Modelling the Leakage Rate and Reduction Using Minimum Night Flow Analysis in an Intermittent Supply System
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Case Study System
2.2. DMA Establishment
2.3. Instruments and Measurements
2.4. Leakage Modelling
2.5. Feasibility of Leakage Reductions
3. Results and Discussion
4. Conclusions
4.1. Minimum Flow Analysis in Intermittent Supplies
- One-day minimum night flow analysis (MNF) cannot be used to estimate the leakage rate in intermittent supplies, because water keeps flowing during night time to fill customers’ tanks in the network. Therefore, the experimented zone (DMA) should be supplied continuously for several days until the zone is saturated, or the customer ground or elevated tanks in the network are completely full, and the readings start to closely repeat themselves.
- MNF could therefore occur at night or day time, even if part the customer base are active as long as the ground tanks are full and customers do not pump water from the ground to the elevated tanks. In Zarqa, the saturation of the DMA started after 63 h of continuous supply and MNF was occurring between 12:00 a.m. and 7:00 a.m. This challenge requires more careful estimation of the legitimate nighttime consumption that is found to be a sensitive parameter in leakage estimation and modelling.
- Generalising the leakage rate at the time of the MNF for all the time of the day causes overestimating of the daily leakage, because of the usually lower pressures during the day. For this reason, the night day factor in Zarqa is a reduction factor (<24 h/day), being 14 h/day.
- While MNF analysis is reasonably accurate at a DMA scale, upscaling its result for the entire system is uncertain and sensitive. One or several DMAs cannot satisfy the diversity of the operating conditions in the network in terms of pressure, flows, pipe length and the number of connections. Therefore, estimating the leakage of the whole system has to be verified through several methods before it is used for full-system leakage reduction modelling.
4.2. Leakage Reduction Modelling in Intermittent Supplies
- The leakage component analysis model (BABE) analyses only a small part of the leakage (26% in the Zarqa case) and the remaining part is considered as hidden losses where the recoverable and unavoidable portions are not known. Increasing the Infrastructure Condition Factor is not sufficiently influential in the studied case, and the model may require an adaptation study for the intermittent supply context.
- Analysing the potential water savings of different leakage reduction policies independently and separately is currently possible. However, this approach is likely overestimating the potential savings significantly, due to the inter-dependency of the different policies, leading the potential savings to be more than the volume and cost of the leakage. In all cases, estimating the benefits of the frequent leakage detection survey seems to be over-estimated, and further investigation is required to clarify and confirm this issue.
- The inter-dependency relationship between the pressure management and active leakage control should be investigated too. Pressure reduction limits the failure frequencies and lowers the potential of leakage detection as leaks become harder to detect. Therefore, future leakage reduction modelling would be more reasonable when considering the influence of a specific leakage reduction policy (e.g., pressure management) on the potential of other reduction policies (e.g., ALC).
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Date | MNF | MNF Time | LNC | LNC Duration | NNL | NDF | Daily Leakage | Leakage Volume | Leakage Level |
---|---|---|---|---|---|---|---|---|---|
m3/h | a.m. | m3/h | h | m3/h | h/day | m3/day | m3 | % SIV | |
5 January | 15.0 | 12:15 | 1.88 | 2.0 | 13.1 | 14.2 | 185.8 | 932.7 | 25.5% |
2.51 | 1.5 | 12.5 | 14.2 | 176.9 | 888.1 | 24.3% | |||
6 January. | 16.4 | 4:45 | 1.88 | 2.0 | 14.5 | 14.2 | 205.0 | 1029.4 | 28.2% |
2.51 | 1.5 | 13.9 | 14.2 | 196.2 | 984.8 | 26.9% | |||
7 January. | 14.8 | 7:15 | 1.88 | 2.0 | 13.0 | 14.2 | 183.5 | 921.3 | 25.2% |
2.51 | 1.5 | 12.3 | 14.2 | 174.6 | 876.7 | 24.0% | |||
Average | 15.4 | - | 1.88 | 2.0 | 13.5 | 14.2 | 191.4 | 961.2 | 26.3% |
2.51 | 1.5 | 12.9 | 14.2 | 182.6 | 916.6 | 25.1% | |||
Average 5 & 7 January | 14.9 | - | 1.88 | 2.0 | 13.0 | 14.2 | 184.6 | 927.0 | 25.4% |
2.51 | 1.5 | 12.4 | 14.2 | 175.8 | 882.4 | 24.1% |
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AL-Washali, T.; Sharma, S.; AL-Nozaily, F.; Haidera, M.; Kennedy, M. Modelling the Leakage Rate and Reduction Using Minimum Night Flow Analysis in an Intermittent Supply System. Water 2019, 11, 48. https://doi.org/10.3390/w11010048
AL-Washali T, Sharma S, AL-Nozaily F, Haidera M, Kennedy M. Modelling the Leakage Rate and Reduction Using Minimum Night Flow Analysis in an Intermittent Supply System. Water. 2019; 11(1):48. https://doi.org/10.3390/w11010048
Chicago/Turabian StyleAL-Washali, Taha, Saroj Sharma, Fadhl AL-Nozaily, Mansour Haidera, and Maria Kennedy. 2019. "Modelling the Leakage Rate and Reduction Using Minimum Night Flow Analysis in an Intermittent Supply System" Water 11, no. 1: 48. https://doi.org/10.3390/w11010048
APA StyleAL-Washali, T., Sharma, S., AL-Nozaily, F., Haidera, M., & Kennedy, M. (2019). Modelling the Leakage Rate and Reduction Using Minimum Night Flow Analysis in an Intermittent Supply System. Water, 11(1), 48. https://doi.org/10.3390/w11010048