The University of Bradford and Switch 2 Energy Limited KTP 24_25 R3
In plain English
AI plain-English summaryA heat network operator will soon be able to test changes to their system on a perfect digital copy before touching a single pipe or boiler in the real world. Heat networks—centralised systems that pump hot water through underground pipes to heat multiple buildings—are notoriously inefficient. Operators often run them too hot to avoid complaints, wasting energy and driving up carbon emissions. Tuning a real network is risky: a wrong adjustment can leave residents cold for days. This project builds a Digital Twin, a live computer model that mirrors the behaviour of any domestic heat network in real time. Operators can simulate adjustments—changing flow temperatures, tweaking pump speeds, or adding new buildings—and see the effects instantly without disrupting anyone’s heating. If successful, this tool could let network managers optimise performance continuously, cutting wasted heat and reducing the fossil fuel burned to generate it. That matters because heat networks are a growing part of the UK’s energy infrastructure, and their inefficiency currently holds back the transition to net zero. A reliable, simulation-driven approach could make these systems cheaper to run, more comfortable for residents, and far less carbon-intensive—without requiring expensive hardware upgrades.
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