By Carl Wordsworth, Head of Water Sector at TÜV SÜD National Engineering Laboratory. https://www.tuvsud.com/en-gb
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. Currently, the estimated daily personal water consumption rate in the UK is on average 142 l/day1 (as per 2020) equating to an estimated total usage of 14 billion l/day.
By mid-2041, it is estimated that the UK population will rise to over 72 million people, increasing this demand further. It is expected that the estimated population increase will probably occur in areas of the country where water scarcity is likely to already be an issue, i.e. the south-east. This coupled with climate change is going to have a significant impact on the volume of usable freshwater available. By 2025 it has been estimated that two-thirds of the world’s population may face water shortages.
In recent years the UK Water Industry has made great strides in leakage reduction. However, with the Ofwat requirements for a 16% reduction in leakage by 2025, much more work is required. Therefore, in order to be able to reduce leakage levels to those required by the water regulator, it will be important to use new technologies in leakage reduction. TÜV SÜD National Engineering Laboratory is taking digital analytical technologies that have been developed for the oil and gas industry and applying these to the water industry.
In the last few 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. A range of techniques will be discussed here and an explanation of how they could be used for leak detection and prediction will be given.
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, whilst 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 of 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 flow meters 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. For example, swirl in the flow will impact the rotor of a turbine meter and, depending on the direction of the swirl, will cause an under or over-reading.
By using modern data analytical techniques, it is possible to analyse the diagnostic data that is generated by most modern electronic flow meters and use this to determine meter performance, locate leaks and predict future leaks in water networks.
Modern digital analysis techniques:
TÜV SÜD National Engineering Laboratory has undertaken extensive research into digital analytical techniques to improve the information gathered by modern flow meters. Based on a huge database of testing a range of different flowmeters under a range of different conditions, TÜV SÜD National
Engineering Laboratory has developed a range of software tools to help develop:
- Data visualisation tools – for historical data evaluation
- The use of flow meter diagnostic data – in the development of fault diagnostics
- Remote monitoring – looking at undertaking a health assessment of the flow meter
- Predictive analysis – by utilising machine learning tools to predict the remaining useful life
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 in the way they represent physical processes.
Physics-based models attempt to gain knowledge and derive decisions through the explicit representation of physical rules and generating hypotheses regarding the underlying physical system.
Physics-based models driven by physical processes 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. Because these models do not necessarily require knowledge about the physics of the processes, they are very flexible when testing different hypotheses and making predictions.
Condition-based monitoring (CBM)
Condition-based Monitoring can be used to determine the health of the flow meter and also to monitor the calibration requirements of the device to understand if it’s 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 flow meters generate it is possible to use data analytics to determine the health and performance of the flow meter.
Recently TÜV SÜD National Engineering Laboratory has undertaken studies looking at both Coriolis meters and ultrasonic flow meters. The knowledge gained from these two studies can be transferred to electromagnetic flowmeters for use in the water industry. TÜV SÜD National Engineering Laboratory now wants to move to the next stage of development in condition-based monitoring by transferring the knowledge it has gained in other industries and applying it to the water industry.
Data validation and reconciliation (DVR)
One cost-effective way of increasing confidence in flow meter data accuracy is to use a technique known as data validation and reconciliation. This is a statistical method that may be used to evaluate the quality of flow measurement data from all types of industrial plants.
Data validation and reconciliation can be applied to 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, data reconciliation is particularly suited to software applications.
So, what do data validation and reconciliation tell us? First and foremost, it 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 – with obvious financial benefits. The data redundancy required by the method also gives the reconciled data greater accuracy and reliability than the unchecked data.
Data reconciliation is not magic – it does not introduce new data that is not there already. What it does is allow operators of plant 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 flow meters
- Perform network analysis and identify leakage on 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.
*UK: daily water usage per person 2020 | Statista
Carl Wordsworth, Head of Water Sector at TÜV SÜD National Engineering Laboratory, a world-class provider of technical consultancy, research, testing and program management services. Part of the TÜV SÜD Group, it is also a global centre of excellence for flow measurement and fluid flow systems and is the UK’s Designated Institute for Flow Measurement.