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Next, plan for data and network security throughout the collection, transmission and analysis stages and include a monitoring programme for ongoing governance. Additionally, design data storage needs for a data analysis strategy. Build out monitoring capability, install new IoT devices and sensors and begin collecting data. Finally, consider working with experienced systems integrators and building technology partnerships to help incorporate smart city solutions.* CHALLENGES OF SMART WATER IMPLEMENTATION However, data interoperability, protection and privacy, high upfront costs and lack of required skills are among the most formidable factors in this case. Firstly, the data collected in sensors and IoT devices do not follow a standard format, leading to interoperability, trapping insights in data silos and rendering smart water projects ineffective. Get data architects involved in device selection to minimise data integration issues. Unfortunately, data breaches are becoming more common, and water management needs to address every point of the data journey. Hacking a water system can have devastating effects on the population. Furthermore, with hundreds of miles of pipes and intricate treatment facilities, it can be expensive to add more digital sensors and devices, install more hardware, build or deploy new software and give teams the skills necessary to make a smart infrastructure possible. These expenses can impact the adoption of smart water infrastructure, especially in developing countries, although strategic and phased deployments can generate the ‘quick wins’ necessary to justify longer-term investments. Smart water infrastructure also requires technical skills across many disciplines. Unfortunately, the global skills gap will make finding the needed skills challenging. Many organisations are engaging in upskilling and reskilling programs for their existing workforce to overcome
driven by live plant data. The solutions included creating a data model and a digital replica to emulate real-world processes, deploying end-to-end cloud infrastructure and creating user-friendly applications for real-time parameter adjustments. Enhanced by AI-driven analytics, these models provided actionable insights, guiding users to optimise operational efficiency and capital expenditures, prevent pollution and unlock massive savings.
this hurdle. This strategy keeps valuable industry knowledge in-house and allows organisations to determine the skills they need. SAND TECHNOLOGIES Sand Technologies has leveraged its data analytics and AI expertise to help water utilities maximise their insights from data-driven IoT devices and achieve their goals. Here are two such examples. One of the UK’s largest private water utilities wanted to consolidate multiple independent data sources used to determine the level of risk in their 31,000km water system network. The utility needed insights from the thousands of sensors across the network. Sand Technologies implemented the Hydraulic Network Risk Tool (HNRT) platform, using AI and IoT devices to monitor sensors across the network and present the data as a consolidated visualisation. The system has built-in, automated alerts to reduce risk, enabling proactive maintenance and reduced interruptions. The net effect is a consolidated, near-real-time view of the vast and complex water system, enabling operators to be better informed and have greater lead times for resolution. The utility is saving a minimum of 3.4ML/day in leakage, resulting in annual savings of more than £1.3m.* Secondly, a major UK water utility needed more precision and adaptability for modern wastewater treatment. Traditional methodologies and ad-hoc solutions lacked the precision and adaptability needed for modern wastewater treatment. Major water companies found themselves constantly battling pollution issues, with their operations needing to leverage the potential of contemporary technology. To address these challenges, the utility introduced cloud-based digital twins for sewage treatment plants (STPs).* These digital replicas — underpinned by pneumatic, hydraulic and process engineering models — empowered operators to digitally mirror their day-to-day operations, adjusting various plant factors in real time. The solutions also included data models
THE FUTURE OF SMART WATER This imbalance could lead to severe consequences, such as increased
water-related diseases, food shortages and social unrest. Smart water infrastructure can help address this imbalance by transforming the future of water systems to reduce water loss, improve water quality and predict and manage water scarcity. Many municipalities agree. The smart water management market was valued at $16.08bn in 2023, and may be worth $30.80bn by 2028.* AI, a component of smart water infrastructure, will be a potential game-changer in the fight against water scarcity. It can have a profound global impact, offering innovative solutions to this pressing issue. Proper water management helps secure public health, supports economic growth and protects the environment for everyone and future generations.
*References are available upon request
Matt Lewis Programme lead, Sand Technologies
Water & Wastewater Asia | September-October 2024 45
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