The integration of digital technologies into water management systems represents a step-change and an exciting opportunity for how we approach our planet’s most vital resource. As we collectively deal with aging infrastructure, increasing urbanization, climate change, and the need for social equity, technology continues to emerge as a key aid.
The quantity of information now available is unprecedented. It is estimated that 35% of companies reported using AI in their business, and 42% reported they are exploring AI. (1) Connected devices and digital services are transforming many aspects of our world. Specifically, in the water sector, this has been driven by advances in sensor technologies and IoT. These systems continuously collect vast quantities of high-resolution data on water quality, usage, and ecological conditions, offering an unprecedented level of insight into environmental health and resource management.
This data, when processed and analyzed, can optimize water distribution, reduce waste, and pre-emptively identify system vulnerabilities. This provides a whole new framework for decision-making at the city or watershed level and enables a much more integrated, adaptive approach to the management of the environment.
“A key paradigm in this transformational shift is the digital twin: a dynamic, digital replica of a physical asset or system that can mirror its real-world behavior,” said Bond Harper, Arup, Senior Consultant. “A digital twin integrates real-time data with model simulations, which can be physics-based or AI/ML driven, to represent current and forecast future conditions of the real-world asset or system.”
Harper goes on to say that a digital twin can then store this information and provide a platform for different stakeholders to view and interact with the data and test interventions in a secure manner.
“A digital twin can help operational decision-making by integrating real-time data, allowing users to analyze and manage relevant information,” she said.
It should be noted, however, that while there is broad agreement about the definition of what makes a digital twin, there is a lack of consensus about what components exactly constitute a digital twin. Some people consider the calibration of a hydraulic model a digital twin, while others focus on the need for a component of real-time interaction to be included. Whatever the distinction, it is clear that the digitization of infrastructure systems is providing water utilities and cities with a wide range of new capabilities.
Digital twins are a great way to bring new pre-emptive management and operation to any building or asset, and the technology is also a great fit for water infrastructure and wastewater systems.(2)
These groups can now dynamically optimize existing infrastructure, saving operating expenses through a reduction in energy costs or chemicals used and capital expenditures by increasing the capacity of existing infrastructure or moving towards a predictive maintenance program, thereby extending asset life.
“For instance, Northern Ireland Water developed a machine learning-based digital twin of their rapid gravity filters to optimize their operation, saving carbon, energy, and money,” Harper said. “Rapid gravity filtration is a key process in the production of safe, clean drinking water worldwide, removing particles and pathogens through physical straining, sedimentation, and electrostatic attraction.”
The performance of the filters changes over time, and must be cleaned regularly, which is an energy-intensive process traditionally carried out on a fixed schedule. The performance of these filters determines the cleanliness and safety of the drinking water.
Harper explained that Northern Ireland Water also developed a M/L model that can analyze historical and live data to predict the performance of rapid gravity filters. The model uses data from observed changes in the quality parameters of the water passing through the treatment works to make predictions about the future performance of the filters, helping operators optimize their cleaning schedules and reduce interruptions to operation.
“Digitization can also play a key part in climate resilience projects, developing sophisticated modeling to simulate various climate scenarios and informing adaptive strategies for flood defense and water scarcity,” she said. “Cities like New York, London, and Shanghai have all adopted new blue-green water management strategies. This is because natural infrastructure is coming to the forefront of urban stormwater design, as reliance on physical barriers to keep flooding out is no longer the best approach.”
The World Economic Forum published that nature-based solutions are 50% more cost-effective than manmade alternatives while also delivering 28% more value. (3)
Cities are struggling with how to deliver these plans at a large scale, but digital technologies are proving to be the key. Satellite data can be used to map land use in seconds, allowing engineers and planners to develop citywide masterplans. Real-time, citywide hydraulic models are allowing planners to validate designs and turn them into fundable schemes that can compete with grey infrastructure.
As shown, digital solutions and data can achieve impressive results both technically and socially in a range of settings, from wastewater to climate resilience to urban development. Incorporating AI into water systems only further elevates these capabilities, allowing for predictive analytics that can forecast usage trends, potential system failures, and environmental changes. This includes artificial neural networks for making predictions, genetic algorithms for optimizing decision-making, and image recognition algorithms for achieving automation. However, the massive benefits of these solutions and tools cannot overshadow the measures that must be taken to maintain transparency and security.
“The advent of data-centric AI and foundational models, such as those which underpin Open-AI’s Chat-GPT, have further highlighted not only the value of data but also the need to de-silo data to allow it to be used to its full potential,” Harper said. “Transitioning to digitally enabled water management systems involves navigating complex issues related to data ownership, privacy, and ethics.”
Harper believes to combat this, we need transparent data governance models that respect individual privacy while leveraging collective insights for the greater good.
“We need AI and technology that are not used as ‘black boxes,’ but developed to be explainable and augment human intelligence rather than supplant it,” she said.
She explained while technical solutions are important, “we must also engage in workforce development, preparing the next generation of water professionals to understand, navigate, and leverage these digital tools effectively.”
AI integration and the adoption of digital technologies also demand a nuanced approach to cybersecurity. There is a need to prioritize robust cybersecurity frameworks to protect these increasingly interconnected systems against potential breaches, ensuring the continuity and reliability of water services.
“The journey of digital transformation in the water sector is an ongoing narrative of innovation, resilience, and sustainability,” she said. “The ultimate goal is clear— we seek to secure a healthy future of water for all by harnessing the power of digital technology.”
REFERENCES:
1 IBM Global AI Adoption Index: ibm.co/42T8jmx
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