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Utilizing Wastewater Tunnels as Thermal Reservoirs for Heat Pumps in Smart Cities

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  • Fredrik Skaug Fadnes

    (Department of Energy and Petroleum, University of Stavanger, 4021 Stavanger, Norway
    Department of Energy and Smart Technology, Norconsult AS, 1338 Sandvika, Norway)

  • Mohsen Assadi

    (Department of Energy and Petroleum, University of Stavanger, 4021 Stavanger, Norway)

Abstract

The performance of heat pump systems for heating and cooling heavily relies on the thermal conditions of their reservoirs. This study introduces a novel thermal reservoir, detailing a 2017 project where the Municipality of Stavanger installed a heat exchanger system on the wall of a main wastewater tunnel beneath the city center. It provides a comprehensive account of the system’s design, installation, and performance, and presents an Artificial Neural Network (ANN) model that predicts heat pump capacity, electricity consumption, and outlet temperature across seasonal variations in wastewater temperatures. By integrating domain knowledge with the ANN, this study demonstrates the model’s capability to detect anomalies in heat pump operations effectively. The network also confirms the consistent performance of the heat exchangers from 2020 to 2024, indicating minimal fouling impacts. This study establishes wastewater heat exchangers as a safe, effective, and virtually maintenance-free solution for heat extraction and rejection.

Suggested Citation

  • Fredrik Skaug Fadnes & Mohsen Assadi, 2024. "Utilizing Wastewater Tunnels as Thermal Reservoirs for Heat Pumps in Smart Cities," Energies, MDPI, vol. 17(19), pages 1-35, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4832-:d:1486702
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    References listed on IDEAS

    as
    1. Lund, Rasmus & Persson, Urban, 2016. "Mapping of potential heat sources for heat pumps for district heating in Denmark," Energy, Elsevier, vol. 110(C), pages 129-138.
    2. Jan Rosenow & Duncan Gibb & Thomas Nowak & Richard Lowes, 2022. "Heating up the global heat pump market," Nature Energy, Nature, vol. 7(10), pages 901-904, October.
    3. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    4. Daniele Cecconet & Jakub Raček & Arianna Callegari & Petr Hlavínek, 2019. "Energy Recovery from Wastewater: A Study on Heating and Cooling of a Multipurpose Building with Sewage-Reclaimed Heat Energy," Sustainability, MDPI, vol. 12(1), pages 1-11, December.
    5. Aguilera, José Joaquín & Meesenburg, Wiebke & Markussen, Wiebke Brix & Zühlsdorf, Benjamin & Elmegaard, Brian, 2024. "Real-time monitoring and optimization of a large-scale heat pump prone to fouling - towards a digital twin framework," Applied Energy, Elsevier, vol. 365(C).
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    Cited by:

    1. Tong Ren & Mengzhuo Li & Long He & De Wang & Lingbo Kong, 2025. "A Theoretical and Test Analysis of Heat and Humidity Transfer for Deeply Buried Underground Corridors with Different Shapes," Energies, MDPI, vol. 18(2), pages 1-24, January.
    2. Sabina Kordana-Obuch & Beata Piotrowska & Mariusz Starzec, 2025. "Gaining CO 2 Reduction Insights with SHAP: Analyzing a Shower Heat Exchanger with Artificial Neural Networks," Energies, MDPI, vol. 18(8), pages 1-23, April.

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