Abstract
This paper presents Symbiosis, a web-based decision support tool developed to improve energy symbiosis within eco-industrial parks (EIPs). Symbiosis addresses the limitations of traditional desktop applications by offering a user-friendly, web-based interface that simplifies the modeling and visualization of energy flows among facilities. This tool is designed to be accessible to users with varying technical backgrounds. The effectiveness of Symbiosis was evaluated through a case study involving an industrial park with wind and photovoltaic energy sources and a greenhouse and brewery as energy consumers. The study assessed how replacing an existing greenhouse with a higher energy-consuming variant impacted the park’s energy balance. Symbiosis successfully modeled energy flows and visualized changes in energy surplus and deficit resulting from this replacement. The tool demonstrated its capability to provide insights into energy dynamics and optimize facility configurations to improve energy efficiency. The case study highlighted that Symbiosis is effective in visualizing and analyzing energy flows, supporting better decision-making for EIP management. However, it also revealed some limitations, such as performance issues with large datasets and the need for support for additional energy types. Future research should explore case studies in various EIP configurations and consider integrating optimization algorithms to enhance decision-making.
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Acknowledgements
This paper is part of the IEA IETS Annex Task XXI: Decarbonizing industrial systems in a circular economy framework, funded by the Danish funding agency, the Danish Energy Technology Development and Demonstration (EUPD) program, Denmark (Case no. 134233-511205); part of the project “GreenLab designer Lite” collaborated with GreenLab Skive, funded by the Villum foundation.
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Gärdström, H.F., Schwarz, H., Jørgensen, B.N., Ma, Z.G. (2025). Symbiosis: A Web-Based Decision Support Tool for Achieving Symbiosis in Industrial Parks. In: Jørgensen, B.N., Ma, Z.G., Wijaya, F.D., Irnawan, R., Sarjiya, S. (eds) Energy Informatics. EI.A 2024. Lecture Notes in Computer Science, vol 15271. Springer, Cham. https://doi.org/10.1007/978-3-031-74738-0_2
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