Water and Sewer Infrastructure Texarkana, Arkansas Board of Directors Presentation
CRT2 - Water and Sewer Infrastructure, Operations, … and Sewer Infrastructure, Operations, and...
Transcript of CRT2 - Water and Sewer Infrastructure, Operations, … and Sewer Infrastructure, Operations, and...
2017 Annual Conference & Exposition
CRT2 - Water and Sewer Infrastructure, Operations, and Maintenance
June 12, 2017 Philadelphia, Pennsylvania
Water and Sewer Infrastructure, Operations, and Maintenance
Course 2 in the Public Officials Certificate Program
Instructor: Frederick Bloetscher, Ph.D., P.E.
2
Course 2 What did you learn in Course 1?
3
My Goals of Class
• Help YOU Gain an Understanding of:
–Operations/Maintenance
–Asset Management and Maintenance
–Programs You Need
–Finance
–Capital Planning
So Now That We Have Water,How Do We Make it Safe?
Let’s Look in More Detail at Processes…
High-Quality Groundwater Treatment Regime
6
Chlorination Distribution System
Well
Source: Bloetscher, 2008
Chlorine Bleach System
10
High Service Pumps
High-Quality Surface Water Treatment Regime
12
Surface Water
Pipe
Filtration
Chlorination Distribution System
Source: Bloetscher, 2008
Sand Filters
Chemical Mix Systems
17Source: Bloetscher, 2008
Medium-Quality Groundwater Treatment Regime
18
Lime
Lime Softening
Filtration
Chlorination Distribution System
Well
Source: Bloetscher, 2008
Lime Slurry Mixers
19
Source: Bloetscher, 2008
Lime Softening and Filters
20
Source: Bloetscher, 2008
Lime Softening
21Source: Bloetscher, 2008
Filters
25Source: Bloetscher, 2008
Medium-Quality Surface Water Treatment Regime
26
Surface Water Alum
Flocculation
Clarifier/Settling
Filtration
Chlorination
Distribution System
Chlorine
Contact
Source: Bloetscher, 2008
27
Flocculation Basin
How Settling Tanks Work…
28
Velocity
vo
Mix end – may be turbulent
Exit turbulence
WeirWater surface
Settled particles
Source: Bloetscher, 2008
Sand Filters
Low-Quality Groundwater Treatment Regime
30
Lime
Lime Softening
CO2
Filtration
Air-Strippers
Well Draft Air
Chlorination Distribution System
Source: Bloetscher, 2008
Low-Quality Surface Water Treatment Regime
31
Surface Water Alum
Flocculation
Clarifier/Settling
Carbon (GAC/PAC)
Filtration
Draft Air
Distribution System
Chlorine
Contact
Source: Bloetscher, 2008
MIEX System
Aeration Concept
33
Media
Water Down
Air Blown Up
Secondary Wastewater Treatment
40
Bar Screen
Aeration Basin
Chlorination
Disposal
Primary
Clarifer
Secondary
Clarifier
Chlorine
Contact
Source: Bloetscher, 2008
Used Membranes
Wastewater Treatment
42
Membrane Bioreactor
Advanced Secondary Wastewater Treatment (Reuse)
50
Filters
Bar Screen
Aeration Basin
Chlorine
Disposal
Primary
ClariferSecondary
Clarifier
Chlorine
Contact
Source: Bloetscher, 2008
Reuse Filters and Chlorination Basin
51
Reuse Effluent Pumping and Tank
52
Advanced Wastewater Treatment
53
Filters
Bar Screen
Aeration Basin
Nitrification
Chlorination
Disposal
Primary
Clarifer Secondary
Clarifier
Denitrif.
Tank
Chlorine
Contact
Dechlor-
rination
Source: Bloetscher, 2008
Anoxic Zone
54
Ultraviolet Light
Full Treatment
56
Filters
Bar Screen
Aeration Basin
Nitrification
CL Disposal
Primary
Clarifer
Second.
Clarifier
Denitrif.
Tank
CL
ContactDechlor
GACRO
Background: Standard of CareMulti Barrier Approach
• Microfiltration
• Reverse Osmosis
• Ultraviolet Advanced Oxidation
Ultraviolet Advanced Oxidation
Reverse Osmosis
Microfiltration
California Water Factory 21Groundwater Replenishment System
Process Flow Schematic
Phase II
Phase I
SP-2 After Microfilter SP-3 After
Reverse Osmosis
SP-4 After UV/AOP
SP-1 Influent to Pilot Plant
SP-1
SP-2
SP-3
SP-4
INFLUENT
Pilot Plant
Pembroke Pines Pilot Plant(Looking Northwest)
Pilot StudyMaximize the Recovery of the RO
• Configurations
Two stage 2:1 → 75% recovery
Three stage 2:1:1 → 82% recovery
Two Stage 2:1
Third Stage 1:1
Comparison of the permeate and third stage concentrate
Membrane Properties
Summary of Membrane Operating Parameters
Parameter DOW FilmTec Hydranautics Koch
Membrane Type Polyamide TFC Composite Polyamide TFC Polyamide
Maximum Operating Temperature 113 °F 113 °F 113 °F
Maximum Pressure Drop 15 psi 10 psi 10 psi
pH Range, Continuous Operation 2 - 11 2 - 11 4 - 11
pH Range, Short-Term Cleaning 1 - 13 1 - 13 2.5 - 11
Maximum Feedwater SDI 5.0 5.0 5.0
Free Chlorine Tolerance < 0.1 ppm < 0.1 ppm < 0.1 ppm
Nominal Active Surface Area 78 ft2 80 ft
2 85 ft
2
Recovery Rate 15% 15% 15%
Permeate Flow Rate: 2,400 gpd 2,000 gpd 2,370 gpd
Maximum Feedwater Turbidity 1.0 NTU 0.2 NTU
Stabilized Salt Rejection 99.5% 99.6% 99.55%
Membrane Specified BW30-4040 ESPA2-LD-4040 TFC-4040-HR
DOW Filmtec Test Operating Conditions: 2,000 ppm NaCl, 225 psig, 77°F, and 15% recovery
Hydranautics Test Operating Conditions: 1,500 ppm NaCl, 225 psig, 77°F , and 15% recovery
Koch Test Operating Conditions: 2,000 ppm NaCl, 225 psig, 77°F, and 15% recovery
Pilot StudyPost Treatment Stabilization
• pH
• Hardness
• Mineral content
• DO
• ORP
Stabilization Techniques
• Sodium hydroxide
• Hydrated lime
• Limestone filter
• Kiln dust
• Sodium metabisulfate
Pilot Study
Florida Atlantic University Spike Test
Group Overall % Removal
Pharmaceuticals 99.78%
Antibacterials 99.99%
Steroids & Hormones 99.95%
Protein Degradation Products 99.99%
Flame Retardants 99.89%
ESOC Treatment Evaluation
Trojan Technologies Spike Test
Parameter Goal Removal
NDMA 1.2-log (93.7%) > 2.76-log
1,4-dioxane 0.5-log (68.4%) > 1.83-log
Constituents SpikedChemical CAS MW Solubility pKa
Acetaminophen 130-90-2 151 14 mg/ml 9.51
Ibuprofen 51146-56-6 206 0.06 mg/ml 4.4
Estrone 53-16-7 270 1.30 mg/L 10.9
Triclosan 3380-34-5 289 0.012 g/L(1) 9.95(2)
Carbamazepine 298-46-4 236 17.7 mg/L 13.9
NDMA(3) 62-75-9 74 29 g/100 ml 3.52
1,4-D (Dioxane)(3) 123-91-1 88 ∞ N/A
TCEP 115-96-8 285 7.82 g/L N/A
Spiking the Water
Spike Removal Values
Spike Test #1 #2 #3 #1 #2 #3
Pharmaceuticals
Acetaminophen 91.30% 96.88% 97.27% 99.92% 98.32% 99.95%
Carbamazepine 85.00% 99.97% 99.91% 99.93% 99.98% 99.97%
Ibuprofen 85.37% 100.00% 99.92% 100.00% 100.00% 99.98%
Antibacterials
Triclosan 27.94% 97.50% 99.98% 100.00% 100.00% 100.00%
Steroids/Hormones
Estrone 67.70% 98.84% 99.99% 99.87% 100.00% 100.00%
Protein Degradation
N-Nitrosodimethylamine (NDMA) 62.77% 48.29% 39.01% 99.96% 99.90% 100.00%
Flame Retardants (Chlorinated Phosphates)
Tris(2-chloroethyl)phosphate (TCEP) 73.97% 99.77% 99.80% 99.89% 100.00% 99.83%
Other
1,4-D (Dioxane) 97.84% 94.28% 96.86% 99.89% 94.17% 97.80%
Average 73.99% 91.94% 91.59% 99.93% 99.05% 99.69%
Green indicates 3 log target is achieved
Percent Removal
Post RO Post UV
Study Results
• One of the goals of this study was to prove a3-Log (99.9%) reduction using an RO/UV/AOPtreatment train.
• Such reduction was obtained for all chemicalsexcept TCEP and 1,4-Dioxane which had acombined percent removal equivalent to99.83 and 97.80 percent respectively.
Orange County Does It
Big Springs Drinks It
Water Distribution System Consists of:
• Extensive underground systems of varyingtypes of:
–Pipe
–Valves, hydrants, and otherappurtenances
–Meters and service lines
• Various storage and pumping programs
• Maintenance of all parts on an ongoingbasis
73
Piping for Water - Issues to Consider
• Strength
• Exposure
• Chemicals/contaminant exposure
• Constructability
• Availability
74
For Example
• High tension forces = steel
• Compression forces = concrete
• Corrosive environments = avoid steel
• Dissimilar metals = always avoid
• Salts = Steel will corrode – need highergrades steel or plastic
• Active biology = not steel
75
Reinforced Concrete (Pressurized)
82
Department of Civil Engineering • F. Bloetscher, Ph.D., P.E. • ENV 4514 • Fall 2007
Recommended Water Distribution Programs
• Leak detection
• Water loss records
• Work order to track repairs, pipe condition
• System mapping
• Replacement programs
• Valve exercise & fire hydrant test programs
85Source: AWWA
Service Line and Meter
Storage Methods
86
Why Storage is Needed
87
Average Daily Flow
Pumping Out > Production
Pumping Out < Production (tanks fill)
Midnight Noon Midnight
Time of Day
Sewer Collection System Consists of:
• Extensive underground systems of varyingtypes of:►Pipe and piping materials►Manholes►Service lines►Pumping facilities
• Maintenance of all parts on an ongoingbasis due to high usage and highly corrosiveenvironment
88
Design ConsiderationsGravity sewers flow downhill
Designed to flow ½ full
Designed for maximum hourly flow (MHF)
Industrial or domestic
Infiltration/Inflow increases the flow volume
Location of groundwater table relevant
Sanitary Pipe Materials
• Small lines:
– PVC
– Vitrified clay
– Asbestos cement
• Larger lines:
– Ductile iron
– Asbestos cement
– Concrete
Types of Manholes?
• Doghouse Precast
• Drop
Brick
Manhole Design
92
bench
Source: City of Austin, TX
Stuff That Shouldn’t Be
93
Sewer Higher Than Water Sales?
0
1
2
3
4
5
6
Dec-99 Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08
Month
AD
F (
MG
D)/
Ra
infa
ll i
n I
nc
he
s
W-ADF
S-ADF
Inflow/Infiltration
95
Inflow/Infiltration
96
Flows Get Worse With Time
Inflow: Rainwater
Infiltration: Groundwater
What is
Inflow and Infiltration?
Rain and High Groundwater AffectsRain and High Groundwater Affects
Wastewater Collection SystemWastewater Collection System
Infiltration/Inflow (I/I)
InfiltrationGroundwater that enters the sewer through defective
pipes, joints, connections, & manholes
InflowStormwater discharged directly into sewers from roof
drains and stormwater runoff
Both overloads collection systems & reduces treatment efficiencyFunction of age
I/I = 0.009 – 0.9 m3/d∙mm∙km (typically)
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.000
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
4,000,000
Se
we
r Fl
ow
Date
Flow (Gallons)
Rainfall
Rain
fall
Flow Data vs. Rainfall
Inflow = Rain
Sewer Flows v. RainfallWe can see…..INFLOW!!!
0
5
10
15
20
25
Dec-99 Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08
Month
AD
F (
MG
D)/
Rai
nfa
ll in
Inch
es
S-ADF
Rainfall
WATERWAY
FOOTING
DRAIN SANITARY
SEWER
STORMWATER
SEWER
SUMP
PUMP
HOUSE
SEWER
(LATERAL)
DOWN-
SPOUT
STREET
INLET
INFILTRATION
STORMWATER
CONNECTIONS
ALLOW INFLOW SANITARY SEWERS
OVERFLOW TO
WATERWAYS
BASEMENT
BACKUP
Inflow =>Sanitary Sewer Overflow
Sanitary Sewer Overflow
• Sewer flow peaks during rain events.
• The size of the storm event matters. A larger storm event will create more inflow.
• During storm events, wastewater flow is linearly related to rainfall.
• Based on an analysis of flow vs. rainfall graphs from pre and post construction activities inflow reduction will be calculated.
Inflow
Broken Pipe = Infiltration…
Broken Pipe
True Inflow vs Infiltration
112
Vacuum Sewer Design
113
A traditional gravity line carries wastewater from the customer to a valve pit package.
When 10 gallons of wastewater collects in the sump, the valve opens and differential pressure propels the contents into the vacuum main.
Wastewater travels at 15 to 18 fps in the vacuum main, which is laid in a sawtooth fashion to insure adequate vacuum levels at the end of each line.
Wastewater enters the collection tank. When the tank fills to a predetermined level, sewage
pumps transfer the contents to the treatment plant via a force
main.
Vacuum pumps cycle on and off as needed to maintain a constant level of vacuum on the entire collection system.
Source: Partlow, 2007
Ongoing Sewer Collection System Maintenance Programs
• Infiltration and inflow correction
• Monitor pumpage
• Work orders to track repairs, pipe condition
• System mapping
• Repair and replacement programs
114
Deterioration of Infrastructure
Maint
Cost
Pipelines
Mechanical Equipment
Time
Rehab Provides Only So Much
116
Percent Refurbish 1
of Original Refurbish 2
Value
(Present Initial Lifecycle
Worth)
Extended Life
Time
Note - refurbishment may occur later in life before full devaluation takes place.
Refurbishment never reaches initial condition.
Extension of life of asset decreases with each refurbishment.
What Are Maintenance Concerns?
• Age• Structural
breakdowns• Wear• Deterioration due to
– Corrosion damage –Water quality–Water stability–Material breakdown
So What Creates the Problem?
• Technology changes
• Use Changes/repurpose
• Environmental effects–Galvanic action/corrosion
–Unstable water/solutions
–Biofouling
• Failure to maintain/inspect/repair
118
What Is Asset Management?
• Business process and a decision-makingframework that covers an extended timehorizon, draws from economics as well asengineering, and considers a broad rangeof assets
• Asset management is part of maintenance
• And part of management
Asset Management is Not New
• What changed?–Construction to
operations
–Materialinstallation torepair
• Not glamorous likeconstruction
Asset Management Has Come of Age Because of…
• Condition of the infrastructure
• Increased potential for water andsewer system disruptions
• Overflows and breaks that disruptservice
• Changes in public expectations
• Advances in technology
Issues
• Certainty – this is what engineers like todeal with, but stationarity is “dead”
• Risk – probability of things happening ornot
• Uncertainty – things you don’t haveanswers to – may be the largest data
Process
Determine Goals &
Objectives
Inventory of Assets
System Review & Assessment
AMP
ImplementationQuality
Assurance
Performance Feedback
123
What Does All This Cost?????
• It is ongoing work
• It cannot be delayed indefinitely
• It cannot be ignored
• Delays = dollars later
• Failure is not an option
• Water system >$1 trillion over 20 years
124
$
Minimum Requirements
• Adaptable
• Efficient
• Practical
• Decisionsupported
• Good feedback
Condition Index• Variety of Factors
• Yields Result
• If we know the incident, we can find theweights
• CI = w1C1+w2C2+w3C3+w4C4+…wiCi
• Where:
– CI = Condition index
– W = weighting factor
– C is condition factor
Concept
• Linear constraints are equalities withthe following form:
• Or inequalities with the followingform:
127
1332211 ... bxaxaxaxa niniii
1332211 ... bxaxaxaxa niniii
1)( dxxf
128
If You Don’t Have Much Data…Bayesian Methods Might Work
Unconditional Probability Density Function
dpxpxp )()|()(
129
where p(x) and p(q) are completely different and independent functions and the function p(q) is the prior distribution
absolute or unconditional probability density function p(x) on X is the underlying distribution found through curve-fitting
What is p(x)? Or b?
It must be a measurable incident or response
So We Need to Know What That Response is
• Water main breaks?
• Sewer breaks? Overflows?
• Stormwater??
• Irrigation breaks?
132
Step 1 Create a table of assets Step 2 Create columns for the variables for which you have data Step 3 Categorical and numerical variable do not provide appropriate comparisons; hence the need to alter the categorical variables to absence/presence variables. Step 4 Summarize the statistics for the variables. .Step 5 Develop a linear regression to determine factors associated with each and the amount of influence that each exerts. Step 6 Identify the predictive equation. Step 7 The equation can then be used to predict the number ofbreaks going forward.Step 8 Finally the data can be used to predict where the breaksmight occur in the future based on the past
Let’s Look at a Water System• Pipe size
• Estimate age form aerials, as-builts, development patterns, etc.
• Condition based some on age, as-builts, field crews
• Soils (USDA), can assume better soil put back in
• Pressure (55 psi)
• Traffic (road it is on)
• Trees (GoogleEarth – going out and looking)
• Rumor (what do crews think)
• On-site analysis (over all the others)
• Others….
133
Step 1 - Create a Spreadsheet of Assets
Asset Diameter
water main 2
water main 2
water main 2
water main 2
water main 4
water main 6
water main 6
water main 6
water main 6
Step 2 – Identify What You Know
Assetbreaks in 10 year Dia Age soil traffic Trees depth pressure material est Cond
water main 17 2 45 1 1 2 1 55 4 3water main 11 2 45 2 1 2 1 55 4 3water main 12 2 45 1 1 2 1 55 4 3water main 10 2 45 1 1 2 1 55 4 3
water main 2 4 50 1 1 2 1 55 1 2water main 3 6 60 2 2 2 1 55 1 2water main 1 6 60 2 2 2 1 55 1 2water main 1 6 60 2 2 2 1 55 1 2water main 0 6 20 1 1 2 1 55 3 1
135
AssumptionsKeySoil 1 = sand 2 = muckDepth 1 =<6 2>6
Trees1 = no, 2 = yes
Traffic 1= low 2 = high
Material1 = Di 2 = GI 3 =C900 4 = AC 5 = hdpe
Est Cond1= good 2 = fair 3 = poor
136
Step 3 – Convert Descriptive Info to Numerical
Asset
breaks in
10 year Dia Age Sand Clay
Low
Traffic
Heavy
Traffic Trees
no
trees
shallow
unde 6
deep
bury pressure
DIP
CI GI PVC AC
HDP
E
water main 17 2 45 1 0 1 0 1 0 1 0 55 0 0 0 1 0
water main 11 2 45 0 1 1 0 1 0 1 0 55 0 0 0 1 0
water main 12 2 45 1 0 1 0 1 0 1 0 55 0 0 0 1 0
water main 10 2 45 1 0 1 0 1 0 1 0 55 0 0 0 1 0
water main 2 4 50 1 0 1 0 1 0 1 0 55 1 0 0 0 0
water main 3 6 60 0 1 0 1 1 0 1 0 55 1 0 0 0 0
water main 1 6 60 0 1 0 1 1 0 1 0 55 1 0 0 0 0
water main 1 6 60 0 1 0 1 1 0 1 0 55 1 0 0 0 0
water main 0 6 20 1 0 1 0 1 0 1 0 55 0 0 1 0 0
Step 4 – Summary Statistics
138
Table 4 Summary statistics:
Variable
Observa-
tions
Obs. with
missing data
Obs. without
missing data
Mini-
mum
Maxi-
mum Mean
Std.
deviation
breaks in 10 year 93 0 93 0.000 17.000 1.398 2.949
Dia 93 0 93 1.000 16.000 5.011 3.255
Age 93 0 93 5.000 60.000 29.194 18.212
Sand 93 0 93 0.000 1.000 0.946 0.227
Clay 93 0 93 0.000 1.000 0.054 0.227
Low Traffic 93 0 93 0.000 2.000 0.968 0.231
Heavy Traffic 93 0 93 0.000 1.000 0.043 0.204
Trees 93 0 93 0.000 1.000 0.903 0.297
no trees 93 0 93 0.000 1.000 0.215 0.413
shallow under 6 ft
deep 93 0 93 0.000 1.000 0.849 0.360
deep bury 93 0 93 0.000 1.000 0.032 0.178
pressure 93 0 93 55.000 65.000 55.323 1.616
Ductile 93 0 93 0.000 1.000 0.419 0.496
GI 93 0 93 0.000 1.000 0.054 0.227
PVC 93 0 93 0.000 1.000 0.247 0.434
AC 93 0 93 0.000 1.000 0.065 0.247
HDPE 93 0 93 0.000 5.000 1.075 2.065
Step 5 – Linear Regression
• Means to derive an equation and fitfor the data
• Highlights how much the variability isdefined by the data
139
Steps 5&6 – Linear Regression and Model Parameters
Model parameters:
Source Value
Standard
error t Pr > |t|
Lower bound
(95%)
Upper bound
(95%)
Intercept -12.355 6.805 -1.816 0.073 -25.901 1.190
Dia -0.489 0.102 -4.795 < 0.0001 -0.692 -0.286
Age 0.008 0.012 0.725 0.471 -0.015 0.032
Sand 3.144 0.891 3.528 0.001 1.370 4.918
Clay 0.000 0.000
Low Traffic 1.151 1.546 0.744 0.459 -1.926 4.227
Heavy Traffic 5.961 2.107 2.830 0.006 1.768 10.154
Trees -2.819 1.236 -2.280 0.025 -5.280 -0.359
no trees 0.297 1.134 0.262 0.794 -1.959 2.554
shallow under
6 0.580 1.034 0.561 0.576 -1.478 2.639
deep bury 0.000 0.000
pressure 0.194 0.119 1.625 0.108 -0.044 0.431
Ductile 2.342 0.640 3.658 0.000 1.068 3.617
GI 5.473 0.598 9.151 < 0.0001 4.282 6.663
PVC 3.229 0.717 4.501 < 0.0001 1.801 4.657
AC 12.428 0.757 16.408 < 0.0001 10.920 13.935
HDPE 0.000 0.000
Step 6 – Impact of Model Parameters
Dia
Ag
e
San
d
Cla
y Lo
w T
raff
ic
Hea
vy T
raff
ic
Tre
es
no
tre
es
shal
low
un
de
6
dee
p b
ury
pre
ssu
re Du
ctile
GI P
VC
AC
HD
PE
-1
-0.5
0
0.5
1
1.5
Sta
nd
ard
ize
d c
oe
ffic
ien
ts
Variable
breaks in 10 year / Standardized coefficients(95% conf. interval)
Step 7 – Predictions by Observation
-4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5
Obs1
Obs6
Obs11
Obs16
Obs21
Obs26
Obs31
Obs36
Obs41
Obs46
Obs51
Obs56
Obs61
Obs66
Obs71
Obs76
Obs81
Obs86
Obs91
Standardized residuals
Ob
serv
ati
on
s
Standardized residuals / breaks in 10 year
Step 7 – Breaks in 10 Year/Standardized Residuals
-3
-1
1
3
5
0 5 10 15 20
Sta
nd
ard
ize
d r
esi
du
als
breaks in 10 year
breaks in 10 year / Standardized residuals
143
Predicted Breaks in 10 Years
144
-2
0
2
4
6
8
10
12
14
16
18
-2 0 2 4 6 8 10 12 14 16 18
bre
ak
s in
10
ye
ar
Pred(breaks in 10 year)
Pred(breaks in 10 year) / breaks in 10 year
Try A Real System Sanitary Sewers in Dania Beach
What We Know
• Age 30, 40, 50 yrs
• VC pipe
• DI FMs
• Front Yard ROW
• Under pavement
• Low use pavement
• Virtually all 8”
• We have TVing
Steps 1&2
Asset
Given
Asset ID Material Age
Traffic
Loading Diameter Depth Length
Pipe
Breaks
8" Gravity SS 1 1 0 0 0 1 1 1
8" Gravity SS 2 1 0 0 0 1 1 1
8" Gravity SS 3 0 1 0 0 1 1 0
8" Gravity SS 4 0 1 0 1 1 1 0
8" Gravity SS 5 1 0 0 1 1 1 0
8" Gravity SS 6 0 1 0 1 1 1 0
8" Gravity SS 7 0 1 0 1 1 1 0
8" Gravity SS 8 0 1 0 1 1 1 0
8" Gravity SS 9 0 1 0 1 1 1 0
Results
Material
AgeTraffic Loading
Diameter
Depth
Length
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
Sta
nd
ard
ize
d c
oe
ffic
ien
ts
Variable
Pipe Breaks / Standardized coefficients(95% conf. interval)
0.073* 0.089* 0.078* 0.066* 0.003*
.0.11*
Likelihood of breaks material age Loading diameter d
epth length
Compare Results
Issues
• Information of tracking breaks waslimited
• Only data was 2012, older data lost
• Did not differentiate services vs mains
• We need a consequence which is whythe water system was not done
Questions
Pre-Conference Assignment
• Bring your budget
• Identify # of employees
• # accounts w/s
• Miles of pipe w/s
• # Lift stations, treatment,
• Gallons/yr of water, wastewater
152
Discussion of Your System
• Flows
• Piping
• Lift stations
• Customers
• Rates
• Let’s compare…
• What did we learn?