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