Theisinger, Lukas ; Borst, Fabian ; Kohne, Thomas ; Weigold, Matthias (2023)
Concept development for industrial heating networks under consideration of low temperature waste heat: a data-driven decision support.
In: Procedia CIRP, 116
doi: 10.1016/j.procir.2023.02.057
Article, Bibliographie
Abstract
Over the last decade, governments throughout the world set ambitious goals for decarbonisation to reduce the impacts of humanity on climate. Here, the industrial sector is of particular importance as it accounts for over one third of the global CO2 emissions. Even though being exceeded by higher temperature levels, low temperature heating accounts for up to 26 % of the total useful energy demand, depending on the specific industry sector. Those lower temperature levels tend to be beneficial for decarbonisation measures like utilization of industrial waste heat or electrification via heat pumps. In this work, we present a data-driven decision support which aims at supporting decision-makers in the concept phase of large-scale industrial heating networks in a bottom-up approach. For this purpose, we utilize heating and cooling demand data which are condensed into key metrics to recommend supply concepts (e.g., consumer, producer, prosumer) within the context of those systems. Concurrently, we ensure user-orientation through adaptability and transparency in the decision-making process. Throughout this paper, our approach is outlined using the example and operational data of a real industrial site.
| Item Type: | Article |
|---|---|
| Erschienen: | 2023 |
| Creators: | Theisinger, Lukas ; Borst, Fabian ; Kohne, Thomas ; Weigold, Matthias |
| Type of entry: | Bibliographie |
| Title: | Concept development for industrial heating networks under consideration of low temperature waste heat: a data-driven decision support |
| Language: | English |
| Date: | 18 April 2023 |
| Publisher: | Elsevier B.V. |
| Journal or Publication Title: | Procedia CIRP |
| Volume of the journal: | 116 |
| DOI: | 10.1016/j.procir.2023.02.057 |
| URL / URN: | https://www.sciencedirect.com/science/article/pii/S221282712... |
| Abstract: | Over the last decade, governments throughout the world set ambitious goals for decarbonisation to reduce the impacts of humanity on climate. Here, the industrial sector is of particular importance as it accounts for over one third of the global CO2 emissions. Even though being exceeded by higher temperature levels, low temperature heating accounts for up to 26 % of the total useful energy demand, depending on the specific industry sector. Those lower temperature levels tend to be beneficial for decarbonisation measures like utilization of industrial waste heat or electrification via heat pumps. In this work, we present a data-driven decision support which aims at supporting decision-makers in the concept phase of large-scale industrial heating networks in a bottom-up approach. For this purpose, we utilize heating and cooling demand data which are condensed into key metrics to recommend supply concepts (e.g., consumer, producer, prosumer) within the context of those systems. Concurrently, we ensure user-orientation through adaptability and transparency in the decision-making process. Throughout this paper, our approach is outlined using the example and operational data of a real industrial site. |
| Uncontrolled Keywords: | key performance indicator, low temperature heating, supply concept, thermal integration |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > ETA Energy Technologies and Applications in Production |
| Date Deposited: | 21 Apr 2023 05:44 |
| Last Modified: | 15 May 2023 08:11 |
| PPN: | 507761758 |
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