The Cunliffe Report into the UK’s water industry has placed asset health at the heart of creating a modern, resilient water infrastructure that can “restore the trust that has been lost” in the sector. A data-driven approach is the only way to deliver on the daunting task that has been set.
David Bean is business development group manager at Mitsubishi Electric – Automation Systems UK
The Cunliffe Report does not mince its words when it comes to the infrastructure that sits at the heart of the nation’s water supplies. “We need far greater clarity on the health of these crucial assets and the resilience of the system as the infrastructure ages and the pressures upon it increase,” Sir John writes in his foreword.
One of the key problems, he concludes, is that “Ofwat measures asset health through a series of failure metrics rather than explicit assessment of the condition of assets”.
Herein lies the root of the problem. Our water infrastructure, with major assets comprising of 10,000 pumping stations, nearly 1,500 water treatment works and over 2,000 large, raised reservoirs is vast, geographically spread out and ageing. The easiest way of measuring the condition of this vast asset base is, unfortunately, failure rate.
Sir John and his Commission accept the scale of the task, recognising “the work already being undertaken” by the water companies and their engineers, before adding: “This work should be accelerated.” It’s a big ask.
Thankfully, the Commission also identifies a route forward namely that “holding accurate and robust data on the assets water companies are responsible for […] is a pre-requisite of a resilient system”. I couldn’t agree more, and in fact, I don’t see another route forward other than a data-driven approach that takes the industry beyond failure-rate metrics.
By data-driven, I mean having a real understanding of the science behind decision-making that ultimately will make an asset perform optimally, understanding its health, when it is likely to start to degrade and then making informed maintenance decisions around it.
Essentially, what I am describing is a journey from reactive to preventative to predictive maintenance, and then onto prescriptive.
Water treatment and unmanned pumping stations provide a textbook example of where data can be used to drive productivity and efficiency. A modular, smart condition monitoring solution can be quickly deployed and offers a clear return on investment. It does so by moving away from reactive and preventative maintenance regimes, or the need for a technician on site, towards the development of systems with early warning of asset failure, via remote monitoring.
With predictive maintenance, software such as Mitsubishi Electric’s Edge platform, which can be deployed onsite with offsite analysis, there is also the potential to develop a dual capability of both data management and Edge-based analytics.
In operation, this type of software can create a diagnostic rule that would include anything affecting the health and performance of the asset. For example, a rule for gear wear could be dependent upon on a number of factors, including temperature, humidity and the materials the gears are made from.
Once the rules are defined, the Edge software uses a real-time diagnostic mode to monitor the asset and a closed loop control to apply the rule directly to the asset when required.
A good operational example would be the use of rules in an aeration plant. The software monitors the health of the equipment and the variables that impact performance, such as pH and dissolved oxygen levels. A digital twin of the plant can then be developed to compare the performance of the plant against the model in order to ensure optimal, real-time performance.
For many water companies the issue is not collecting data but organising it into a single view.
The use of a situational awareness platform that unites telemetry, customer data, engineering data, weather data, fleet and workflow management, into a single, integrated system, viewed through a single pane of glass, is essential for operations to respond quickly to events.
Situational awareness platforms can be deployed across a water utility’s entire water cycle, including catchment, clean water treatment, consumption and wastewater treatment, to collect data from disparate sources, enabling operations to make meaningful decisions about asset performance.
The platform enables automated decision making which, in turn, can reduce response times, operating costs and improve regulatory performance.
One major UK water company with 45 treatment plants had 19 different systems that needed to be integrated. These include a combination of SCADA, telemetry, advanced business analytics, alarm management, KPI-driven dashboards, GIS reporting and integration with business data, such as customer calls.
The implementation of a situational awareness platform allowed the company to respond quicker and proactively plan for scenarios where assets and customers were affected. The software ultimately delivered a saving of £3.6M whilst protecting the company’s Ofwat overall performance assessment score.
The water industry is behind the curve on maintenance, as the Cunliffe Report points out. The industry is not alone in this regard – the food and beverage sector is similarly challenged – but having a lot of catching up to do is never a comfortable place to be.
The task is daunting, but the Cunliffe Report has identified a route forward that lies in the better use of data, both existing and new. The technology exists and with AMP8 the funding is in place – we now just have to seize the opportunity.
- David Bean is business development group manager at Mitsubishi Electric – Automation Systems UK
For more information on Mitsubishi Electric’s Edge platform, please visit this site.
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