Together with Bentley Systems, Silo AI developed a smart, data-driven asset optimization solution for city pipeline operators. Known as Silo Flow, the service predicts pipeline leakages and identifies the potential cooling upside of district heating systems.
- To combine a digital twin with AI, creating a prediction model for proactive network maintenance.
- To ensure efficient, reliable, and sustainable network water and district heating services throughout Finland.
Optimizing aging citypipeline networks
Finland maintains over 16,000 kilometers of district heating pipelines and, in the Helsinki metropolitan area alone, approximately 3,000 kilometers of water and sewerage lines. 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 wasted water, which is not only detrimental from a business perspective but also environmentally. To improve the 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.
Silo AI set out to pilot the digital pipeline optimization solution in cooperation with the 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 the 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. “Silo AI’s service will increase energy efficiency, performance, and productivity of [our] customers’ network systems by double digit percentages,” said Harri Kaukovalta, Business Development Executive at Silo AI.
Dispersed Data Sources and Manual Workflows
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 created a proactive solution to optimize pipeline operations, using AI and data analytics to pinpoint areas prone to leakage and prioritize pipeline maintenance renovations. The digital twin model was developed to identify and predict network failures before leakages occur. Pevious workflows required a combination of numerous data sources in various data formats, resulting in partial and inaccurate representations of the network “Today, scattered data sources make it difficult to have a holistic overview of the pipeline network health, and manually driven investment planning takes only a very limited amount of data sources into account,” said Kaukovalta.
The multi-sourced, disparate data and manual workflows prevented operators from accurately identifying potential risks and proactively addressing them before they become costly, environmentally damaging problems, leading to shutdowns and interrupted service. Operators had to aggregate the data and visualize their network in its entirety to predict and prioritize pipeline maintenance. “This is where Silo Flow can provide an answer where you can optimize your network assets by investing in the right locations at the right time while guaranteeing a high level of services for your customers,” said Kaukovalta. To execute the solution, Silo AI collaborated with Bentley Systems to build a user-friendly, web-based interface. Silo AI sought to integrate the multiple pipeline data sources and perform advanced data analysis in a digital platform, providing operators with a visual, comprehensive overview of asset health and systematically identifying and addressing leakages before they occur.
Developing a Digital Prediction Model
To predict pipeline maintenance needs and optimize network management, Silo AI developed its smart Silo Flow prediction model based on Bentley’s 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. “Bentley’s iTwin framework offered a simple and straightforward way to visualize data and data analysis results,” said Kaukovalta. 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. They can optimize their network to timely invest in the right locations, ensuring safe and reliable service while promoting energy efficiency and carbon neutrality.
As the foundation for Silo Flow, Silo AI used the iTwin Platform to integrate the multi-sourced data into a living digital twin and aligned it with real 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 a 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.
Smart Solutions Drive Savings and Sustainability
Bentley’s iTwin Platform is easy to use, enabling data integration and visualization of the new data analysis capabilities implemented by Silo AI, simply and cost-efficiently. The flexibility and interoperability of Bentley’s application shortened the project time, and any needed additions were easy to add to the digital platform. Using iTwin reduced visualization efforts by 50% and significantly reduced delivery time for the digital AI leakage prediction and flow optimization solution.
Having easy accessibility to the data and visually analyzed results, network operators can improve pipeline operations. The iTwin-based smart solution offers a holistic view of pipeline network health, where maintenance needs and potential risk areas are clearly identified and visualized. It predicts and prioritizes pipeline maintenance to ensure customer satisfaction and sustainability, avoiding unwanted shutdowns of the grid, environmental damage, and waste from leakages. The successful project decreased the client’s district heating network supply temperature by 5 degrees, improving energy efficiency while decreasing fuel consumption. “Silo Flow adds predictability and restores control over your system. It also helps you to optimize energy production,” said Kaukovalta.
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