Download - Wastewater Collection System Rehab
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OHM-ADVISORS.COM
Wastewater Collection System Rehab
The High Hurdle of Measurable Flow
Reduction
MWEA 2013 Annual Conference
Gregory P. Kacvinsky, P.E.
OHM Advisors
ARCHITECTS. ENGINEERS. PLANNERS.
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Investing to Reduce Flows
Identify System
Problems
Flow Metering
Modeling and
Analysis
Evaluation of
Alternatives
Implement CIP
Measure Success or
Identify Failures
When alternatives
are evaluated:
Do we really
understand all
potential costs for
each alternative?
How (and where)
will we measure
success?
Do we really
understand the cost
efficiencies of all
alternatives?
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Why Do We Invest?
Sewer pipes and
manholes are
deteriorating
More stringent wet
weather requirements at
WWTP
Documented SSOs,
Consent Orders
Desire to reduce bill to
wastewater treatment
authority (i.e. DWSD)
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How Do We Invest?
Source Removal (pipe/lateral
rehab, manhole repair,
replacement)
Wet weather storage
Transport and treat
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Source Removal 101
How do you measure flow
reduction success?
Volume?
Peak?
Baseflow?
Where do you measure flow
reduction?
WWTP?
Collector sewer?
Interceptor sewer?
Measuring flow reduction at a
regional level: hard to demonstrate
Measuring flow reduction at a
neighborhood level: easier to
demonstrate
Misconceptions on flow reduction
from rehab
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Source Removal 101
Important to set the goals and manage
expectations before a source removal
program
• What percentage of peak and
baseflow do we need to reduce? Is
one or the other more important?
• How much flow meter history do we
have prior to rehab?
• What is the plan for post-rehab flow
metering?
• Who will evaluate pre- vs. post-
rehab success? What method(s)
will they use?
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Case Studies in I/I Removal
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Case Study Communities
Municipalities tributary to key
interceptors flowing into regional
systems
• Individual communities pay
bills to regional authority
based on metered flows
• Total bills based on total
measured flow volume
• Additional charges assessed
for exceeding contractual
peak flows
• Dual pressures (local billing
and MDEQ ACOs)
Wealth of flow data
Motivation to reduce I/I
flows
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Example 1
• Initiated sewer/manhole rehab
program to reduce measured
flows and wet weather peaks
• Rehab was performed in
various neighborhoods, mostly
older sewers
• Several years of pre-rehab
flow meter data (continuous)
• About 2 years of post-rehab
flow meter data
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Example 1
Methods of Analysis
• Community predicted nearly 10 cfs
reduction in wet weather design
flow due to typical values for pipe
lining and manhole rehab
• 13-15 miles of pipe rehabilitated
• ~600 laterals partially lined
• ~150 manholes rehabilitated
• Defect repairs (from smoke testing)
• Independent analysis
• Baseflow analysis
• Event RDII volume vs. rainfall
• Meter Correlation
• Antecedent Moisture Modeling
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Example 1
Baseflow Analysis
WAT Community Flow
Long-Term Baseflow Patterns (2007-2011)
5
6
7
8
9
10
11
12
8-Mar 27-Apr 16-Jun 5-Aug 24-Sep 13-Nov
Date
Bas
efl
ow
(cf
s)
2007
2008
2009
2010
2011
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Example 1
RDII Volume v. Rainfall – Total Community
WAT Community Flow
Total RDII Volume vs. Total Event RainfallPre- vs. Post-Rehab (May - September data only)
10
100
1000
10000
0 1 2 3 4 5 6
Total Event Rainfall (inches)
Tota
l RD
II V
olu
me
(th
ou
san
d
cub
ic f
ee
t)
Pre-Rehab(2006-2008)
Post Rehab(2010-2011)
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Example 1
RDII Volume v. Rainfall – Subdistrict Analysis
Flow Meter 1590 (WAT)
Total RDII Volume vs. Total Event RainfallPre- vs. Post-Rehab (May - September data only)
10
100
1000
0 1 2 3 4 5
Total Event Rainfall (inches)
Tota
l RD
II V
olu
me
(th
ou
san
d
cub
ic f
ee
t)
Pre-Rehab(2007-2008)
Post Rehab(2010-2011)
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Example 1
Meter Correlation
Meter correlation measures the wet weather
response of a rehabilitated meter district against
that of a control (non-rehabilitated) district
-0.5
0.0
0.5
1.0
1.5
2.0
2006
2007
2008
2009
2010
2011
Co
rrela
tio
n F
acto
r
Correlation Multiplication Factor
Correlation Additive Factor (baseflow adjustment in cfs)
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Example 1
Continuous Modeling
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
8/19 8/20 8/21 8/22 8/23 8/24
Flo
w (
cfs
)
0
1
2
3
4
Rain
(in
/hr)
Observed
Modeled
Rain
6.0
8.0
10.0
12.0
14.0
16.0
18.0
3/4 3/5 3/6 3/7 3/8 3/9
Flo
w (
cfs
)
0
1
2
3
4R
ain
(in
/hr)
Observed
Modeled
Rain
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
3/20 3/21 3/22 3/23 3/24 3/25 3/26 3/27
Flo
w (
cfs
)
0
1
2
3
4
Rain
(in
/hr)
Observed
Modeled
Rain
Pre-rehab calibration
Post-rehab validation
Model predicts RDII Volume to within 1% of observed
Model under-predicts observed RDII volume by 2.5%
Continuous modeling reveals that there is no
systematic under- or over-prediction when
using pre-rehab calibrated model against post-
rehab conditions
This suggests NO CHANGE in wet weather response
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Example 1
Findings
• Rehab did not have a
measureable impact on peaks
or volumes
• Rehab effort was scattered,
and difficult to measure
success on a community level
• No positive economic benefit
(quarterly bills not reduced)
• Estimates used for flow
reduction from rehab
activities may be flawed
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Example 2
Bloomfield Orchards Subdivision
(Auburn Hills)
• ~450-500 homes
• Footing drain disconnection for
every home
• Several years of pre-rehab flow
meter data (continuous)
• About 3 years of post-rehab flow
meter data
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Example 2
Methods of Analysis
• Independent analysis (County)
• Baseflow analysis
• Event RDII volume vs. rainfall
• Meter correlation
• Continuous modeling
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Example 2
Baseflow Analysis
Bloomfield Orchards Subdivision
Long-Term Baseflow Patterns (2000-2006)
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
1-Mar 20-Apr 9-Jun 29-Jul 17-Sep 6-Nov
Date
Bas
efl
ow
(cf
s)
2000
2001
2002
2003
2004
2005
2006
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Example 2
RDII Volume v. Rainfall
Bloomfield Orchards Subdivision
Total RDII Volume vs. Total Event RainfallPre- vs. Post-Rehab (May - September data only)
1
10
100
0 0.5 1 1.5 2 2.5 3
Total Event Rainfall (inches)
Tota
l RD
II V
olu
me
(th
ou
san
d
cub
ic f
ee
t)
2000-2001
2005-2006
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Example 2
Continuous Modeling
0.0
0.5
1.0
1.5
2.0
2.5
8/2 8/2 8/3 8/3 8/4 8/4
0
1
2
3
4
Observed
Modeled
Rain
0.0
0.5
1.0
1.5
2.0
2.5
3.0
9/10 9/10 9/10 9/10 9/10 9/11 9/11 9/11
Flo
w (
cfs
)
0
1
2
3
4
Rain
(in
/hh
r)
Observed
Modeled
Rain
0.0
0.5
1.0
1.5
2.0
2.5
6/21 6/21 6/22 6/22 6/23 6/23
0
1
2
3
4
Observed
Modeled
Rain
0.0
0.5
1.0
1.5
2.0
2.5
5/10 5/11 5/11 5/12 5/12 5/13
Flo
w (
cfs
)
0
1
2
3
4
Rain
(in
/hr)
Observed
Modeled
Rain
Pre-rehab
calibration
Post-rehab
validation
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Example 2
Findings
• Rehab had a significant
impact on measured flows and
volumes
• Success is easier to measure
when rehabilitation occurs
aggressively over entire
metered district
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Example 3
• Aggressive sewer, manhole,
lateral rehab over 3-year period
• $15-$16 million investment
• 2 years of pre-rehab flow meter
data (continuous)
• 12 months of post-rehab flow
meter data
• Post-rehab data is 2012-2013
(historically-dry conditions)
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Example 3
Methods of Analysis
• Local analysis
• Model calibration to 2012 storms
(July and August)
• Analysis shows significant
reduction in wet weather flows
• Independent analysis
(Regional Authority)
• Baseflow analysis
• Event RDII volume vs. rainfall
• Continuous Modeling
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Example 3
Baseflow Analysis (>25% reduction)
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
Sep-06 Mar-07 Sep-07 Mar-08 Sep-08 Mar-09 Sep-09 Mar-10 Aug-10 Feb-11 Aug-11 Feb-12 Aug-12 Feb-13 Aug-13
Community Master Meter7-day minimum flows (BASEFLOW)
2006-2013
Pre-Rehab
Post-Rehab
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0.300
0.350
0.400
0.450
0.500
0.550
0.600
0.650
0.700
0.750
0.800
Jan-07 Jul-07 Jan-08 Jul-08 Dec-08 Jun-09 Dec-09 Jun-10 Dec-10 Jun-11 Dec-11 Jun-12 Dec-12 Jun-13
Control District (No Rehab)7-day minimum flows (BASEFLOW)
2006-2013
Example 3
Baseflow Analysis (non-rehab district, >20% reduction)
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Example 3
RDII Volume v. Rainfall
100
1000
10000
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Tota
l RD
II V
olu
me
(th
ou
san
dcu
bic
fe
et)
Total Event Rainfall (inches)
Community Master MeterTotal RDII Volume vs. Total Event Rainfall
Pre- vs. Post-Rehab (April - October data only)
2006-2009
2011-2012
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Example 3
RDII Volume v. Rainfall
1
10
100
1000
0 1 2 3 4 5 6
Tota
l RD
II V
olu
me
(th
ou
san
dcu
bic
fe
et)
Total Event Rainfall (inches)
Control District (No Rehab)Total RDII Volume vs. Total Event Rainfall
Pre- vs. Post-Rehab (April - October data only)
2006-2009
2011-2012
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Example 3
Continuous Modeling
• 2012 data show a “nudging of
the needle” to reduced peaks
and volumes
• 2013 data (only 3 storms)
show similar response to pre-
rehab conditions
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Example 3
Findings
• Early data suggests little to
no flow reduction (peak and
baseflow)
• More data needed to confirm
this hypothesis (meter data
through 2013 would be ideal)
• Success is easier to measure
when rehabilitation occurs
aggressively over entire
metered district
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Limits of Source Removal
Chasing I/I is difficult. This is what
we’re learning by observing
numerous City-wide efforts at I/I
reduction:
• Problems migrate to next
downstream defect
• Sanitary sewer acts as an
“unofficial” dewatering pipe for
groundwater in developed
areas
• ENTIRE system (Pipes, MHs
and laterals) needs to be
rehabilitated to have a
measurable impact (VERY
EXPENSIVE)
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Wet Weather Alternatives
0.4
$0
$100,000
$200,000
$300,000
$400,000
$500,000
$600,000
$700,000
$800,000
$900,000
$1,000,000
0 0.5 1 1.5 2 2.5
Storage Cost Curve
Inflow Source Removal
Manhole Rehab
Private I/I Sources (FDD)
Notes1. Flow removal based on SSES report estimates2. Assumes a flow removal of 4 gpm per home at $10,000 cost per home
Cost to remove 0.40 cfs of private sources (e.g. FDD) = $897,600Cost to store 0.40 cfs = $122,000
Cumulative Peak Flow Removed (cfs)
Cu
mu
lati
ve C
apit
al E
xpe
nse
(U
SD)
Cost to remove 0.96 cfs of direct inflow sources = $40,000Cost to store 0.96 cfs = $294,000
Cost to remove 0.81 cfs of manhole sources = $365,000Cost to store 0.81 cfs = $250,000
Cost to Construct 10.6 MG to store excess peak flow (43 cfs) = $12.2 M
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Economics of System Rehab
Rehabilitation may be sought for the
wrong reasons. It is ultimately
about:
• Structural integrity of pipes and
manholes
• Adding 20+ years to pipe
longevity through lining
• Lower O&M costs through
reduced frequency of
emergency repairs
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Literature Review
Reviewed specific programs across the
country
Read technical/academic papers on wet
weather flows in wastewater collection
systems
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Municipal Experience
Urbana-Champaign Sanitary District (UCSD), IL
Naperville, IL
Concord, MA
Various municipalities, Oakland County, MI
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European Communities
Dresden University Study (2007)
Study of communities in UK, Germany, Switzerland
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European Communities
Non-sanitary flows are the norm in separate
wastewater collection systems
(30%-50% of total sewer flows are typical)
Typically, about 5% to 10% of total I/I can be
cost-effectively removed
Aggressive rehabilitation programs have
mixed results. Often, only complete rehab
programs can “nudge the needle”
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Key Takeaways
Many communities have invested in significant
rehabilitation programs with little to no
measureable peak or volume reduction
Flow meter data is often the weakest link –
flow metering should be extended well beyond
rehab period for statistically-meaningful data
The key benefit of sewer rehabilitation may be
structural (extending the life of the asset)
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Financial-Driven Decisions
• What is the economic impact of reduced
baseflows or peaks? Present value?
• What is the most economically-efficient way
to reduce I/I?
• Is a regional solution possible (i.e. storage
or conveyance)?
• Local rate payers ultimately will expect the
most cost-effective solution
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Recommended Steps
• Know the goals - set appropriate hurdles
• Establish a reliable and long-term metering
program (extend beyond rehab period)
• Fully vet the wet weather control options at
your disposal
• Don’t overpromise
• Asset Management Plans – great opportunity
to put these steps to the test
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OHM-ADVISORS.COM
Questions
Contact info:
Gregory P. Kacvinsky, P.E.
OHM Advisors
ARCHITECTS. ENGINEERS. PLANNERS.