Currently, the estimated daily personal water consumption rate in the UK is on average 142 litres/day (as per 2020), equating to an estimated total daily usage of 14 billion litres per day.
Worldwide, climate change will have a significant impact on the volume of useable freshwater. By 2025 it has been estimated that two-thirds of the world’s population may face water shortages.
Action is required now to reduce demand, increase supply and apply the principles of a circular economy to meet future freshwater requirements.
There could be enough water to meet the world’s growing needs, but only if we dramatically change the way water is used, managed, and shared.
In recent years, the UK Water Industry has made great strides in leakage reduction. However, with the UK regulatory body Ofwat requirements for a 16% reduction in leakage by 2025 much more work is required.
Therefore, to reduce leakage levels to those required by the water regulator, it will be important to use new technologies in leakage reduction.
In recent years, the availability of inexpensive computing power and measurement databases has enabled the development of powerful data analysis techniques that allow metering networks to be monitored daily.
Such techniques can give operators details about meter performance and leakage and are much more effective than the traditional water balance calculation over the distribution network.
Flow measurement
Flow metering is essential for measuring water usage and managing water supplies. Most water meters around the world are small and primarily used to record domestic water consumption.
However, larger meters, while smaller in number, measure an equivalent volume of water and are key to managing both resource and demand.
It is principally through the use of larger meters that we quantify how much water is being abstracted from underground aquifers, rivers and other water bodies to provide clean water supplies to our cities. Both small and large meters are, therefore, essential for effective, economic and sustainable water management.
The need for accurate measurement on large diameter transmission (trunk) mains is of vital importance to the global water industry, to optimise water resources, accurately estimate leakage and calculate the water balance across the water distribution system.
A significant proportion of modern flowmeters rely on assumptions about the flow profile in the pipe. Bends, valves and other pipe components upstream of the measurement device will affect the assumed flow profile and the accuracy of the meter.
Uncertainty is the degree of doubt about a measurement. Undertaking an analysis of the uncertainty involves identifying the main influences that affect the final measurement.
This will result in a number, which represents the margin of error in the measurement. Applying this across the network gives 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.
However, the rigour with which uncertainty analysis is applied in the water industry 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.
Therefore, the water industry would gain real benefits from adopting the practice of the oil and gas industry, by applying rigorous uncertainty analysis at the heart of their network monitoring procedures.
With flow monitoring becoming an increasingly important part of a water company’s business, it is, therefore, 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.
This data also acts 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.
Modern digital analysis techniques
TÜV SÜD National Engineering Laboratory has undertaken extensive research into digital analytical techniques to improve the information gathered by modern flowmeters. This is based on a huge database, which has been created by testing a range of different flowmeters under a range of different conditions.
There are two types of computer models used for solving engineering problems such as those experienced in oil and gas production - physics-based models and data-driven models. These two classes of computer models differ from the way they represent physical processes.
Physics-based models attempt to gain knowledge and derive decisions through explicit representation of physical rules and generating hypotheses regarding the underlying physical system.
These models are driven by physical processes and can normally be described by a set of mathematical (theoretical) equations.
For example, Navier-Stokes (N-S) equations explain the motion of fluids and can govern Newton’s second law of motion for fluids.
On the other hand, data-driven models uncover relationships between system state variables without using explicit instructions.
These models employ algorithms to perform statistical inference and pattern recognition wherein a model maximises its performance through an iterative learning process.
It should be noted that such models do not contain the full complexity of the true physical phenomenon.
Instead, they provide a less complex (but valuable) abstraction that approximates the real system.
As these models do not necessarily require knowledge about the physics of the processes, they are very flexible when testing different hypotheses and making predictions.
We have developed a range of data-driven models for the oil and gas industry, and these are now being considered for use in the water industry. These models include:
Condition-based monitoring (CBM)
CBM can be used to determine the health of the flowmeter and also to monitor the calibration requirements of the device to understand if it is possible to move from a time-based calibration approach to a more dynamic calibration approach.
By making use of the diagnostic data that most modern flowmeters generate it is possible to use data analytics to determine the health and performance of them.
Data validation and reconciliation (DVR)
One cost-effective way of increasing confidence in flow meter data accuracy is to use a technique known as DVR. This is a statistical method used to evaluate the quality of flow measurement in many different types of industrial plant, from simple systems consisting of only a few measurements to complicated systems with several hundred.
Due to the large number of calculations involved, DVR is particularly suited to software applications.
First and foremost, DVR may be used as a diagnostic tool to pinpoint exactly which meters are operating outside their uncertainty bands.
This may indicate that operators have made incorrect assumptions about the uncertainty of the meter. This can be changed and the reconciliation re-run with the new value. Alternatively, it could mean that the meter has drifted out of calibration or that a fault has developed.
Either way, the ability of the technique to highlight anomalies will allow operators to target maintenance at specific equipment. This allows plant operators to make the most of the data that they have – with the accompanying financial and operational benefits.
Fault prediction analysis
By making use of historical data and using machine learning techniques it should be possible to predict where leakage is likely to occur in the water networks.
Combining multiple data analysis techniques such as these will allow modern software techniques to be developed that will enable water companies to:
• Verify the performance of modern electronic flowmeters;
• Perform network analysis and identify leakage in their networks;
• Predict where leakage events may happen in the future.
Data is the most valuable asset
Optimising data utilisation 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 and cost-effective data analysis techniques, risks increased capital and operational expenditure through poor targeting of effort.
Therefore, smart metering and network analysis will have to be used together to achieve the improvements necessary to meet the challenges facing the water industry today.
This will give water companies much more confidence in their data, alongside their investment decisions and operational expenditure levels.
The application of these techniques, along with the recent advances in electronics and computing power, will give water companies the tools to meet the challenges facing them in the 21st century.
For more information: Visit: tuvsud.com. The article was written by Carl Wordsworth, head of water sector at TÜV SÜD National Engineering Laboratory
Feeling the pressure
