In England and Wales alone, water companies manage a network of 335,000 km of pipes, with around 24 million connections to homes and industrial properties. This makes the task of water network management highly complex.

Water and sewerage companies face a vast range of challenges in the effective maintenance of their delivery networks. One constant challenge is tackling water leakage issues. Many of the water pipes in the UK were installed many years ago and are now old and more prone to leakage. In an attempt to respond to this, over the years the industry has invested vast amounts of capital into its assets and infrastructure. Is the best use being made of these considerable investments?

With flow monitoring becoming an increasingly important part of a water company’s business, it is crucial that:

  • Good measurement practice is followed at all times
  • Established procedures and processes are used and regularly updated
  • Staff training and competence is recorded and regularly verified

This helps to ensure that the data obtained from the metering network is reliable and can be used in demand forecasting and strategic planning. These data also act as inputs to a range of numerical analysis techniques such as gross error detection, uncertainty analysis and data reconciliation. These techniques are cost-effective methods of improving the effectiveness of network monitoring and are now being frequently applied in the water industry.

Flow measurement errors and the use of Uncertainty Analysis

A flow meter’s response depends on the way in which it is used. Many meters depend on assumptions about the velocity profile of the fluid in the pipe. Most flow meters assume a “fully developed” profile: that is an ideal profile based on flow in a perfectly straight pipe. However, distortions caused by bends, valves and other fittings upstream will invalidate the assumed profile, and hence affect the accuracy of the meter. For example, swirling flow will affect the rotor of a turbine meter and may cause the instrument to either over or under read.

Uncertainty is the degree of doubt about a measurement. Undertaking an analysis of the uncertainty of a measurement involves identifying the main influences that affect the result of the measurement such as the swirl mentioned previously. This will result in a number which represents the “margin of error” in the measurement. Applying this across the network gives an uncertainty in the water balance; that is, a margin of error within which the mass balance should lie. Identifying the main contributors to this figure can ensure that capital expenditure is targeted to areas in the network where it will produce the most benefit.

“undertaking an analysis of the uncertainty of a measurement involves identifying the main influences that affect the result of the measurement such as the swirl”

However, the rigour with which uncertainty analysis is applied in the water industry currently varies widely. Some companies use only the manufacturers’ accuracy claims, while others devote effort examining meter history and location to identify the key influences. In contrast, in the oil and gas industry uncertainty analysis is integral to the business. This is driven principally by the high value of the product and companies simply cannot afford inaccurate flow measurement. Accounting for uncertainty in flow measurement allows them to see the ‘bigger picture’ – enabling them to calculate financial exposure on fields and make strategic decisions on such issues as investment in metering infrastructure.

Rigorous uncertainty analysis lets a company go beyond simply saying: “Our distribution input is 950 ±30 Ml/day.” It will identify the main contributors to the uncertainty, in terms both of the key meters and of the sources of uncertainty in those meters. The nature of uncertainty is such that only by addressing these aspects can the uncertainty in the system be improved. Detailed uncertainty analysis can therefore ensure that capital expenditure is targeted to where it will produce the most benefit. As a first, and inexpensive, step to addressing the existing demand and leakage challenges, the water industry would gain real benefits from adopting the practice of the oil and gas industry and applying rigorous uncertainty analysis at the heart of their daily network monitoring procedures.

Water leakage

In England and Wales, 3.3 billion litres of water are lost through leakage in the delivery networks. The scale of this problem is set to get worse since the UK population is projected to increase by nearly 15% over the next 25 years. This increase is predicted to be greater in areas that are already classed as water stressed, such as London and the South East. In addition to this, Environment Agency predictions show that water levels could drop by between 10% and 15% over the next few decades due to climate change. This represents a growing gap between what the industry can supply and what is required by customers.

“there is a range of modern numerical analysis techniques that can be used to assist in the management of water pipeline networks, including data validation and gross error detection, and data reconciliation”

To manage leakage in a network, water utilities have to measure volumes going into it via Distribution Input (DI) meters, then at various points across the network; district meters, domestic meters and non- domestic meters. By comparing measurements at each point, leakage can be estimated and traced to allow fixes and leakage reduction. There is a range of modern numerical analysis techniques that can be used to assist in the management of water pipeline networks, including data validation and gross error detection, and data reconciliation.

Data validation and gross error detection

The first and most basic of these tools is a general technique known as data validation. This is effectively a collection of tools that are used to assess the quality of the acquired data. Part of this suite of tools applies numerical filters to acquired data to ensure that it ‘makes sense’. For example, it is possible to set upper and lower limits on measured flows and to reject data that are outside this range. This is, in effect, a way of detecting gross-errors in the system. Another component of this technique is to set limits on the rate of change of measured quantities to detect spikes and other transient behaviour in the network. This allows the operator to base further analyses on data that lie within the normal operating parameters of the network.

Often analyses are not based on individually acquired points, but on data that have been smoothed or averaged over a period, which allows the operators to determine longer term trends in the data and assist in demand forecasting. This can be taken further by the application of neural networks or generic algorithms where a computer- based neural network ‘learns’ the behaviour of a metering network and then highlights anomalous behaviour.

Data reconciliation

In the past, water companies have used mass balance techniques to estimate the amount of leakage and other unmetered abstraction in their distribution and waste treatment flow networks. Data reconciliation (DR) takes this a stage further by identifying the instruments most likely to be responsible for imbalances, and allowing water companies to target maintenance to where it is most required.

DR is a calculation technique that is increasingly being used by water companies to monitor the quality and reliability of flow measurement data acquired from trunk mains. It performs a network self-check to ensure that all of the measuring devices are consistent with each other. Using this technique, engineers may quickly identify which meters are reading outside their uncertainty bands and take appropriate remedial action. It can also be used to determine the level of leakage in a network.

The basic premise behind data reconciliation is that it uses a mathematical technique known as ‘least squares’ to adjust the values of the measured flows so that they all exactly obey the balance equations in the network. The magnitudes of the numerical adjustments required to do this are compared with the uncertainty of the measurement (to 95% confidence) and a “quality index” is calculated for each measurement point in the network. If the value of this index is less than unity then the measurement is deemed acceptable; if greater, then there is an issue with the measurement that should be investigated.

The calculations are normally performed using DR software installed as part of the water company’s data historian. It is common that the calculations are triggered along with others during the minimum nightline period, usually on a daily basis. In this way it can act as an “early warning” system for instruments drifting out of calibration or the development of leaks in the trunk mains. Companies using this technique have much more confidence in their trunk main flow data, as it is continuously being checked for consistency against the rest of the network. It also can substantially reduce OPEX due to reduction in the level of maintenance required.

In many water distribution networks, data acquired from flow meters can be affected, to a varying degree, by electromagnetic, mechanical or acoustical interference. This can significantly alter the numerical value picked up by the data logger. This not only seriously undermines the quality of the data, but also reduces the effectiveness of the analysis techniques. Statistical filters are available that are designed to remove such anomalous data.

Multiple challenges

A number of global trends, such as climate change, are also having an effect on the water industry. The water industry must alter public perception in industrialised nations that water is an inexhaustible resource that is cheap and plentiful, particularly because rainfall patterns changes are likely to result in more regular infrastructure failure.

The industry has to manage customer behaviours to assist in driving innovation. Like many sectors within engineering, there is pressure brought to bear by an aging workforce, resulting in a shortage of key skills. This may mean a substantial investment in re-training to ensure that they have the skills required in the future.

Along with all of this, water companies still have to meet the demands of the regulator. For example, UK-based water companies are obligated to perform an annual water balance calculation, which compares the water volume from sources of supply to that of the demand. There is also the SIM (Service Incentive Mechanism) score (out of 100), which is part of the regulatory process for setting the price and service package that each of the companies must deliver. Companies must publish these scores independently alongside other information about their performance.

Smart metering

With this range of array of challenges facing it, the industry is being driven to adopt new technologies and protocols to make the most of the resources that they have at their disposal, such as smart metering.

Smart meters measure and transmit a customer’s water usage data to supply companies. They can operate on their own or as part of a wireless network, transferring readings periodically from pipes connected to an individual property using a range of different technologies, so that both the customer and the provider can monitor the amount of water being used by the household. They are often referred to as AMI (Advanced Metering Infrastructure) in that the meter can transmit data to the water company and also receive data from it. This removes the need for staff to have to physically check, or to inspect the meter, reducing the cost of maintenance.

There are several types of meters available from different companies falling into multiple categories such as mechanical meters, simple digital meters and upgraded mechanical meters. These meters cost in the region of £250 to install and are paid for by the customer.

It is anticipated that smart meters will reduce bills in most households by assisting in the identification of leaks. Some companies are also considering the introduction of “seasonal tariffs” which is expected to help with demand management. Practically, this means that customers would be charged more during periods of water scarcity. These meters are much cheaper to read than existing equipment and are generally more accurate. Because they can transmit a large volume of data they allow the company to manage their network much more efficiently.

Big data

This is a relatively new development that is being enthusiastically embraced by a considerable number of industrial companies. It means that all of a company’s vast amount of operational data can be uploaded to the Cloud. This provides enough space for the data and a single, central area from which it may be analysed. Companies use statistic analysis methods to identify trends present in the data that may not be apparent over shorter time periods, or by using a smaller volume of data. Once these trends have been identified, the company then will change practices, equipment or personnel to minimise or eliminate their adverse effects – saving considerable revenue in the process. This is a development that promises to enhance the efficiency of many businesses, including water companies in the very near future.

It can be done – Singapore

Singapore’s modern water supply network boasts the benefits of smart technology and government investment. Its Smart Water Grid incorporates sensors, meters, digital controls and analytic tools throughout the island, including 300 multi-parameter probes to detect both leaks and water quality issues in real-time. This is an example of how government and business can combine expertise with clear requirements to deliver reliable and sustainable water supply for generations to come.

Meeting 21st Century demands

Optimising data use is an operational imperative, especially to water companies under environmental, regulatory and resource pressure. Failure to protect significant metering investments, by not complementing it with modern, cost-effective, data analysis techniques, risks increased capital and operational expenditure through poor targeting of effort.

Gross error detection, uncertainty analysis, data reconciliation and smart metering will give water companies much more confidence in the reliability of their data and the resilience of their networks from trunk main level, down to the supply to individual properties. These techniques, along with recent advances in electronics and computing will go a long way to meeting the challenges facing water companies in the 21st Century.