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Background and Client: Finland maintains over 16,000 kilometers of district heating pipelines and approximately 3,000 kilometers of water and sewerage lines in Helsinki, and it was in need of an Intelligent Digital Optimization Service. A significant portion of these networks are aging and expiring, resulting in water leaks, higher costs, and unreliable service for pipeline network customers. The inefficient network performance and leakages increase fuel consumption and water wastage, causing business losses and environmental damage. To improve performance, reliability, and energy efficiency of Finland’s water and district heating networks, Silo AI initiated a project to develop a smart, data-driven asset optimization service for city pipeline operators.
As the largest private artificial intelligence (AI) lab in the Nordics, Silo AI develops customizable, AI-driven solutions and products to enable intelligent monitoring and forecasting in city infrastructure, energy, and logistics. The company set out to pilot their digital pipeline optimization solution in cooperation with Helsinki Environmental Services Authority HSY and Suur-Savon Sähkö Oy, one of the largest grid operators in Finland. The goal was to enable these system operators to offer more sustainable energy services, optimizing performance of district heating assets and eliminating pipeline leakages. Known as Silo Flow, the system optimization service will help predict network failures and prioritize proactive asset maintenance to avoid costly repairs and potential network shutdowns, ensuring efficient and reliable service while minimizing environmental impact.
The Challenges: System operators have been trying to localize leakages by different methods. However, these methods take place only after the leaks have occurred, requiring extensive repair and often resulting in service interruption. Silo AI sought a proactive solution to optimize pipeline operations, using AI and data analytics to pinpoint areas prone to leakage and prioritize pipeline maintenance renovations. They wanted to develop a digital twin model to identify and predict network failures before leakages occur. However, previous workflows required a combination of numerous data sources in various data formats, resulting in partial and inaccurate representations of the network. To execute their solution, Silo AI needed a user-friendly, web-based interface. They sought to integrate the multiple pipeline data sources and perform advanced data analysis in a digital platform, providing operators a visual, comprehensive overview of asset health, systematically identifying and addressing leakages before they occur.
The Solution: To predict pipeline maintenance needs and optimize network management, Silo AI developed its smart Silo Flow prediction model based on the Bentley iTwin Platform. The solution combines Silo AI’s advanced data analytics with Bentley’s digital, cloud-based interface for easy, accessible visualization of the pipeline data and assets. Combining advanced data science with cutting-edge visualizations, district heating and water network operators can pinpoint assets in need of maintenance prior to leakages or asset failure. Silo AI used the Bentley iTwin Platform to integrate the multisourced data into a living digital twin and aligned it with reality data, sensors, and AI without any additional equipment needed by the network operators. The combined solution can consolidate and analyze data into an understandable, valuable format facilitating data-driven decisions.
Working within a fully accessible visual interface, operators can achieve comprehensive insight into asset health, and analyze and predict where better cooling can bring savings and more efficient productivity, optimizing heat balance in district heating as well as water flows throughout the network.
Quantifiable Benefits: The Bentley iTwin Platform was easy for Silo AI to use. It allowed them to integrate data and visualize the new data analysis capabilities that they implemented. The flexibility and interoperability of Bentley’s application shortened the project time, and any needed additions were easy to make to the digital platform. Using iTwin reduced visualization efforts by 50 percent and significantly reduced delivery time for the digital AI leakage prediction and flow optimization solution.
With one degree cooling improvement providing a 1 percent to 1.5 percent increase in the network energy performance, system operators can deliver more sustainable service and increase ROI through pipeline system optimization, preventing leakages, eliminating lost resources, and budgeting future maintenance and network investments. By combining progressive data science with advanced visualizations in a digital twin platform, Silo AI is simplifying decision-making for network operators, enabling straightforward conclusions to support reliable heating and water access crucial to quality of life.
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