The use of PI and SigmaFine in the Water Industry S

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Transcript The use of PI and SigmaFine in the Water Industry S

The use of PI and SigmaFine
in the Water Industry
B. D. Neve
Rex
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Introduction
• In the last three decades water has increased dramatically in value and
cost in both the clean water and waste utilities.
• This is due to a number of factors, in particular environmental issues,
population growth, urbanization, and in some areas climate change.
• Water will never again be a free commodity, and indeed the cost is
likely to go on rising at an even faster rate.
• The Water and Waste utilities now realize that the implementation of
industry standard control, SCADA, plant information and flow
accounting are now fully justifiable and that data quality is vital to the
stewardship of their valuable water assets.
• The paper covers the real $ benefits of implementing PI and SigmaFine
to cover flow balancing and accounting and data quality improvements
which can be over 2 million $ per year for a 5 million household utility.
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Overview
• The PI implementation data in this paper comes from the
experiences of Instem Beaver Valley, the distributor of PI in the
UK
• The SigmaFine data comes from the experiences of the author
on 1 pilot and 2 commercial projects implementing SigmaFine in
the Water industry to improve data quality.
• In addition to work on the projects, the author conducted a
study involving 6 large water companies in the UK on the $
benefits of improving data quality via data reconciliation
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Typical 80,90 and 2000 operational IT structure
Users
Custom centralised OMS system
Scada systems
Communications: Radio,
PSTN, Satellite, Wide area
networks, VPNs
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Custom communications
to custom user interfaces
Treatment plants
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Problems with the old
Architecture
• To much customised hardware and software
• Supplier locks user into high costs and low
performance compared to new solutions
• Difficult and expensive to expand
• knowledge of the system disappears which leads
to misuse of data and degradation of data quality
• Result is low data quality to the user
• Bad business decisions,
• E.g.money spent on wrong meters, costly manual studies
to give robust water balances and on leakage detection
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The Solution
• Change OMS level to PI based standard systems
• Install SigmaFine to audit, test improve data
quality
• Migrate SCADA to standard PC and Fieldbus
based systems over time
• This paper majors on improving data quality since
such a project can have a significant hard ROI
which can help pay for the other two enabling
technologies
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The Final Goal
Overall reconciliation
Distribution Input
Flow balance
reconciliation around
storage and distribution
reconciled,
accurate,
consistent
and
auditable
information
stakeholders
Supply and plumbing losses
Water taken legally unbilled
Distribution losses
Water taken illegally unbilled
Flow balance around treatment plants
Water delivered billed un-measured
Water delivered billed measured
Leakage monitoring and reduction systems
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The use of PI
in the Water Industry
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Slide courtesy of Instem
Beaver Valley
PI In Use at Southern Water
Telemetry Archive
-------------------------100,000 PI points
MTDB, PIQA & Tag Group Database
WADIS & Report Manager
PI-SCOPE Interface
PI-CMS Interface
Derived Values Archive
--------------------------------5,000 PI points
Derived Values Calculations
Task Scheduler
Exception Notification
Servelec Regional
Telemetry / SCADA
SCOPE-X 4,000 Remote Sites
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Slide courtesy of Instem
Beaver Valley
Telemetry Archive System
Installed in late 1998 to replace P.ARCH and to provide:
•
•
•
•
Easy access to telemetry data
Larger SW audience
More accurate and complete data
Process data in a time frame meaningful to business
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Slide courtesy of Instem
Beaver Valley
Telemetry Archive System
Server
100,000 Point PI Data Archive
Oracle
PI Quality Archive
PI SCOPE Interface
PI CMS Interface
Master Translation Database
Tag Group Database
Report Database
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Client
PI-ProcessBook
PI-DataLink
PI-Manual Logger
Archive Edit
PIQA Viewer
End-to-End Test Logging
Report Manager
Data View
Tag Manager
Tag Group manager
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Slide courtesy of Instem
Beaver Valley
Derived Values Archive
Installed in summer 2000 to provide:
• Process Management & Water Resources Information
System
• Integration of data from a number of sources:
• PI-UDS, Operational Database, WAACS, ISIS, QXP, …
• Derivation of meaningful performance indicators of
treatment processes (PM)
• Maintenance of customer supplies using current
hydrometric and antecedent conditions (WRIS)
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DVA Calculations
Slide courtesy of Instem
Beaver Valley
Telemetry
PI Archive
Microsoft Excel
(PI-API)
Operational
Database
25 Calculation Functions
Multiple function calls
Multiple sheets
Exception Report
(ODBC)
Microsoft Task
Scheduler
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DVA PI
Archive
PI-API
Remote
Task
Scheduler
Manager
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Slide courtesy of Instem
Beaver Valley
Thames Water PI System
Server
10,000 Point PI Data Archive
ABB Aqua Master Interface
ABB Gateway
Radcom Interface
Radcom Gateway
DMA Function Sets
MeterMan
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PI-ProcessBook
PI-DataLink
Archive Edit
PIQA Viewer
Report Manager
Data View
Tag Manager
Tag Group manager
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PI in the water industry
• PI is being used in the UK to supplement and
replace the existing OMS systems
• As existing OMS systems become more
expensive and difficult to maintain more
systems will transfer to cross industry
standard systems such as PI
• Meanwhile PI becomes an enabler for
SigmaFine data reconciliation for improved
data quality
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PI in the water industry
• Lessons Learnt:
• PI copes well with the requirements for
• Flexibility
• Expandibility
• PI is a cost effective purchase for water
companies in terms of
• Initial capital cost
• Whole life cost
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Improving Data Quality
in the Water Industry
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The Problems that arise from
inadequate data (1)
• Unreliable leak detection and
estimation
• Water balancing: non closed balances,
too much guess work, and lack of
consistent history
• Investment decisions based on
inaccurate and inconsistent data
• Difficulties in describing the networks
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Problems that arise from
inadequate data (2)
•
•
•
•
Little knowledge of meter accuracy, drift or
bias
Different data in different parts of the
company
Unknown operational/process
performance
Arguments over shared asset agreements
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The Problems that arise from
inadequate data (3)
• Unaccounted for flows
• Difficult and resource consuming
reporting to Water Regulator
• Difficulties supporting arguments
during billing disagreements
• Difficulties in justifying increased
monitoring or improved measurements
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UK Regulator Ofwat reporting
requirements
 Section 2 Chapter 10 of July Return Reporting
requirements& definitions manual


Water delivered forms the majority of the water balance. A company's
approach to Table 10 can validate any assumptions used to estimate
water delivered components. Ofwat encourages companies to
estimate each component of distribution input and compare the
sum of these with measured distribution input. Where there is a
small discrepancy (say less than I or 2%) this can be allocated to
those components with the greatest uncertainty.
A large
discrepancy suggests that a review of a company's estimating process
is required, as it is clearly not satisfactory for a company to be unable
to account fully for its major product.
The company should give an explicit explanation of any reconciliation
adjustment, indicating which water balance components have received
the adjustment using the Maximum Likelihood Estimation method.
Where the company's estimating process has been reviewed the
company should provide a full briefing; outlining the degree of the
discrepancy, which components were reviewed, what assumptions
were altered, and is so why, and which water balance components
needed improvement.
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Ofwat reporting requirements

To estimate distribution losses (Mld) companies should use the
Integrated Flow Method. The resultant leakage level should then be
checked against monitored night flows. Companies should therefore
use the Integrated Flow Method and the Minimum Night Flow Method in
conjunction, as a means to substantiate their estimation of leakage.

Ofwat would also encourage companies to support estimates with
effective data monitoring systems; an example would be a domestic
consumption monitor used by Severn Trent Water to support their
estimate of unmeasured household per capita consumption.

Ofwat would also expect to see the impact of metering on some water
delivered components:
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Ofwat reporting requirements

Distribution input (Mld

Reliability Grade A
The sum of the separately estimated water balance
components reconcile with the measured volume of distribution input to within 1-2%.
There has been no adjustment made to measured distribution input other than as a result
of the aforementioned reconciliation; that is, the sum of the water balance components
with measured distribution input. Measured distribution input has been estimated from
water-into-supply meters which record 95% of the volume of distribution input, and the
meters have been used and regularly recalibrated in accordance with the manufacturers
recommendations.

Reliability Grade B
The sum of the separately estimated water balance
components reconcile with the measured volume of distribution input to within 5% but not
to within 2%. There has been no adjustment made to measured distribution input, other
than as a result of the aforementioned reconciliation; that is, the sum of the water balance
components with measured distribution input. Measured distribution input has been
estimated from water-into-supply meters which record 90% of the volume of distribution
input, and the meters have been used and regularly recalibrated in accordance with the
manufacturers recommendations.
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Ofwat reporting requirements

Overall water balance

Reliability Grade A
The water balance components reconcile with
measured distribution input to within 2%. An explicit explanation for any
reconciliation adjustment is given and an adjustment has been made to
distribution input or has been distributed between water balance components.
Water-into-supply meters have been used and recalibrated in accordance with
the manufacturers recommendation. The water balance components have been
separately estimated and reconcile with the equivalent residual of the water
balance. 90% of the volume of distribution input (not including distribution input)
has been awarded a reliability band of A or B within the separately estimated
water balance components.

Reliability Grade B
The water balance components do not reconcile
with measured distribution input to within 5%, hence an adjustment has
been made to distribution input or has been distributed between water balance
components using the Maximum Likelihood Estimation technique. Water-intosupply meters have been used and recalibrated in accordance with the
manufacturers recommendation. The water balance components have been
separately estimated and reconcile with the equivalent residual of the water
balance. 90% of the volume of distribution input should have been awarded a
reliability band of A or B within the separately estimated water balance
components.
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Sources of data quality
problems
• Measurement/metering errors
• Plant/Network errors
• Hidden flows or leaks
• Un-metered flows
• Un-measured inventory changes
• Dynamic effects
• Data processing errors
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Sources of Flow Measurement
Error
• Installation Effects
• Precision
• Fouling
• Fossilized Bias (buttered toast)
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Measurement Uncertainty
Daily performance
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Data Processing Errors
•
•
•
•
Manual data entry systems
Multiple values for single data points
Incorrect engineering calculations
Lack of time synchronization measurements
• Different end of period for accounting and engineering
• Data is historized and stored in multiple locations
• Data is changed and “fixed” by multiple functional
areas
• Supply, Distribution, Planning, Engineering Accounting
• Control
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How to improve the data
• Carry out a top down data quality
improvement project
• Use “Data Reconciliation” as an integral
element
• A proven method from the Oil and Petrochemical
industries
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Conventional Wisdom on
Data Quality
•
•
•
•
•
•
•
•
•
Engineering and accounting data are different
Meter errors balance out in the long run
Volume balances are the same as mass balances
Mass balances are simple
Mass balances are impossible
Manual estimates are not important
Accounting data does not matter
Custody transfer measurements are correct
Inventory measurements have no variance
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Measures of Data Quality
• Completeness
• Meters, inventories, Transactions, Composition, Densities
• Redundancy
• How many times is the same volume measured
• Precision
• What is the variance of the measurement device
• Accuracy
• How is the measurement compared to a standard
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Data Quality Analytical Tools
• Expert systems
• Neural networks
• Reconciliation systems
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Expert Systems
• Rules of Thumb
• Complex to build and maintain
relationships
• Useful for gross error detection
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Neural Networks
• Recognizes patterns
• Model setup is important
• Accurate to a few percent
• Useful for gross error detection
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Data reconciliation
• Data reconciliation is a systematic way of using all
the available information about a process or system
or business to improve consistency and accuracy
• Very often some information is overlooked
• This information can be flows, inventories, levels, ,
meter accuracies, loss estimates and equations i.e..
mass balances, component balances, energy
balances
• Sigmafine is an advanced data reconciliation package
designed for the process and utility industries
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The theory behind data
reconciliation
Reconciled value
i.e.. best estimate
of value consistent with
all information
4.5 ML/day
Flow
Meter
Level
Meter
Delta Level
Integrated flow
reading
Change in
inventory
Average Area
Tolerance
of flow meter
Tolerance of level
measurement
Tolerance of
reconciled value
0.0 ML/day
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The mathematics
• The SigmaFine Data reconciliation algorithm
distributes all the errors in proportion to the
confidences on the data (e.g. meter readings) so that:
• All the balances are precisely satisfied
• the total sum of the perturbations on the data
is minimised
• The sum is the squared deviation normalised by the
confidence on each piece of the data.
• This is a large constrained minimum sum of errors
squared problem and uses a Kalman filtering
algorithm.
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The history of data
reconciliation
• Data reconciliation has been used for 20 years
in the Oil and Petrochemical sector
• It produces accurate material, energy and
component balances
• It helps the accountants track expensive feed,
intermediates and products and account for
losses
• Before SigmaFine, data reconciliation was
expensive and cumbersome to use
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What is the SigmaFine package
OTHER PROGRAMS
NETWORK MODELS
AND CASES
RECONCILED
DATA
APPLICATIONS
AD HOC
REPORTS
SQL
PI
DATA
RECONCILIATION
The optimised
reconciliation
algorithm
SIGMAFINE
HISTORICAL
Process Book
REPORTS
• WATER
BALANCES
• LEAK
ESTIMATES
• INVENTORIES
• ETC.
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PUMPS
OTHER
INPUTS RESERVOIRS
FLOW METERS
MANUAL
DATA
NIGHT LINE
DATA
Invensys
Honeywell
THE FIX
IN TOUCH
SCADA
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How SigmaFine deals with
inventories
For irregularly shaped tanks or reservoirs, SigmaFine
has an automatic built in “strapping” feature
The vessel
is divided
into a number
of slices and each slice has
an area associated with it. The program interpolates
linearly between the slices to calculate the area at any
depth and thus the change in volume for any change
in depth.
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Typical applications
• Accounting mass/water balance
• Operational water balance
• Leakage estimation and tracking
• Suspect meter reports/meter proving
• Dosing component balance
• Dosing accuracy improvement
• Shared processing/asset agreements
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Applications continued
• Preparation of data to Regulator
• Water stock monitoring/reporting
• Recovery support after upset
• Network description and
documentation
• Training of operational personnel
• Historical performance reporting
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Applications continued
• Mass, volume and component balances
at treatment plants
• Mass and volume balances around
sewage works
• Improved process knowledge
• Diurnal flow estimation and balancing
• Adverse trend detection, e.g. solids
build-up
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Applications continued
• Identification of problems
• Instrument/meter problems
• Badly installed, faulty, or biased meters
• Faulty calibration or instrument drift
• Missing measurements
• Model or network knowledge errors
• unaccounted or missing flows
• incorrect association of data
• incorrect time stamping
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Implementation
• Attend training course (3 days) Install on target
desktop computer
• Develop initial model/network (few days to few
months depending on size)
• Set up auto transfer of data PI, and accounting
systems
• Debug model, test data
• Develop and enlarge model in line with business
needs. Migrate to larger machine or network.
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SIGMAfine Monthly
Operations
Overall reconciliation
Distribution Input
Flow balance
reconciliation around
storage and distribution
Distribution losses
Flow balance around treatment plants
Supply and plumbing losses
Water taken legally unbilled
Water taken illegally unbilled
Water delivered billed un-measured
Water delivered billed measured
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Using Sigmafine
balance area
balance area
balance area
balance area
balance area
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• SigmaFine lets you describe a network of treatment works, pipes, pumps,
reservoirs and meters as a live intelligent graphic which you can change at
any time
• The reconciled balance formula are derived automatically from this picture
when you run a balance
Zone 5
Zone 6
balance area
Zone 4
balance area
Zone 3
Zone 1
Zone 2
balance area
balance area
balance area
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• SigmaFine lets you build up your balances from small local zones through
district to division and company wide balances or vice versa
• Everyone can access a standard updated network and can alter their own
local copy for test runs, feasibility studies, investment decisions etc
District balance area
balance area
balance area
balance area
balance area
balance area
balance area
balance area
balance area
balance area
balance area
balance area
balance area
balance area
balance area
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balance area
balance area
balance area
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Typical SigmaFine Screen
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Building a Model
C1 zone
C2 zone
Icons
C3 zone
shaft
LD 2
Treatment
works
RX1 sr
RX2 sr
Distribution
zone
Shaft 1
Pump station
Shaft 2
Shaft 3
C4 zone
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Catchment 1 dem
Rx10 to C1
Catchment 1 zone
C3 to RX1
C3 to RX3
C4to FX2
C5 to RX6
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Catchment 1 dem
Rx10 to C1
Catchment 1 zone
C3 to RX1
C3 to RX3
C4to FX2
C5 to RX6
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Experience so Far with
SigmaFine in the Water industry
• Three projects so far
• Large ring main flow balance (circa 90
meters)
• Distribution area flow balance
• Treatment works flow balance
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Lessons learnt
• Treatment plant have lots of redundancy in meters
and this can be used to significantly increase the
quality (i.e.accuracy) of the flows into the
distribution areas. This can go from a tolerance of +/5% or worse to to +-.5%
• SigmaFine data reconciliation studies should be done
before planning new (distribution) meter projects
• This can reduce number of meters and ensure they are in the
optimum location. This can save once of costs in $millions!!
• There are many intangible benefits form
implementing data reconciliation projects including
better retention of network knowledge
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Lessons learnt
• Many meters have biases or slope errors
• These can be soft calibrated using results if
reconciliation
• Bad meter detection is very useful since all water
companies have shortages of maintenance man-hours
• Large undetected flows or flows in opposite direction
to anticipated can be present especially in old
networks.
• Consistent knowledge of network topology is rare
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Average meter performance
.00
Meter 18
-10.00
-20.00
-30.00
-40.00
-50.00
-60.00
-70.00
-80.00
-90.00
-100.00
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y = 0.7732x - 13.639
meter18
Linear (meter18)
.00
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Meter with bias and slope
error
Meter 8
750.00
700.00
650.00
600.00
550.00
500.00
450.00
y = 0.6773x + 121.01
400.00
600.00
700.00
meter8
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800.00
Linear (meter8)
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Looks like a bad meter but
flow is low
Meter 17
25.00
20.00
15.00
10.00
5.00
.00
-5.00
-10.00
y = 0.3436x - 0.3643
-15.00
.00
10.00
20.00
meter17
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30.00
40.00
50.00
Linear (meter17)
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Just some calibration errors
Meter 37
80.00
70.00
60.00
50.00
40.00
30.00
20.00
10.00
.00
30.00
y = 1.0454x - 2.0142
35.00
40.00
45.00
meter37
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50.00
55.00
60.00
65.00
70.00
Linear (meter37)
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A sticking meter
Meter 11
550.00
500.00
450.00
400.00
350.00
y = 1.3984x - 196.99
300.00
300.00
y = 0.9556x - 15.071
400.00
meter11
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500.00
Linear (meter11)
600.00
Linear (meter11)
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Thames Water Ring Main
Reconciled Flow balance report
All figures in Million litres
05/03/95 23:59:59
raw
reconciled influence
Tr w 1 to rm
AshC_LWRM
412.75
382.41
0.12
Tre w 2 torlm
Hamp_LWRM
291.92
321.66
0.09
Trw w 3 to lm
Surb_LWRM
36.60
36.72
0.00
COPP-FP
94.73
95.83
0.00
C1 to z1
Copp
49.44
52.04
0.00
C2 totoz1Stoke
mert
nun
39.24
39.24
0.00
C3 totoz3
mert
to
honoak
16.70
13.11
0.01
C2 to z1
streath
to
nun
65.61
-20.49
0.20
C3 to s1
streat
to
nor
51.22
57.79
0.00
Sh1 toz1
batt
to
brix
0.00
0.00
0.00
Sh 1 to z2
batt to crouch
14.87
5.15
0.08
Sh 2 to z1
batt to putney
22.09
29.58
0.00
Sh 3 to z3
Batt to Nunhead
30.51
13.09
0.04
Sh 4 to z4
Brix to res & z
28.83
34.86
0.00
sh 5 to z1
Brix to HO
75.95
-13.26
0.16
sh 5tothMid
z2 Lev
Brix
15.94
13.00
0.01
Sh 2 toto
z 2Nun
Brixton
0.00
0.00
0.00
Sh 4 lne
to z3
Park
to Put
33.92
26.52
0.00
Sh 6 lne
th z1
Park
to SUH
28.93
29.05
0.00
Sh7 to
z2to BH
Park
Lne
0.00
0.00
0.00
Sh 10
Barr
Hilltotoz1FP
10.29
10.09
0.00
Sh 2Hill
to to
z2Res
Bar
5.26
5.46
0.00
Barr
to ML
76.01
78.23
0.00
Sh 5Hill
to z1
Bar
53.74
59.30
0.00
Sh 2hilltotoz2SUH
NRH
53.89
50.74
0.00
Sh 6 to
to ML
z1
NRH
to
CrchHill
30.91
-14.36
0.54 Problem
Sh 3 to z3
NRH
to
FP
0.00
0.00
0.00
Sh 3 to z2
Tunnel
Filling
0.00
0.00
0.00
Sh 5 to z2
Barnes
to
LWRM
0.00
0.00
0.00
C1 to c3
feeds
Total
to zones
to LWRM
total
19102.9
flows from
demand
LWRM
total
17617.3
by
in zones
region
total
20715.16
end of period
IT Services for Process &
Manufacturing Industries
Norwood dem
Zone 1 demand
Hampton dem
Zone 2 demand
Nunhead dem
Zone 3 demand
Putney demand
Zone 4 demand
ShootupHill dem
Zone 5 demand
Brixton
dem
ZoneLevel
6 demand
Mid
dem
Zone 7 demand
Walton
Dem
Zone 8 demand
Barrow
Hill dem
Zone 9dem
demand
Ealing
Zone `10demand
Maiden
Ln dem
Zone 11 demand
Finsbury
Pk dem
Campden
dem
Zone 11 demand
Crouch
dem
Zone 12hill
demand
raw
reconciled influence
71.53
58.03
0.00
191.57
191.44
0.00
12.72
31.82
-0.04
71.57
56.33
0.01
127.76
88.04
0.01
45.10
35.10
0.01
10.24
168.66
-0.07
17.14
17.13
0.00
114.48
114.18
0.00
46.41
5.10
0.03
133.10
167.87
-0.07
105.89
106.17
0.00
0.00
0.00
0.00
2.09
3.38
-0.02
unmetered
flows
kett to eali z
Zone 1 z2
kewsh to brnsh
Zone 2 to z3
barnsh to hpksh
Zone 3 to z4
Hpksh to BHsh
Zone 6 to zone2
Pklnsh to BHsh
Rs1 to
Ashsh
torx2
Kempsh
Ps2
to
BHsh to ps3
NRHsh
C1 totoc3
Batsh
PkLsh
C4 totoc6Batsh
Brixsh
C4 to c6Brixsh
Streshto
Zone 2 to
z 10
Mertshto
Streas
X to x1
Walsh
to Mertsh
Hamsh
Des totoz3Walsh
Ashsh
to rx4
Hamsh
Rx2 to
Kempsh
Rx5 to to
ps2Kewsh
hammto
Bahzn
Ps3 to sx
BHresto
BHznflo
Ps3 to rxy
btnk
Ps4totohmmps
ps4
BHresto
BHznflo
Xs rto lf2
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5.13
-5.10
-5.14
-54.45
302.52
0.00
36.16
360.93
402.76
438.47
476.94
701.04
710.77
383.75
0.00
0.00
5.42
0.00
5.48
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Cost benefit analysis
• A single undetected leak of treated water can
cost £50,000 a year
• An investment decision made too soon due
to inaccurate data can cost many K£ per
month in interest alone
• Improved data quality can payback can be
very fast
• Detailed cost benefit analyses can be
provided
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Benefits
The following benefit calculations are based on one large municipal water company supplying 5 million households with a cost of
75c per household per day and a value of water delivered of $1.5 per M3. Leakage rate is assumed to be at 7 litres per hour per
property with a target leakage rate ( i.e. where the cost of further reductions balances the cost of repairing leaks) of 4 Litres per
hour per property
per year
$204,400
$79,489
$490,560
$80,500
$2,862
$225,000
$6,708
$536,550
$300,000
$80,500
$286,160
$110,250
$30,000
$180,000
$1,050,000
$80,500
$80,500
Billing improvement
saving due to incident prevention equal to cost of lost supply
value of saved leakage at marginal cost ex treatment works
total saving from reducing by one engineer
re leakage
Chlorine reduction
Saving from better shared processing billing
One man month saving for Offwat Data preparation
savings due to increased water price from better negoitiation with regulator:
savings due to increased water price from better negoitiation with regulator:
network engineer saving from improved network problem detection.
leakage reduction by better historical performance reporting
value of improvement from reduced dosing at treatment works
saving from reduced power at treatment works
saving from reduced power at sewage works
improved process knowledge
Instrument engineer saving from improved metering problem detection.
network engineer saving from improved network problem detection.
total yearly saving
total net present value assuming a ten year life at a discount rate of
IT Services for Process &
Manufacturing Industries
10.00%
$3,823,979
$30,047,281
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Conclusions
• Data Quality is an endemic problem in the
water industry that needs addressing
• Together PI and SigmaFine can help solve
this problem and significantly improve
business operations and profitability
• Tangible $ benefits can be very large and can
be identified.
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Manufacturing Industries
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PI in the Water sector
For More information on existing applications
of PI in the UK Water sector contact:
David Rees
Instem Beaver Valley
2 Watermoor Road
Cirencester
Gloucestershire
CL7 1JN
Tel
Email
+44 (01785) 827329
[email protected]
IT Services for Process &
Manufacturing Industries
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Sigmafine in the Water sector
For More information on existing applications
of SigmaFine in the Water sector contact:
Brian Neve
e-mail: [email protected] or [email protected]
Telephone
+44 (0)2380 629 429
Direct line
+44 (0)2380 745 920
Direct Fax
+44 (0)2380 745 921
Mobile
+44 (0)7768 797 276
Rex Software Limited
Chesil House
Shakespeare Road
Eastleigh
Southampton SO50 4SY
IT Services for Process &
Manufacturing Industries
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