TwinOptPRO—Digital Platform for Online Pump Scheduling Optimization
Climate change is leading to a general shortage of raw water availability combined with more pronounced seasonality and dry phases. The goal of the collaborative research project TwinOptPRO is to contribute to EU-wide climate neutrality in 2050 by the minimization of energy supply for water treatmen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/69/1/94 |
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| author | Thomas Bernard Jochen W. Deuerlein Martin Dresen Michael Fischer Nicolai Guth Rüdiger Höche Christian Kühnert Christa Mastaller Gerhard Rappold Gordon Schlolaut Andreas Wunsch Mathias Ziebarth |
| author_facet | Thomas Bernard Jochen W. Deuerlein Martin Dresen Michael Fischer Nicolai Guth Rüdiger Höche Christian Kühnert Christa Mastaller Gerhard Rappold Gordon Schlolaut Andreas Wunsch Mathias Ziebarth |
| author_sort | Thomas Bernard |
| collection | DOAJ |
| description | Climate change is leading to a general shortage of raw water availability combined with more pronounced seasonality and dry phases. The goal of the collaborative research project TwinOptPRO is to contribute to EU-wide climate neutrality in 2050 by the minimization of energy supply for water treatment and pumps in drinking water distribution systems. For that purpose, a digital platform that combines different forecasting models with simulation and optimization modules was developed. The aim is to ensure secure and compliant supply to customers in the future while maximizing the use of renewable energy and minimizing costs. |
| format | Article |
| id | doaj-art-392fd45d0712401fb61c451eb2b7ea33 |
| institution | DOAJ |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-392fd45d0712401fb61c451eb2b7ea332025-08-20T02:42:38ZengMDPI AGEngineering Proceedings2673-45912024-09-016919410.3390/engproc2024069094TwinOptPRO—Digital Platform for Online Pump Scheduling OptimizationThomas Bernard0Jochen W. Deuerlein1Martin Dresen2Michael Fischer3Nicolai Guth4Rüdiger Höche5Christian Kühnert6Christa Mastaller7Gerhard Rappold8Gordon Schlolaut9Andreas Wunsch10Mathias Ziebarth11Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, Germany3S Consult GmbH, 30827 Garbsen, GermanygeoSYS, 12047 Berlin, Germany3S Consult GmbH, 30827 Garbsen, Germany3S Consult GmbH, 30827 Garbsen, GermanyStadtwerke Bühl GmbH, 77815 Bühl, GermanyFraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, Germany3S Consult GmbH, 30827 Garbsen, GermanygeoSYS, 12047 Berlin, GermanygeoSYS, 12047 Berlin, GermanyFraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, GermanyFraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, GermanyClimate change is leading to a general shortage of raw water availability combined with more pronounced seasonality and dry phases. The goal of the collaborative research project TwinOptPRO is to contribute to EU-wide climate neutrality in 2050 by the minimization of energy supply for water treatment and pumps in drinking water distribution systems. For that purpose, a digital platform that combines different forecasting models with simulation and optimization modules was developed. The aim is to ensure secure and compliant supply to customers in the future while maximizing the use of renewable energy and minimizing costs.https://www.mdpi.com/2673-4591/69/1/94pump schedulingbi-level optimizationhydrologydemand forecastneural networks |
| spellingShingle | Thomas Bernard Jochen W. Deuerlein Martin Dresen Michael Fischer Nicolai Guth Rüdiger Höche Christian Kühnert Christa Mastaller Gerhard Rappold Gordon Schlolaut Andreas Wunsch Mathias Ziebarth TwinOptPRO—Digital Platform for Online Pump Scheduling Optimization Engineering Proceedings pump scheduling bi-level optimization hydrology demand forecast neural networks |
| title | TwinOptPRO—Digital Platform for Online Pump Scheduling Optimization |
| title_full | TwinOptPRO—Digital Platform for Online Pump Scheduling Optimization |
| title_fullStr | TwinOptPRO—Digital Platform for Online Pump Scheduling Optimization |
| title_full_unstemmed | TwinOptPRO—Digital Platform for Online Pump Scheduling Optimization |
| title_short | TwinOptPRO—Digital Platform for Online Pump Scheduling Optimization |
| title_sort | twinoptpro digital platform for online pump scheduling optimization |
| topic | pump scheduling bi-level optimization hydrology demand forecast neural networks |
| url | https://www.mdpi.com/2673-4591/69/1/94 |
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