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In the pro-active strategy, preventive maintenance is emphasized ... CARE-W is a computer based system for water network rehabilitation planning, where the.
COMPUTER AIDED REHABILITATION OF WATER NETWORKS IN AN AREA INCLUDING INFORMAL SETTLEMENTS T. Bakken1 S. Sægrov2 S. Pietersen3 C. Brouckaert4 1

Norplan/Asplan Viak AS, Tempeveien 22, 7031 Trondheim, Norway E-mail – [email protected], tlf.; + 47 90026108, date of birth: 02.10.78 2 NTNU - Norwegian University of Science and Technology, Department of Hydraulic and Environmental Engineering, Trondheim, Norway 3 eThekwini Water Services, Durban, South Africa. 4 Pollution Research Group, University of KwaZulu-Natal, Durban, South Africa.

ABSTRACT Rehabilitation of water networks is decisive for an efficient water management, but it is not evident which pipes to rehabilitate at what time. The software Care-W (computer aided rehabilitation of water networks) was developed to facilitate the process of assessing rehabilitation alternatives and to enable the utility manager to rehabilitate the right pipe at the right time. The idea is to avoid premature rehabilitation (rehabilitation of pipes that are still serviceable), interruption of water supply (due to unexpected pipe breaks), and to avoid poor water quality. Care-W has successfully been tested in several European cities. Water utilities in South Africa have also shown interest in using the system. South Africa experiences different problems to those found in Europe. This paper is based on a master thesis that was performed to gain an insight into some of those problems and to assess the feasibility of Care-W in a different environment. Umlazi reservoir 1 supply zone, a sub area in the township Umlazi in Durban, was chosen as a test area. The area includes a number of informal settlements with the consequential problems of illegal connections and un-metered water consumption, which provide a challenging test of the Care-W methodology as applied to South African conditions. The performed analysis showed that the network in the test area is stressed and thus very vulnerable to pipe breaks. Besides, pipe breaks are likely to occur because of the state and the quality of the materials used in the area. In addition, the social conditions marked by poverty and high crime rates make the management of the network a great challenge. INTRODUCTION A major challenge for water managers world wide is to maintain their water assets within an appropriate standard. Water networks experience general ageing, corrosion and other deterioration processes, and rehabilitation of the pipelines is therefore crucial. Rehabilitation of water networks can be performed in a preventive, “pro-active” or a repairing, “re-active” way. In the pro-active strategy, preventive maintenance is emphasized, meaning that pipes where problems are expected are repaired or renewed before problems arise. This strategy demands a thorough knowledge of the network, if bad investments are to be avoided. A re-active strategy means that the problems are solved more or less at the same time as they arise. CARE-W is a computer based system for water network rehabilitation planning, where the main objective is to enable the utility manager to rehabilitate the right pipe at the right time.

CARE-W is a tool to prioritise rehabilitation projects, to avoid unnecessary investments, and to avoid major burst and leaks that have negative financial and social consequences (1). The software, supported by the European Union, is currently being tested by several cities in Europe. The city of Durban, South Africa also expressed interest in CARE-W. Analysis was performed to gain an insight into what water related challenges a city like Durban have, that are not prevalent in Europe. In addition it was assessed whether Durban, at this stage, is ready to take advantage of a rehabilitation program like CARE-W. OVERVIEW OF CARE-W The project Care-W (Computer aided rehabilitation of water networks) was a joint European initiative, coordinated by SINTEF, Norway, to develop a framework for water network rehabilitation. From 2001-2004 Care-W was supported by the European Commission under the fifth Framework Programme and the outcome of the project is a computer based system for water network rehabilitation planning. Care-W is also a tool for general management of the water networks which estimates the current and future condition of the networks. Care-W includes tools to assess performance indicators (PI), predict pipe failures (FAIL), calculate water supply reliability (REL), and estimate long term rehabilitation needs (LTP). Finally, annual rehabilitation projects can be selected and ranked and a long term investment plan created (ARP- Annual Rehab Planner). PI, Care-W FAIL, Care-W REL and LTP, may be used as stand alone programs or the tools may be operated jointly within the “Care-W Manager.” The analysis can be performed on an entire network, a district or a cluster of pipes, and the results are presented in tables, reports and graphs (1). It is important to understand that the Care-W prototype software is meant to be a tool to help the engineers in making rational annual and strategic rehabilitation plans. The software does not produce the plan itself. Figure 0.1 gives an overview of the Care-W program. It shows the input data needed and how the output data from the tools can be fed into the Care-W manager. The main output of Care-W, when running the complete system within the manager, is a priority list of rehabilitation projects and a short and long term investment plan

Figure 0.1 Overview of the Care-W program Only the stand alone versions of the Care-W tools Relnet, PI and LTP were used for this investigation. In addition, the computer program Epanet had an important role, because Relnet is based on this program’s` computing core. Relnet, PI-tool and LTP-tool were chosen because of their flexibility in data requirements. The other Care-W tools; Care-W FAIL, Care-W ARP and the hydraulic reliability tools Aquarel and Failnet- Reliab, require more extensive data. Owing to the fact that, when the master project started the amount of available data was unknown, the more data intensive tools were not considered. STUDY AREA– UMLAZI RESERVOIR 1 SUPPLY ZONE, DURBAN As a result of the National and Local Government restructuring in 1994, the operational area of the eThekwini (Durban) Municipality has increased by over 1000%. A great part of the Durban Metro Area is now made up of areas that were previously black townships with separate administrative bodies under the old apartheid system. The infrastructure in these areas is poor, and many of the residents have traditionally had inadequate water supply and sanitation services. Umlazi is a township that experiences these problems, and so was used as a test area for the Care-W program. Umlazi Township is about 20 km south of Durban in the eThekwini Municipal Area. It contains a mixture of approximately 35000 formal houses and 40000 informal houses, and the average water demand is 65 mega litres a day (2), (3), (4). EPANET Epanet is a computer program for analyzing water quality and hydraulic behaviour of pressurised pipe networks. The program models pressures and flows based on pipe data, demand information and elevation data. For the Epanet program to be applicable, the modelled pressures and flows have to be calibrated against field measurements to make the match as good as possible. A calibrated model with regard to roughness coefficients and average demand was prepared. Then, the new pressures and flows were compared to field measurements to evaluate the fit. Figure 0.1 indicates the measurement points.

Figure 0.1 Location of measurement points Due to the number of illegal connections in the area, the model calibration presented several difficulties. The distribution of water demand was unknown and the area did not have water meters in smaller measuring zones for a better monitoring of the flow. The initial results showed that the modelled values were very different from the measured values. It was appeared the measured flow from the reservoir was too low and not representative. A calibration program was set up for allocating demand to particular areas (5) Calibration program The calibration program made use of the Epanet demand pattern facility. The demand pattern is a sequence of multipliers which modify the water demand at any node to produce a demand that varies with time. Different demand patterns can be applied to different sets of nodes in the model. The calibration was based on the assumption that the illegal connections increased the water by an unknown factor relative to the official metered demand. However, the exact location of these illegal connections was unknown. Therefore the model was divided into a number of zones based on proximity to informal settlements, where the majority of the illegal connections were assumed to be. A different time pattern was allocated to each zone, and the calibration program adjusted the time pattern multipliers to fit the model predictions as closely as possible to measured flows and pressures. Adjusting the individual multiplier for each time in each zone would have resulted in a very large number of adjustable parameters, so the calibration was carried out in two separate steps:

1. A single pattern was applied to all the different zones to obtain an overall average time sequence of demands. This gave a model which matched the overall demand profile with time, but allocated it to the different zones in proportion to the number of legal connections that each contained.

2. Keeping the same relative time pattern developed in the first step, the pattern for each zone was multiplied by an additional parameter, which thereby adjusted the demands of the zones relative to one another, The division of the nodes into zones had to be carried out beforehand, and was based on a judgement of the relative likelihood of finding illegal connections in each area. In the first trial, an equal demand pattern was allocated to the whole network. Later, 7 areas with assumed high density of illegal connections were given equal patterns and pattern names. The remaining area was given a different pattern and name, and the two patterns were adjusted differently relative to each other. In the third trial, the area that was divided in 8 separate regions was given 8 different patterns, which were adjusted to get the best fit. Pattern 2-8 were given to areas with high incidence of illegal houses, and pattern 1 was given to the remaining area. Results Epanet – Umlazi reservoir 1 Comparing all the calibration results indicated that the manual modelled pressure values and the pressure values obtained from the 2 patterns regression using the calibration program, gave the best fit to the measured data. The two results gave very similar pressures in the measurement points. Both Epanet input files were run in Relnet, but because of an unexpected error when running the 2 pattern input file, the input file from the manual calibration was chosen as the final calibrated Epanet model. Table 0.1 presents pressure values modelled before calibration, pressure values obtained after the manual calibration, pressure values obtained using the calibration program and the pressure values measured in the field. Compared to the uncalibrated Epanet model, the calibration resulted in a model showing pressures closer to the measured field values. Table 0.1 Calibration results Modelled value

Item

Modelled value before calibration

Modelled value 2 demand Measured value after manual patterns (average) (Calibration calibration program)

PRV 2

90.87 m

PRV 5

86.55 m

PRV 7

68.56 m

PRV 8 Hydrant B section Hydrant T section Hydrant V section

74.83 m

73.7353 m

78.28 m

58.5007 m

58.44 m

50.53 m

48.6495 m

46.79 m

86.18 m

51.10 m

50.4895 m

69.61 m

85.73 m

73.04 m

72.5463 m

65.85 m

71.82 m

49.77 m

49.3072 m

38.89 m

76.12 m

50.04 m

48.5023 m

53.32 m

63.76 m

CARE-W: RELNET Description of Relnet Relnet calculates a hydraulic criticality index (HCl) that is expressing the hydraulic impact a pipe failure will have on the network. In that way, the HCl value determines the pipe’s relative importance towards an adequate water supply to the users, in terms of volume and pressure. The pipes are ranked, and it is possible to identify the ones that are most critical. Relnet is based on the Epanet computing core and requires an Epanet data file (*.inp). The HCI ranges from 0 to 1. The higher the HCl value, the higher impact the discarded link has on the total network reliability. If HCl =1 then no demand is satisfied (Qnew = 0) and if HCl = 0 then all demands are fully satisfied at the required pressure (Qnew = Qtotal) (6) Relnet does not include failure data in its calculations. As a consequence of Relnet being a pure hydraulic reliability tool, the results will be the same even if the structural conditions of the pipes are improved and the failure probability reduced. The reliability tools Aquarel and Failnet - Reliab can be used if failures in the network are included in the analysis (7) Results Relnet – Umlazi reservoir 1 The importance of a pipe to the whole network is mainly related to its diameter (large pipes supplies greater amounts of water than small pipes) and the redundancy of pipes in the network (“branched” network vs. “looped” network) Table 0.2 presents the 5 most critical pipes according to their hydraulic importance. As expected, large diameter pipes are shown to be critical.

Table 0.2 Most critical pipes in Umlazi Res. 1 supply zone, according to RelNet 2.04 Link ID Diameter Length (m) HCI - value Removed Nodes Nodes < RP (mm) 350 29.4 327 0 542 0.4583 350 109.2 821 0 548 0.4539 350 86.8 1204 0 547 0.4515 350 296.3 949 0 523 0.4389 450 140.7 838 0 561 0.4096 Figure 0.2 shows the pipe diameters in the network. The pipe sizes give an indication of where the most critical pipes are located, although the pipes´ redundancy is not considered. (If Relnet was run in the Care-W manager, and not as a stand-alone tool, it would have been possible to present a map showing the exact location of the most critical pipes.)

Figure 0.2 Overview of pipe diameters in Umlazi Reservoir 1 supply zone Table 0.3 presents the 5 least critical pipes according to their hydraulic importance. The table shows very clearly that not only pipe diameter are important for the hydraulic reliability. Pipe number 490 is, according to Relnet, the least important pipe even though there are smaller pipes in the network. Table 0.3 Least critical pipes in Umlazi Res. 1 supply zone, according to RelNet 2.04 Link ID Diameter Length (m) HCI - value Removed Nodes Nodes < RP (mm) 490 0.01112 225 145.2 0 72 639 0.01114 225 240.9 0 72 943 0.01134 150 108.6 0 71 464 0.01140 150 59.4 0 72 525 0.01195 32 42.4 1 67

1.1 Summary/Discussion Relnet-results Not surprisingly, the most critical pipes are shown to be the large diameter trunk pipes. A failure on pipe number 327, which is the most critical in Umlazi Res. 1 supply zone, will result in as much as 45.83 % unsatisfied demand in the area. Pipes transporting water out to “islands” (“branched” network), where the water only has the possibility to go in one direction, are also hydraulically important. Those pipes are however not among the “top 30” most critical pipes in the network, because they are smaller and not feeding to as many nodes as the trunk pipes. The least critical pipes do not necessarily have to be the smallest diameter pipes, as shown in Table 0.3. From the table it can be seen that one of the 32 mm pipes are more critical than some of the larger pipes. The observation indicates that the amount of undelivered water will be larger if a failure occurs on that 32 mm pipe than if a failure occurs on one of the larger pipes, which are given a lower HCl value. It might be a concern

that even for the least critical pipe, pipe number 490, a failure will result in more than 1.11% unsatisfied demand. An important result from the analysis is that none of the pipes were given an HCl value of zero, meaning that all the pipes are considered critical. According to Relnet, somebody will be affected by a pipe closure no matter where in the system it occurs. The people affected will experience a pressure below the required pressure, consequently their demand will not be fully satisfied. It is known that Umlazi Res. 1 supply zone experiences a great deal of leakage resulting from pipe bursts. Attempts are being made to reduce the leakage by decreasing the pressures in the area as much as possible (8). If the pressures are low to start with, it will be even more difficult, than if the pressures are higher, to deliver a satisfactory amount of water to the consumers when a pipe failure occurs. The balance between fewer leakages (requiring relatively low pressures) and satisfied demand (requiring relatively high pressures), needs to be investigated. If the structural conditions of the pipes were improved, it might be that higher pressures could be justified.

CARE-W: PI – PERFORMANCE INDICATORS Description of PI-tool Performance indicators are used to describe, follow up and manage an activity in a way that makes it possible to get rapid, relevant and comprehensive information. Such indicators also help in setting goals and achieving results (9). The CARE-W PI system is inspired by the International Water works association (IWA) performance indicators system for water supply services. CARE-W PI measures the performance of the water network with a range of key indicators. The tool can be used to study network development over time and to identify areas for improvement, and therefore supports the development of rehabilitation strategies. It also provides a source for benchmarking studies. It is possible to estimate evolution over time using different rehabilitation strategies and to compare the results to a “do nothing” approach. The effects of the measures are quantified using the PI, which shows the difference between the alternatives. The determination of effect may be based on “explanatory factors” which in some cases can be controllable for the utility (e.g. supply pressure or preventive maintenance), or uncontrollable factors such as temperature and precipitation. Lack of data does not prevent a useful application of the PI system, as the software is flexible and can be used even if not all the data are available. The water utility should give preference to the most important performance indicators (identified by the particular utility) and look at the databases for these indicators first. In this investigation the PI-system was used as “stand-alone”, however the main objective of the CARE-W rehab PI system is to be used as part of a more comprehensive tool, in close coordination with the other modules of the CARE-W system (6), (9), (1). The calculated PI-values for Umlazi Res. 1 are shown in Table 0.1. In Figure 0.1 the PI`s are compared to European guidance values.

Table 0.1 Calculated PI`s

Figure 0.1 Calculated PI indicators and guidance values (min./max.)

Summary/Discussion PI-results The calculated performance indicators were based on 2004 data. Due to the time limit it was not feasible to look at data for additional years for comparison. Results of the analysis show that the water losses in the area are extremely high compared to the guideline values. Also in comparison to other areas in Umlazi, the water losses in Umlazi Reservoir 1 supply zone are high. Mark Sheperd (2004) (8) writes in “Report on the Umlazi Water Loss Management Project (Phase 1): “It is possible to identify Umlazi Reservoir 1 supply zone as that zone with the highest consistent loss/non-revenue volumes.” The number of illegal connections, which account for at least 43% of the total water losses in the area, is a big concern. Considerable resources and a change in the residents’ attitude are needed to deal with the problem. The Real Losses value is also very high. Real losses can be reduced through direct intervention in the field, such as pressure management, active leak detection and repair programmes (1). In addition, the losses would probably be reduced if a rehabilitation program was implemented. Pipe rehabilitation decreases the number of burst and consequently the amount of water losses/real losses. The performance indicators for mains failures, pipe failures, joint failures and valve failures may not be very accurate due to reporting uncertainties of fault codes and other causes. Useful information can still be extracted from the results if these uncertainties are taken

into consideration. When comparing the results with European guidance values, it can be seen that the Umlazi values are very high. Unit running costs are not comparable to the European guidance values because of the differences in level of cost in South Africa and Europe. But, comparing the running costs in Umlazi with the running costs of previous years would probably be useful in giving an indication of the network’s change of state.

CARE-W: LTP – LONG TERM PLANNING Description of LTP Care-W LTP is based on the KANEW software which was developed at Karlsruhe University. The LTP module consists of three closely related tools, namely the Rehab Scenario Writer, the Rehab Strategy Manager and the Rehab Strategy Evaluator. The Rehab Scenario Writer is used for developing consistent scenarios, the Rehab Strategy Manager for simulation of long-term effects of specific rehabilitation options and alternative programmes for different pipe classes, and the Rehab Strategy Evaluator strives to find the best long-term rehabilitation strategy (1). This investigation focussed on the Rehabilitation Strategy Manager (RSM) tool. The RSM is the only one of the three tools that can be used as a standalone tool. The RSM is based on the cohort survival model and it calculates residual service life expectancies and annual rehabilitation needs of assets based on their service life distribution. The user determines a prognosis period and the RSM forecasts the annual length of assets which have to be rehabilitated within that time. The forecasted rehabilitation needs can be used to define, analyze and evaluate various rehabilitation strategies to find the most appropriate strategy for the selected network. The calculation is based on length, installation year and specific service life expectancies for the different asset types. Local experience and statistics of failure and rehabilitation activities in the past should be used to get realistic estimates of the asset life-span (1).

Analysis of asset data In Umlazi Res. 1 supply zone the dominant material used for mains is Asbestos Cement (AC). As much as 54.60% of the total stock is AC pipes. Unplasticised Polyvinyl Chloride (UPVC) is the second largest group, constituting 33.95 % of the total stock. The other asset types in the area are Polypropylene (PP), Modified Polyvinyl Chloride (MPVC), Steel (ST), Polycop (PC), High Density Poletylene (HDPE) and XX (XX is unknown asset type, but it is assumed that most of the XX pipes are AC pipes). Service life expectancies for the pipes of interest are proposed in Table 0.1 and Table 0.2. Table 0.2Table 0.1 shows expected service life of pipes in Durban, and Table 0.2 shows expected service life of the same pipes in Europe.

Table 0.1 Service life expectancies, Durban (pessimistic/optimistic) Service life expectancies (years) Asset type 100% 50% XX (AC) 10/15 15/25 AC 10/15 15/25 UPVC 20/30 30/40 MPVC 20/30 30/40 ST 20/40 40/60 PP 5/20 20/40 PC 5/20 20/40 HDPE 20/30 30/40

10% 35/40 35/40 40/50 40/70 60/100 40/70 40/70 40/70 (10)

Table 0.2 Service life expectancies, Europe (pessimistic/optimistic) Service life expectancies (years) Asset type 100% 50% XX (AC) 20/25 30/40 AC 20/25 30/40 UPVC 50/70 100/130 MPVC 50/70 100/130 ST 80/100 120/150 PP 50/70 100/130 1 PC 5/20 20/40 HDPE 50/70 100/130

10% 40/50 40/50 150/200 150/200 180/240 180/250 40/70 180/250 (11)

Comparison of Table 0.1 and Table 0.2 shows that all service life expectancies in Durban are very low compared to European values. In what follows, some of the results presented will be based on European values in addition to Durban values, to emphasize the impact that different service life expectancies have on the results. Only the most important figures are presented in this paper.

1

The service life expectancies for PC pipes are Durban values. Europe does not have values for PC pipes because that particular asset type is not used in that part of the world.

Figure 0.1 Inventory of asset type and installation year Figure 0.1 shows that steel pipes (ST) and pipes of the unknown asset type (XX) have a larger spread in age than the rest of the stock, with the newest pipes being around 5-15 years and the oldest pipes being more than 30 years. Most of the pipes are laid within the time period 1990-2004. Rehabilitation scenarios KANEW creates three scenarios to forecast the future (1): ¾ The optimistic scenario - best case scenario, describing the most beneficial future development ¾ The pessimistic scenario - worst case scenario, describing the most disadvantageous future development ¾ The trend scenario - the most probable scenario, describing the future development according to observed trends and expectations All scenarios start from the present situation. Figure 0.2 shows the pessimistic survival functions computed by the KANEW software. The survival functions are based on the values stated in Table 7.1. As an example, the pessimistic service life expectancy shown in Figure 0.2 indicates that 100% of the AC pipes are expected to be serviceable for at least 10 years, and 10% of the AC pipes are expected to be serviceable for at least 25 years.

Figure 0.2 Survival functions of asset types, pessimistic scenario

Figure 0.3 shows that, if the Durban estimations of service life expectancies are correct, approximately 60% of the pipes in Umlazi Res. 1 have a residual service life of 6-12 years and approximately 35% of the pipes have a residual service life of 17-27 years. Only a few pipes are expected to last for more than 17-27 years.

Figure 0.3 Cumulative residual service life distribution of assets, Durban values

Analysis of future rehabilitation needs Based on survival functions and age distribution of assets, a service life prognosis is generated for the year 2050. The prognosis indicates future rehabilitation needs for the different asset types. If no rehabilitation is initiated, the average age will have increase by one year every year and the residual service life will decrease correspondingly. If the residual service life is shorter than the average age, the network is relatively old (illustrated in the figures under, where the red line is above the blue line). A relatively young network on the other hand has a residual service life longer than the average age. Based on Durban values of expected service life of assets (see Table 0.1) and the proposed rehabilitation plan, Figure 0.4 indicates the future average age of the network and the corresponding residual service life using the pessimistic scenario. During the prognosis time the stock changes from predominantly AC pipes to predominantly MPVC pipes. According to Table 0.1, MPVC pipes have a longer expected service life than AC pipes, resulting in an increase in residual service life when AC is replaced by MPVC. According to the figure, residual service life will however not change a whole lot if the proposed rehabilitation program is implemented.

Figure 0.4 Future average age and residual service life, pessimistic scenario, Durban values Figure 0.5 shows the future average age and residual service life based on European values of expected service life of assets (see Table 0.2). Comparing the Durban figure with the European figure indicates that the latter is preferable to the first. An assumed longer service life of assets results in a higher average age and a longer residual service life.

Figure 0.5 Future average age and residual service life, pessimistic scenario, European values Figure 0.6 shows the future rehabilitation rates for the whole network (Umlazi Res. 1 zone), until year 2050. The figure includes the service life of the rehabilitation pipes. The red curve describes the pessimistic average rehabilitation rate, and the green curve describes the optimistic average rehabilitation rate. According to the medium service life expectancy the future rehabilitation rate for Umlazi Res. 1 zone is approximately 4.7 % today, then the rehabilitation rate decreases to 2.6% in year 2015. From year 2015 the rate increases to 3.5 % in year 2017. After year 2017 the rehabilitation rate decreases to 2.0% in year 2030, when the rehabilitation rate increases every year up to 2.8% in year 2050. The reason why the rehabilitation rate is increasing towards the end is that the “new” rehabilitation pipes are being replaced.

Figure 0.6 Future network rehabilitation rates, Durban assumptions Figure 0.7 shows the future rehabilitation need using European values for service life expectancies. According to the medium service life expectancy the future rehabilitation need for Umlazi Res. 1 zone is approximately 0.1 % from present to year 2012, when the rehabilitation need increases every year and reach a peak of 3.4 % in 2025. From year 2025 the rehabilitation need decreases every year, almost down to nil in year 2050. The reason for the bell shape is that the pipes in the area are laid within a short time frame. In addition, the European service life expectancies are much longer than the expected service lives in Durban, postponing the rehabilitation need. A similar shape would have appeared in Figure 0.6 (Durban service life expectancies) if the service lives of all the pipes had been longer. It is also interesting to note that in Figure 0.7 the rehabilitation rate reaches a peak of 3.5% while in Figure 0.6, 3.5% is the approximate average rehabilitation rate.

Figure 0.7 Future network rehabilitation rates, European assumptions Summary/Discussion LTP-results The main asset types in Umlazi Reservoir 1 supply zone is Asbestos Cement (AC) (54.60 % of total stock) and Unplasticised Polyvinyl Chloride (UPVC) (33.95 % of total stock), mainly laid around 1991. XX (unknown material), Polypropylene (PP), Modified Polyvinyl Chloride (MPVC), Steel (ST), High Density Poletylene (HDPE) and Polycop (PC) are the other materials used in the area. Asset types expected to be used for rehabilitation are predominantly MPVC and PP. The cumulative residual service life distribution of assets (see. Figure 0.3) shows a modest need for rehabilitation of the network the coming 10 years, but the rehabilitation need will

most likely be considerable in 10 years and in 22 years. Replacement of assets the next 10-12 years will mainly be related to AC pipes. Expected service lives of pipes in Umlazi are shown to be surprisingly low compared to expected service lives of the same asset types in Europe. If the estimations of service life expectancies are correct, a comprehensive rehabilitation program should be planned, and renovation should start within a few years. According to the proposed rehabilitation plan, the residual service life of assets in Umlazi Res.1 zone will not change much. Whether the residual service life should be kept constant at the same level, increase or decrease can be debated. To keep a constant residual service life, some investments have to be made immediately, but not more than what is needed to keep up with the aging of the pipes. If an increase in residual service life is chosen, large rehabilitation investments have to be made. A decrease in residual service life postpones the large investments to the next generation. Rehabilitation plans are concerned about the balance between the three approaches, and the economic consequence of the alternative chosen is considerable. When the residual service life increases, the relative average pipe age will decrease, resulting in a younger and healthier network. Even though investments would have to be made for rehabilitation, it is likely that the lower maintenance costs would lower the total cost in the long run. In addition, interruption of water supply (due to unexpected pipe breaks) and major burst and leaks that have negative social consequences would be less frequent. On the other hand, if the resources are limited and it is critical to provide water to new areas, which is the Durban situation at the moment, a different rehabilitation strategy might be better. Perhaps the best management strategy would be to keep a constant residual service life, or even allow for a decrease in residual service life, until the majority of the residents are supplied with potable water. It is possible that, only then, it will be appropriate to start a comprehensive rehabilitation program. CONCLUSION/DISCUSSION – PRACTICABILITY OF CARE-W IN DURBAN One of the major differences between managing water supply in South Africa and managing water supply in Europe is that South Africa is a relatively young country (with a relatively young water distribution system) and is more concerned with expanding the water network than in renovating the existing network. There are still thousands of households in South Africa that have inadequate water supply, and reaching those people as quickly as possible is a major challenge for the country. In addition, narrowing of the revenue streams and insufficient number of employees in water related positions make it difficult to allocate resources on rehabilitation. The prevalence of illegal connections is another evident challenge in South Africa. Especially in townships, like Umlazi, the handling of illegal connections is a problem. Umlazi is a very dangerous township. Maintenance people have been killed on duty, and armed guards are now brought along for safety when repair work has to be performed. Workers have been attacked when attempts were made to locate illegal connections. Vandalism, such as damaging pipes, is also a problem. The dangers of working in the area, the prevalence of illegal connections and the vandalism are slowing down the process of maintaining an effective water supply. The problems of illegal connections and vandalism also, at some extent, make it difficult to get reliable outputs from the Care-W software. Not all parts of the Care-W system are dependent on data related to the challenges mentioned above, but in some cases there are other obstacles that need to be overcome. In the following, an attempt is made to assess the practicability of Relnet, PI-

tool and LTP-tool in Durban. The Care-W tools Aquarel, Failnet-Reliab, Care-W_Fail and Care-W ARP, which were not tested in the project, are considered not feasible due to the type of failure information needed to run the programs. Relnet Relnet is dependent on an Epanet input file, and the Relnet results can only be as reliable as the Epanet model, determined by calibration. Accordingly, there are two challenges that need to be overcome; establishing an Epanet model and performing a good calibration of the model. Durban has not had a practice of establishing hydraulic models to existing water networks, consequently new models have to be built for the areas to be analysed. Network data could not be transferred directly from Arc View to Epanet. Therefore, an Arc View Model Generator Extension (HydroGen) was used to link the two programs. In the transferral, simplifications had to be made. Further, the extension did not support the addition of reservoirs, pumps, tanks or many of the other features available in the Epanet program. If DMWS wants Epanet models to be established in the future, it is recommended that other alternatives for data transferral are tested. Calibration of models may also be difficult if field measurements are insufficient. The insufficiency may result from damaged water meters, too few meters in the area or too short measurement time periods. A system for tracking faulty meters and storing data on the water meters’ condition could be implemented to ease the measurement work. Establishing measurement zones in the area would help in monitoring the water distribution, which is especially challenging in an area with a high incidence of illegal connections. Enough data can be made available for running Epanet and subsequently Relnet. However, at the current time it demands quite a lot of effort in first establishing a calibrated Epanet model. Nevertheless, when an Epanet model is established, it may be used for other applications in addition to being an input file for Relnet. PI-tool Several of the Performance Indicators used in the analysis are not directly comparable to European values. The setting for the analysis is very different and some of the challenges in Umlazi do not exist in Europe. Some of the indicators may therefore be more useful if compared to indicators in areas with the same setting and/or compared to indicators in the same area by looking at development over time. Op 24; Water losses per connection is an example of an indicator for which it would be useful to have historical data. By comparing values for different years, the performance indicator could give an indication of the development of illegal connections in the area, assuming the real losses are known. However, in formal areas, without illegal connections, the values for water losses and real losses should be directly comparable. Failures in relation to mains, pipes, joints and valves may be difficult to quantify. One reason for the difficulty is reporting uncertainties of fault codes. More experienced people in all levels of the reporting system would probably result in more accurate failure data. But, even with the existing system, the calculated PIs will give a good indication of the situation. Before the failure data is processed in PI-tool it has to be extracted from a database. Extracting data from the database is currently difficult. There is a system used for larger regions like Umlazi, but extracting data for smaller sub areas, like Umlazi Reservoir 1 zone, is however very time consuming. A system should be developed to extract failure data from smaller areas more efficiently. There are several indicators that can be analysed in PI-tool, requiring a corresponding amount of data if all the PIs are to be considered. However, one of the great advantages

with the program is, that only the PIs considered relevant need to be analysed, making it very flexible.

LTP LTP tool is also quite flexible when it comes to the amount of data required. The vital data is pipe material, installation year and pipe length. An important aspect of LTP-tool is that it is dependent on experienced people who can make reasonable judgments about expected service life of materials in the selected area. In addition, the person should be competent in making a realistic list of rehabilitation assets. If information about the efficiency of rehabilitation and an economic evaluation of the rehabilitation strategy is desired, more information is needed. At present there is unfortunately no system for storing these data in Durban. The first steps in the program; analysis of stock, prognosis on future rehabilitation needs and prognosis on future rehabilitation work can, however, be used without difficulties. All things considered, the Care-W tools tested in this project are fairly flexible concerning data input required in the way that the tools can be adjusted to the data available. However, great effort has to be made for an integrated recording of information, which is needed to match the specific data format required by Care-W. Because Durban does not have a fully integrated system for recording water supply network information - for instance, faults are recorded in a database which is separate from the pipe specification data - the data need to be collected and coordinated before they can be used by the tools. The need for a more integrated information system is something that has been recognized independently of this investigation by eThekwini Water Services, and is part of their future planning. If a fully integrated system for recording network information is not available, the CARE-W system may act as a very good guidance for the type of data needed to obtain a modern maintenance of water networks. When the information need is examined and the data collected, Care-W will become an important tool to improve the delivery of water services by providing an overview of long-term water network rehabilitation needs. Subsequently, when a fully integrated system for recording water supply network information is obtained, Care-W will provide a priority list of rehabilitation projects and a short and long term investment plan. Apart from the issue of access to integrated data, this specific investigation has demonstrated that CARE-W has the flexibility to deal with the special circumstances which might be encountered in other cities in South Africa, or indeed throughout Africa. The Umlazi Township was deliberately selected to provide a severe test of its adaptability. ACKNOWLEDGEMENT “The CARE-W system has been supported by the European Union under the 5th framework program for research and development”

REFERENCES 1. 2.

3.

4.

5.

6. 7.

8. 9.

10. 11.

Sægrov et al. (2004). Care-W final report Brocklehurst Clarissa (2001). Durban Metro Water. Private Sector Partnerships to Serve the Poor (Case study). Downloaded 17.02.05 from http://web.mit.edu/urbanupgrading/waterandsanitation/resources/caseExamples/c ommunity-water.html#durban-metro-water Scruton Simon, DMWS (2004). Implementation of a water demand management programme in Durban, South Africa. Downloaded 07.06.05 from http://www.wdm2004.org/ conferenceprogram/technical/Wednesday/wed%207/simon.htm Sanzin Mike, Technician and Simon Scruton, Engineer: Special projects, Unaccounted for water, non revenue water dept. DMWS (April 2005). Conversation and writing support Brouckaert Chris, Professor University of KwaZulu Natal (UKZN) (April-May 2005). Inventor of Epanet calibration program. Chris Brouckaert was preparing an Epanet calibration program, primarily made for allocating demand to particular areas Røstum et al. (2004). User Manual, Care-W Rehab Manager Herrero Manuel, Matthew Poulton, Marcella Volta, Ingrid Selseth and Frøydis Sjøvold (2004). Annual rehabilitation plan for the water distribution network of Nidarvoll, Trondheim- Care-W test case. SINTEF report no. STF22 A04704 Sheperd Mark (2004). eThekwini Wate Services. Report on the Umlazi Water Loss Management Project (Phase 1) Sjøvold Frøydis (2002). Indikatorer for tilstandsvurdering av vannledningsnett. SINTEF rapport STF66 A02109 Macleod Neil, Head of Water and Sanitation DMWS (March 2005). Conversation Selseth Ingrid and Axel König (2004). Langtids rehabiliteringsbehov for vann- og avløpsnettet i Larvik kommune. Delrapport Nr.10, hovedplan vann og avløp- Larvik kommune. SINTEF rapport STF22 F04705

Other References: Cook Peter, Superintendent: Southern region, Water Network DMWS2 (April 2005). Conversation Crowe Clayton T., Donald F. Elger and John A. Roberson (2001). Engineering Fluid Mechanics (7th edition). USA: John Wiley & Sons, Inc. Eisenbeis Patrick et al. (2003). WP2 Description and Validation of Technical Tools. D4-Report on Tests and Validation of Technical Tools. Report No. 2.2 EPA (2000). Epanet 2. Users Manual. Downloaded 26.01.05 from http://www.epa.gov Farshad Fred F. and H. H Rieke (2003). Flow test validation of direct measurement methods used to determining surface roughness in pipes (OCTG). Chem. Eng. Trans., 3: 1511-1517 Hafskjold, Sigurd, Researcher, SINTEF (June 2005). Conversation Macleod Neil, Head of Water and Sanitation DMWS (March 2005). Conversation Metropolitan Durban (1999). Fresh Water Resources. Downloaded 17.02.05 from http://www.ceroi.net/reports/durban/issues/fshwater/index.htm Mlaba Cllr Obed, Dr Micheal Sutcliffe, Cllr Nomusa Dube, eThekwini Municipality

(2003). eThekwini Municipality. Integrated Developmentplan 2003-2007 Pietersen Steve, GIS/Survey Supervisor Strategic planning DMWS (March- May 2005). Information extracted from GIS Pilay Seeland (2005). Seagate Crystal Smart Viewer for ActiveX. Computer program for extracting fault codes in Durban Metropolitan Area. Given access to by Seeland Pilay, System Analyst, Information services DMWS Pfaff Bill, Manager Strategic Planning DMWS (2004). Key statistics-Water Services, 07.12.04 Rodrigues Pedro, Manager Water Network: operations DMWS (May 2005). Conversation Røstum Jon, Researcher, SINTEF (2004). Help function, PI-tool Sægrov et al. (2000). Kursdagene NTNU 2000. Optimal fornyelse av vannforsyningsnett. Planlegging og teknolo|gisk grunnlag Sægrov Sveinung, Professor, NTNU(October 2004). Lecture in Pipe Technology Sægrov Sveinung, Professor, NTNU (May 2005). Conversation Technische Universität Dresden (2005). Kanew Software for the Analysis of Infrastructure Rehabilitation Strategies. PowerPoint Presentation. Downloaded 15.05.05 from http://www.tu-dresden.de/stadtbau/stbw_kanew.html. LaGrega Micheal D., Philip L. Buckingham and Jeffrey C Evans (2001). Hazardous waste Management (2nd edition). USA: The McGraw-Hill Companies, Inc. Gauffre Pascal and Katia Laffrechine (2003). Brief Help Guide: Care-W_ARP