How UK water companies can fully realize the potential of digital plants
Blog: Capgemini CTO Blog
The water industry today
The UK water industry faces the greatest period of disruption – and opportunity – since privatization. Within the next five to ten years, significant changes are set to hit the industry in multiple areas, each bringing both challenge and opportunity.
The water sector is facing a number of significant challenges, for example climate change, population growth, the need for increasing resilience, and ever-rising customer expectations. At the same time, there is a need to maintain an affordable service and public trust. All of this can be impacted by available digital technology and tools.
The digitization of water
New digital technologies are enabling water utilities to extract greater information and efficiencies from legacy water infrastructure to enhance decision making, promote water conservation, build a twenty-first century water infrastructure, and – perhaps most importantly – increase the value and benefits of the water infrastructure network.
The industrial internet is now leading the digitization of the water business. The lessons learned from the digitization of other sectors, such as heavy industry, manufacturing, power, media, retail, communications, healthcare, and education are now being successfully applied to water infrastructure and commercial projects.
“Digital plants” concepts in water
Digital utility plants leverage a large number of digital applications across the utility production value chain. A digital application is a combination of multiple technologies addressing a specific business requirement. For example, predictive maintenance, which is a combination of technologies such as IIoT, big data, and artificial intelligence, is a popular digital application to improve productivity and reliability by minimizing unplanned downtime.
Applications of digital plant in water industry
Proactive and predictive understanding of the behavior of the network infrastructure (water and wastewater) has been a long-sought goal within the industry. Despite rapid technological advances in sensors and acoustics, the industry is still reliant upon customer generated information to be the first signals of network failure.
OT/IT convergence – At its heart, the issue of network control and intelligent operational understanding is making up for the previous lack of connection between OT and IT; where OT has typically been owned by the operational part of the business, and the IT department has operated and maintained the existing IT estate. By integrating these traditionally separate technologies and changing the interaction between the operational and IT functions regarding network monitoring, the opportunities are significant. A more intelligent network, with more connected sensors and devices, gives a richer level of data, which in turn supports richer analytics and greater insight.
Smart analytics and smart leakage management
Twenty to thirty percent of UK water production is lost to leakage. Leakage detection still relies on people and listening technology; nothing much has changed for 50 years.
Based on principles of predictive maintenance, machine learning, and digital communication – by scheduling maintenance based on predictive analytics, instead of just the age of the pipes, water companies can prevent more leaks while replacing fewer pipes. The next-generation machine learning tooling and algorithms drive insight-led interventions targeted at driving value. Further data visualization and conceptual aggregation layers provide actionable, intuitive operational intelligence.
Digitally connected engineer
We see the development over the next 5–10 years of a truly connected workforce that will have real-time access to knowledge and information about critical assets. Workforce scheduling will be more dynamic and intelligent, taking feeds from analytics engines and predictive asset-behavior models to get engineers to the issues ahead of failure or customer impact. We envisage a world where an engineer will be equipped with technology that provides a highly connected experience that can utilize live data feeds into wearable or mobile technology, use augmented reality to render 3D models of assets for maintenance, and then be instructed in how to carry out the work by a remote engineer seeing the work site through the use of streamed video.
Predictive asset maintenance
There will continue to be rapid innovation in the analytics platforms, which will increasingly be combined with machine learning capabilities and operated in the cloud to deal with the volumes of data.
Increasingly, the analytics will be combined with widespread use of advanced visualization capability, including augmented reality and virtual reality. There will also be much better and more widespread use of traditional 3D rendering, used both in design and also in operations. This, in turn, gives easy, controllable access to standardized and reusable data across the entire operational asset base, combining standard asset configuration data, real-time situational data (asset- and customer-generated), historic performance data, and environmental data. The visualization of this data in real time, using the capabilities described above, will enable far better understand of operations and asset performance.
How to approach
- Go for quick wins by selecting low-investment and high-value technologies. The use of drones in critical asset inspection operations across a network, for example, results in immediate gains at limited cost-avoiding cost and effort in interruptions to water supply. A drone generates significant savings on downtime and on overall maintenance operations. Another example is to invest in running proofs of value in areas such as leakage management, energy management leveraging predictive analytics and significantly reducing TOTEX.
- Launch projects with high-value applications such as predictive maintenance, intervention management, or digital asset lifecycle management for assets to ensure recurring gains. Analytics, big data, and advanced modelling are used to detect leaks or losses as well as for predictive maintenance.
- For faster gains, invest in applications that allow for more targeted operational efficiencies, such as smart autonomous site operations, investment planning, asset monitoring, sludge/waste monitoring.
- Finally, rely on emerging applications and technologies, like additive manufacturing such increased use of 3D printing in engineering/construction lifecycle models in capital delivery are being used to drive standardization of assets and asset data throughout design-build-commission-operate lifecycle of water and wastewater plants.
Looking forward to the next 25 years and more, the water sector faces challenges from population growth, climate change, tightening environmental standards and changing customer expectations.
These challenges will require both significant further investment and the adoption of digital technologies. This will ensure that the water sector can continue to deliver the outcomes customers and society want, efficiently and effectively.
A Digital plant leverages many digital applications across the Water and Waste Water treatment and production value chain. Disruptive technologies will enable water sector to extract greater information and efficiencies, build a twenty-first century water infrastructure, and perhaps most importantly increase the value and benefits of the water infrastructure network.
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