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$Page 1: 1 http:\\ IT Services for Process & Manufacturing Industries The use of PI and SigmaFine in the Water Industry B. D. Neve Rex.$
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IT Services for Process & Manufacturing Industries

The use of PI and SigmaFine in the Water

IndustryB. D. Neve

Rex

2



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.

3



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

4



Typical 80,90 and 2000 operational IT

structure

Treatment plants

Scada systems

Users

Communications: Radio, PSTN, Satellite, Wide area networks, VPNs

Custom centralised OMS system

Custom communications to custom user interfaces

5



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

6



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

7



The Final Goal

Flow balance around treatment plants

Flow balance reconciliation around storage and distribution

Water delivered billed measured

Distribution Input

Water delivered billed un-measured

Distribution lossesWater taken legally unbilled

Water taken illegally unbilled

Supply and plumbing losses

Overall reconciliation

Leakage monitoring and reduction systems

reconciled, accurate, consistent and auditable information stakeholders

8



The use of PIin the Water Industry

9



PI In Use at Southern WaterPI In Use at Southern WaterTelemetry Archive--------------------------100,000 PI pointsMTDB, PIQA & Tag Group DatabaseWADIS & Report ManagerPI-SCOPE InterfacePI-CMS Interface

Derived Values Archive---------------------------------5,000 PI pointsDerived Values CalculationsTask SchedulerException Notification

Servelec Regional Telemetry / SCADASCOPE-X 4,000 Remote Sites

Slide courtesy of Instem Beaver Valley

10



Telemetry Archive SystemTelemetry 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

11



Telemetry Archive SystemTelemetry Archive SystemServer

100,000 Point PI Data ArchiveOraclePI Quality ArchivePI SCOPE InterfacePI CMS InterfaceMaster Translation DatabaseTag Group DatabaseReport Database

ClientPI-ProcessBookPI-DataLinkPI-Manual LoggerArchive EditPIQA ViewerEnd-to-End Test LoggingReport ManagerData ViewTag ManagerTag Group manager


12



Derived Values ArchiveDerived 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)

13



DVA CalculationsDVA CalculationsTelemetry PI Archive

(PI-API)DVA PI Archive

PI-API

Microsoft Excel25 Calculation Functions

Multiple function callsMultiple sheets

Exception Report

Microsoft Task Scheduler

Remote Task

Scheduler Manager

Operational Database

(ODBC)


14



Thames Water PI SystemThames Water PI SystemServer10,000 Point PI Data ArchiveABB Aqua Master InterfaceABB GatewayRadcom InterfaceRadcom GatewayDMA Function SetsMeterMan

ClientPI-ProcessBookPI-DataLinkArchive EditPIQA ViewerReport ManagerData ViewTag ManagerTag Group manager


15



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

16



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

17



Improving Data Qualityin the Water Industry

18



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

19



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

20



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

21



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.

22



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 AThe 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 BThe 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-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 should have been awarded a reliability band of A or B within the separately estimated water balance components.

25



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

26



Sources of Flow Measurement

Error

• Installation Effects• Precision• Fouling• Fossilized Bias (buttered toast)

27



Daily performance

Measurement Uncertainty

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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

29



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

30



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

31



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

32



Data Quality Analytical Tools

• Expert systems• Neural networks• Reconciliation systems

33



Expert Systems• Rules of Thumb• Complex to build and maintain

relationships• Useful for gross error detection

34



Neural Networks• Recognizes patterns• Model setup is important• Accurate to a few percent• Useful for gross error detection

35



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

36



The theory behind data reconciliation

Delta Level

FlowMeter

LevelMeter

Average Area

Tolerance of flow meter

Tolerance of level measurement

Integrated flow reading Change in

inventory

Reconciled valuei.e.. best estimateof value consistent withall information

Tolerance of reconciled value

0.0 ML/day

4.5 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.

38



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

The optimised reconciliation

algorithm

Invensys

HoneywellTHE FIX

IN TOUCHSCADA

PUMPSRESERVOIRS

FLOW METERSMANUAL

DATA

• WATER BALANCES

• LEAK ESTIMATES

• INVENTORIES• ETC.

DATARECONCILIATION

HISTORICAL

OTHERINPUTS

REPORTS

NETWORK MODELSAND CASES

APPLICATIONS

AD HOCREPORTS

SQL

OTHER PROGRAMS

Process Book

RECONCILEDDATA

NIGHT LINEDATA

SIGMAFINE

PI

40



How SigmaFine deals with inventories

For irregularly shaped tanks or reservoirs, SigmaFinehas an automatic built in “strapping” feature

The vesselis dividedinto 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 anydepth and thus the change in volume for any change in depth.

41



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

42



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

43



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

44



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

45



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.

46



SIGMAfine MonthlyOperations

Flow balance around treatment plants

Flow balance reconciliation around storage and distribution

Water delivered billed measured

Distribution Input

Water delivered billed un-measured

Distribution losses

Water taken legally unbilled

Water taken illegally unbilled

Supply and plumbing losses

Overall reconciliation

47



balance area

balance area balance area balance area

balance area

Using Sigmafine

48



• 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

balance area

balance area balance area balance area

balance area

Zone 1Zone 2Zone 3Zone 4Zone 5Zone 6

49



• 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

balance area

balance area

balance area

balance area

balance area

balance area balance area

balance area

balance areabalance area balance area balance area

balance area

balance areabalance area balance area

balance area

District balance area

50



Typical SigmaFine Screen

51



Building a Model

shaft

Treatment works

Distribution zone

Pump station

Icons

C1 zone

LD 2

Shaft 2

Shaft 1

RX1 sr

RX2 sr

C2 zone

C3 zone

C4 zone

Shaft 3

52



53



54



Catchment 1 zoneCatchment 1 dem Rx10 to C1

C3 to RX3

C4to FX2

C5 to RX6

C3 to RX1

55



Catchment 1 zone

C5 to RX6

C4to FX2

C3 to RX3

C3 to RX1

Catchment 1 dem Rx10 to C1

56



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

57



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

58



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

59



Average meter performance

Meter 18

y = 0.7732x - 13.639-90.00

-80.00

-70.00

-60.00

-50.00

-40.00

-30.00

-20.00

-10.00

.00

-100.00 .00meter18 Linear (meter18)

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Meter with bias and slope error

Meter 8

y = 0.6773x + 121.01400.00

450.00

500.00

550.00

600.00

650.00

700.00

750.00

600.00 700.00 800.00meter8 Linear (meter8)

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Looks like a bad meter but flow is low

Meter 17

y = 0.3436x - 0.3643-15.00

-10.00

-5.00

.00

5.00

10.00

15.00

20.00

25.00

.00 10.00 20.00 30.00 40.00 50.00

meter17 Linear (meter17)

62



Just some calibration errors

Meter 37

y = 1.0454x - 2.0142.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

30.00 35.00 40.00 45.00 50.00 55.00 60.00 65.00 70.00

meter37 Linear (meter37)

63



A sticking meterMeter 11

y = 1.3984x - 196.99 y = 0.9556x - 15.071

300.00

350.00

400.00

450.00

500.00

550.00

300.00 400.00 500.00 600.00

meter11 Linear (meter11) Linear (meter11)

64



Thames Water Ring Main Reconciled Flow balance report feeds flows from by to LWRM LWRM zones

All figures in Million litres total total total19102.9 17617.3 20715.16

end of period 05/03/95 23:59:59 unmeteredraw reconciled influence raw reconciled influence flows

AshC_LWRM 412.75 382.41 0.12 Norwood dem 71.53 58.03 0.00 kett to eali z 5.13Hamp_LWRM 291.92 321.66 0.09 Hampton dem 191.57 191.44 0.00 kewsh to brnsh -5.10Surb_LWRM 36.60 36.72 0.00 Nunhead dem 12.72 31.82 -0.04 barnsh to hpksh -5.14COPP-FP 94.73 95.83 0.00 Putney demand 71.57 56.33 0.01 Hpksh to BHsh -54.45Copp to Stoke 49.44 52.04 0.00 ShootupHill dem 127.76 88.04 0.01 Pklnsh to BHsh 302.52mert to nun 39.24 39.24 0.00 Brixton dem 45.10 35.10 0.01 Ashsh to Kempsh 0.00mert to honoak 16.70 13.11 0.01 Mid Level dem 10.24 168.66 -0.07 BHsh to NRHsh 36.16streath to nun 65.61 -20.49 0.20 Walton Dem 17.14 17.13 0.00 Batsh to PkLsh 360.93streat to nor 51.22 57.79 0.00 Barrow Hill dem 114.48 114.18 0.00 Brixsh to Batsh 402.76batt to brix 0.00 0.00 0.00 Ealing dem 46.41 5.10 0.03 Streshto Brixsh 438.47batt to crouch 14.87 5.15 0.08 Maiden Ln dem 133.10 167.87 -0.07 Mertshto Streas 476.94batt to putney 22.09 29.58 0.00 Finsbury Pk dem 105.89 106.17 0.00 Walsh to Mertsh 701.04Batt to Nunhead 30.51 13.09 0.04 Campden dem 0.00 0.00 0.00 Hamsh to Walsh 710.77Brix to res & z 28.83 34.86 0.00 Crouch hill dem 2.09 3.38 -0.02 Ashsh to Hamsh 383.75Brix to HO 75.95 -13.26 0.16 Kempsh to Kewsh 0.00Brix to Mid Lev 15.94 13.00 0.01 hammto Bahzn 0.00Brixton to Nun 0.00 0.00 0.00 BHresto BHznflo 5.42Park lne to Put 33.92 26.52 0.00 btnk to hmmps 0.00Park lne to SUH 28.93 29.05 0.00 BHresto BHznflo 5.48Park Lne to BH 0.00 0.00 0.00Barr Hill to FP 10.29 10.09 0.00Bar Hill to Res 5.26 5.46 0.00Barr Hill to ML 76.01 78.23 0.00Bar hill to SUH 53.74 59.30 0.00NRH to ML 53.89 50.74 0.00NRH to CrchHill 30.91 -14.36 0.54 ProblemNRH to FP 0.00 0.00 0.00Tunnel Filling 0.00 0.00 0.00Barnes to LWRM 0.00 0.00 0.00

Tr w 1 to rmTre w 2 torlmTrw w 3 to lmC1 to z1 C2 to z1C3 to z3C2 to z1C3 to s1Sh1 toz1Sh 1 to z2Sh 2 to z1Sh 3 to z3Sh 4 to z4 sh 5 to z1 sh 5 th z2Sh 2 to z 2 Sh 4 to z3 Sh 6 th z1Sh7 to z2Sh 10 to z1Sh 2 to z2Sh 5 to z1Sh 2 to z2Sh 6 to z1Sh 3 to z3Sh 3 to z2Sh 5 to z2C1 to c3

Zone 1 demandZone 2 demandZone 3 demandZone 4 demandZone 5 demandZone 6 demandZone 7 demandZone 8 demandZone 9 demandZone `10demandZone 11 demandZone 11 demandZone 12 demand

Zone 1 z2Zone 2 to z3Zone 3 to z4Zone 6 to zone2Rs1 to rx2Ps2 to ps3C1 to c3C4 to c6C4 to c6Zone 2 to z 10X to x1Des to z3Rx2 to rx4Rx5 to ps2Ps3 to sxPs3 to rxyPs4 to ps4Xs rto lf2

Total to zones demand in region

65



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

66



BenefitsThe 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 yearBilling improvement $204,400saving due to incident prevention equal to cost of lost supply $79,489value of saved leakage at marginal cost ex treatment works $490,560total saving from reducing by one engineer re leakage $80,500Chlorine reduction $2,862Saving from better shared processing billing $225,000One man month saving for Offwat Data preparation $6,708savings due to increased water price from better negoitiation with regulator: $536,550savings due to increased water price from better negoitiation with regulator: $300,000network engineer saving from improved network problem detection. $80,500leakage reduction by better historical performance reporting $286,160value of improvement from reduced dosing at treatment works $110,250saving from reduced power at treatment works $30,000saving from reduced power at sewage works $180,000improved process knowledge $1,050,000Instrument engineer saving from improved metering problem detection. $80,500network engineer saving from improved network problem detection. $80,500

total yearly saving $3,823,979total net present value assuming a ten year life at a discount rate of 10.00% $30,047,281

67



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.

68



PI in the Water sector

David ReesInstem Beaver Valley2 Watermoor RoadCirencesterGloucestershireCL7 1JN Tel +44 (01785) 827329Email [email protected]

For More information on existing applications of PI in the UK Water sector contact:

69



Sigmafine in the Water sector

Brian Nevee-mail: [email protected] or [email protected] +44 (0)2380 629 429Direct line +44 (0)2380 745 920Direct Fax +44 (0)2380 745 921Mobile +44 (0)7768 797 276

Rex Software LimitedChesil House Shakespeare RoadEastleighSouthampton SO50 4SY

For More information on existing applications of SigmaFine in the Water sector contact:

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