to capture trends in a given set ofdata.
Capable of forecasting the behavior of asystem given reasonable amount of datasystem, given reasonable amount of data.
However, the predictions are not based ona specific physical model of the system,and the phenomena that occur there.
ANN for Forecasting the Behavior of the Landfill Historical landfill gas data (T, P,
Gas Consents) A ti f th l dfill d i
ANN for Forecasting the Behavior of the Landfill
A section of the landfill was used inthe study. 32 Wells are located inthis zone.
60% f d t d t t i ANN 60% of data was used to train ANN Forecasted T, P, CH4, CO2 and O2
distribution Th di i h l The predictions help:
• Operators to make quick and effectivedecision for the short term.
• Operating the wells at the optimal• Operating the wells at the optimalvacuum conditions to avoid potentialfires and optimal gas quality.
• Calibrating the site condition for longt l dfill t bilitterm landfill stability.
How Does an ANN Work?
Σ F1 Σ F2
b11 b
CH4, CO2, O2,P, Well 1
T @ well 1
21n1
1n
How Does an ANN Work?
Σ F1 Σ F2
b11
b12
b21
b22
CH4, CO2, O2,P, Well 2 T @
well 2
22n1
2n
Σ F1 Σ F2
b12
b13
b22
b23
CH4, CO2, O2,P, Well 3
CH CO O
T @ well 3
11n
23n1
3n
Σ F1 Σ F2
b14
b23
b24
CH4, CO2, O2,P, Well 4 T @
well 4
24n1
4n
CH4, CO2, O2, 2n1nΣ F1 Σ F2
b15 b25
P, Well 5 T @ well 1
5n5n
Artificial Neural Network 20 hidden layers with a Tansig transfer
function are used.N
mi
mj
mij
mi baWn
m
1
1
Artificial Neural Network
Data are separated into training, validated,and testing.
The output layer is evaluated. m
imm
i
iijiji
nFa
1
p y
The performance function P is minimized.
Weight and biases are updated using aback propagation algorithm
N
iCalActual TT
NP
1
21
M
Mi
Mmii
mj
mij
mij
nFataWW 2 1back propagation algorithm
All the calculations require less than 100iterations.
Mi
Mi
Mmiiold
mijnew
mi
Mi
iijoldijnewij
nnFatbb
n
2
CH4 ForecastingCH4 Forecasting
CO2 ForecastingCO2 Forecasting
Temperature ForecastingTemperature Forecasting
Conclusion for ANN and GE Modeling
Genetic algorithm can be used to correctly predict thespatial distributions of the morphology of a landfill
Conclusion for ANN and GE Modeling
spatial distributions of the morphology of a landfill.
However, GA requires a significant amount of CPUd l b hi h f tand can only be run on high performance computers
As an alternative, artificial neural networkscan be used to get quick estimates and forecast for thefuture, short-term, behavior of a landfill.
One should be able to combine the two to develop apredictive tool for the short-term, as well as long-termp , gbehavior of a landfill.
Data Visualization Automationata sua at o uto at oVisualization Tools Visualization
180F
250F
75F
130F
100F
180F
Enhanced Oxidation ZonesEnhanced Oxidation Zones
180F
250F
130F
100F
T>135 F
75F
T>135 F T>155 F
Gas Well Vacuum Distribution Vs. Gas Well Temperatures
Legend
Temperature (oF)
Well Head Vacuum (inch Water)
135 – 257 (max T measured)
Enhanced Oxidation Zones
3D View of Possible Hot Zone
Recent Cracks
Possible Hot Zone ExtensionZone Extension
Extraction Wells
Sampled Extraction Wells
Non-sampled Extraction Wells
Legend
Extraction Wells
35
Methane Concentration Distribution
CH4 Concentration, %
Legend
CH4 Concentration at Gas Probes, %
Well Location
36
Probe Location
Vacuum Distribution
Well Head Vacuum (Inches of Water Column)
Legend
CH4 Concentration at Gas Probes, %
Well Location
37
Probe Location
Settlement Forecasting
0 820 0 720
LegendLegend
Elevation Change in ft
Control Points
-0.820 - -0.720-0.710 - -0.620-0.610 - -0.520-0.510 - -0.410-0.400 - -0.310-0.300 - -0.210-0.200 - -0.110-0 100 - -0 005-0.100 - -0.005-0.005 - 0.0100.100 - 0.200Note: Change in elevation between March
and September 2004