VYŠKOV - HYDRAULIC ANALYSIS OF WATER SUPPLY NETWORK HYDROINFORM a.s. + AQUA PROCON s.r.o. Czech republic

INTRODUCTION Vyškov is a district city with app. 20,000 inhabitants 100 km of pipeline, DN 50 - DN 500 Cast and PVC, 1936 Four main water sources supplying the whole area together High pressure (up to 7.5bar), redundant high leakage

MODEL AREA CZECH REPUBLIC PRAGUE VYŠKOV BRNO

MODEL AREA

MODEL AREA

MODEL AREA 20,000 Inhabitants 100 km of pipelines DN 50 - DN 500 4 Water sources High pressure Big water losses

OBJECTIVES Development, calibration and verification of a mathematical model Steady state and extended period simulation Dividing the existing network into several pressure zones: optimum pressure distribution leakage decrease savings (electricity consumption, water production)

ODULA MODELLING PACKAGE Analyses an entire water distribution system under steady-state and extended period simulations with water quality analysis. Operates within Microsoft Windows 95 and Windows NT. Integrates robust SQL Client - Server Database.

ITEA‘97 ODULA package was awarded by the European Innovation Technology Price ITEA‘97 in Brussels.

ODULA OVERVIEW Interactive model development. Data exchange with MapInfo, ArcInfo, ArcView and MicroStation Outlook GIS. Import ASCII files. Import database files. Display of vector and raster images. Fast and accurate modelling. High quality graphical capabilities.

ODULA MODELLING PACKAGE ODULA DATA IMPORT GRAPHICS DATABASE GIS DATA MODELS AutoCAD DXF Raster Files ASCII InterBase DBASE PARADOX ODBC - Oracle - Informix - others MAPINFO ArcVIEW LIDS EPANET WATERCAD H2ONET CYBERNET KYPIPE HYPRESS ODULA MODELLING PACKAGE

ODULA MODELLING PACKAGE ODULA DATA EXPORT ODULA MODELLING PACKAGE GRAPHICS DATABASE GIS DATA MODELS ASCII InterBase DBASE PARADOX ODBC - Oracle - Informix - others AutoCAD DXF Raster Files Microsoft AVI MAPINFO ArcVIEW EPANET HYPRESS

ODULA ANALYSIS ENGINE Industry standard EPANET (EPA) algorithm. Powerful rigorous Hybrid method. Hydraulic and water quality modelling. Fast and accurate modelling.

ODULA ANALYSIS ENGINE Storage node equations: ¶ys /¶t = Qs / Sv (1) Qs = Si Qis - Sj Qsj (2) hs = Es + ys (3) Link and junction node equations: hi - hj = f(Qij) (4) Si Qik - Sj Qkj - Qk = 0 (5)

ODULA ANALYSIS ENGINE Substance mass conservation: ¶ cij / ¶ t = -(Qij / Sij) (¶ cij / ¶ xij) + q(cij) (6) First order kinetics reaction rate: q(c) = -kbc - (kf / RH) (c - cw) (7)

ODULA ANALYSIS ENGINE Hydraulic model Dynamic water quality model Gradient algorithm, Todini,E, Pilati, S, 1987 Efficient sparse matrix solution, George-Liu, 1981 Dynamic water quality model Discrete volume element method, Rossmann, 1983

ODULA APPLICATIONS Master plans of water distribution networks. Fire-flow analysis. Pressure zones optimisation. Water quality analysis. Water age analysis. Source tracing. Industry pipe networks. IF-THEN control rules modelling.

ODULA PROJECT EXAMPLES MASTER PLAN OF PRAGUE DATA IMPORTED FROM LIDS GIS

ODULA PROJECT EXAMPLES OKLAHOMA CITY 24-HRS SIMULATION

ODULA USERS More than 60 users within: Czech Republic (Czech version) Poland (Polish version) Russia (Russian version) Italy (English version) Austria (English version) USA (US version) Denmark (English version) Sweden (English version)

COMPUTATIONAL SCHEME WATER INFLOW WATER TRANSFER

INPUT DATA PROCESSING ORIGINAL NETWORK DATA WAS DIGITIZED IN MAPA2 PIPE DATA - MATERIAL - RESIDENTIAL TYPE - DIAMETER NODE DATA - X,Y,Z

INPUT DATA PROCESSING ORIGINAL NETWORK DATA WAS IMPORTED INTO ODULA PIPE SCHEMATIZATION HYDRAULIC STRUCTURES - Tanks, reservoirs - Pumps, valves DEMAND DISTRIBUTION - Reduced pipe lengths

INPUT DATA PROCESSING PIPE SCHEMATIZATION

CALIBRATION DATA RADOM DATA H - POINTS Q - POINTS

CALIBRATION DATA LARGE WATER CONSUMERS DATALOGGERS 14 DAYS PERIOD DEMAND DATALOGGERS

MODEL CALIBRATION Macro-level calibration Micro-level calibration pipe roughness coefficients nodal demands Steady state calibration Extended period calibration

MODEL CALIBRATION Steady state calibration PUMPING STATIONS Accuracy about 3% of the total flow within selected time level TANKS Accuracy from 0-22% of the total flow within some of the time levels. The differences are probably caused by undefined water consumers.

MODEL CALIBRATION Extended period calibration RADOM and DATALOGGERS readings within the time step of 15 minutes. The total simulation time was 24-hours

MODEL CALIBRATION TANK LHOTA TANK DRNOVICE TANK LHOTA TANK DEDICE [%] [ HOURS ] TANK LHOTA TANK DEDICE PUMPING STATION KASPAROV PUMPING STATION DRNOVICE TANK DRNOVICE PUMPING STATION KRECKOVICE

HYDRAULIC SIMULATION Steady state analysis Extended period analysis Multiple operational scenarios Dividing the network into separate pressure zones

HYDRAULIC SIMULATION Dividing the network into separate pressure zones Three main pressure zones The estimate of the designed total demand was based on: RADOM readings within the period of 1996-1997 Reduced pipe length method and the ratio of the invoiced water Future development coefficient correction (increase of industry, population, specific demands)

HYDRAULIC SIMULATION ODULA allows you to model these pressure zones all together III. I. IV. II.

I. PRESSURE ZONE PUMPING STATION DEDICE AT-Station with frequency regulation TANK BRNANY RECONSTRUCTED TANK

I. PRESSURE ZONE II. PRESSURE ZONE TANK DEDICE Pumping station with the frequency regulation 0.5 TANK BRNANY I.PRESSURE ZONE 3.2 CONTROL RULES were defined in the ODULA package for both fast and slow filling including water transfer from the II.PRESSURE ZONE and the automatic frequency regulation of the AT-Station FAST FILLING 2.3 SLOW FILLING 2.0

I. PRESSURE ZONE II. PRESSURE ZONE TANK DEDICE Pumping station with the frequency regulation II. PRESSURE ZONE 0.5 80l/s TANK BRNANY Minimum frequency Maximum frequency 3.2 FAST FILLING IF h(DEDICE)<0.5m THEN Q=80l/s from II.PRESSURE ZONE. Minimum frequency for h=3.2m in BRNANY Maximum frequency for h=2.3m in BRNANY FAST FILLING 2.3

I. PRESSURE ZONE II. PRESSURE ZONE TANK DEDICE Pumping station with the frequency regulation II. PRESSURE ZONE 0.5 10l/s TANK BRNANY Maximum frequency SLOW FILLING IF h(BRNANY)<2.0m THEN Q=10l/s from II.PRESSURE ZONE. Maximum frequency for h=2.3m in BRNANY 2.3 SLOW FILLING 2.0

I. PRESSURE ZONE - SAMPLE RESULTS TANK BRNANY TANK BRNANY FILLING EMPTYING BACKWARD TRACING FORWARD TRACING FLOW TRACING 220 HOURS 1700 HOURS

I. PRESSURE ZONE - SAMPLE RESULTS SOURCE TRACING OF DEDICE 24-Hours simulation

II. PRESSURE ZONE TANK LHOTA TANK DEDICE CONTROL VALVE TANK DRNOVICE PUMPING STATION KASPAROV PUMPING STATION KRECKOVICE

II. PRESSURE ZONE III. PRESSURE ZONE I. PRESSURE ZONE TANK LHOTA TANK DEDICE TANK DRNOVICE 4.90 Control valve 2.45 PUMPING STATION KASPAROV I. PRESSURE ZONE CONTROL VALVE SETTINGS IF h(DRNOVICE)=4.9m THEN OPEN 100% IF h(DRNOVICE)=2.45m THEN CLOSED 0% FROM 2.45 TO 4.90 smooth opening 0 .. 100% PUMPING STATION KRECKOVICE

SAMPLE RESULTS ORIGINAL STATE PROPOSED STATE PRESSURE ABOVE 60m COMPARISON OF PRESSURE DISTRIBUTION BETWEEN THE ORIGINAL AND PROPOSED NETWORK STATE DURING MINIMUM FLOW.

PROPOSED NETWORK STATE CONCLUSION PROPOSED NETWORK STATE FOUR PRESSURE ZONES III. AUTOMATIC THROTTLE VALVE BOA-COMPACT PUMPING STATION WITH FREQUENCY REGULATION LOWARA RECONSTRUCTED TANK I. IV. II.

SIGNIFICANT ENERGY SAVINGS CONCLUSION SIGNIFICANT ENERGY SAVINGS Total energy consumption was calculated from the simulation results for both existing and proposed state for designed demands app. 500 kW/day - Proposed state app. 770 kW/day - Existing state 35% of energy savings

PRESSURE OPTIMIZATION CONCLUSION PRESSURE OPTIMIZATION “Average pressure HĆ” is defined as statistical average of all junction nodes pressure values HĆ = 38.7m - Proposed state HĆ = 51.4m - Existing state 33% of the average pressure decrease

WATER QUALITY IMPROVEMENT CONCLUSION WATER QUALITY IMPROVEMENT Groundwater sources are preferred Surface water sources are less used

CONCLUSION LEAKAGE DECREASE NETWORK SEGMENTATION Redundant leakage decrease corresponding to the average pressure decrease Systematic leakage control is suggested for the future network maintenance NETWORK SEGMENTATION Easier network operational control