Forschung

Aktuelle Forschungsprojekte

  • FlOw4Heat - Entwicklung von Kompetenzen und Tools für die Transformation von Wärmenetzen zu intelligenten, flexiblen und nachhaltigen Energiesystemen.

Vergangene Publikationen

Nach Themengebieten:

  • Wasseraufbereitung/Blasensäulenbefeuchter: [1], [2], [3]
  • Gashydratbasierte CO\(_2\)-Abscheidung: [4]
  • Datenbasierte Modellierung: [5], [6], [7]
  • Energiesystemoptimierung: [8], [9], [10]

Literaturverzeichnis

1.
Eder E, Preißinger M (2020) Experimental analysis of the humidification of air in bubble columns for thermal water treatment systems. Experimental Thermal and Fluid Science 115:110063. https://doi.org/10.1016/j.expthermflusci.2020.110063
2.
Eder E, Hiller S, Brüggemann D, Preißinger M (2022) Characteristics of air–liquid heat and mass transfer in a bubble column humidifier. Applied Thermal Engineering 209:118240. https://doi.org/10.1016/j.applthermaleng.2022.118240
3.
Eder E, Cordin M, Pham T, Brüggemann D, Preißinger M (2023) An experimental study of oily wastewater treatment in a humidification–dehumidification system with bubble column humidifier. Thermal Science and Engineering Progress 37:101578. https://doi.org/10.1016/j.tsep.2022.101578
4.
Eder E, Kadinger M, Hiller S, Arzbacher S (2025) Hydrate-based carbon capture via pressure swing in a packed bed of ice. Separation and Purification Technology 361:131206. https://doi.org/10.1016/j.seppur.2024.131206
5.
Wohlgenannt P, Vetter V, Moosbrugger L, Kolhe M, Eder E, Kepplinger P (2026) Supervised Imitation Learning for Optimal Setpoint Trajectory Prediction in Energy Management Under Dynamic Electricity Pricing. Energies 19(6). https://doi.org/10.3390/en19061459
6.
Prokhorskii G, Preißinger M, Rudra S, Eder E (2025) An interpretable and adaptable data-driven model for performance prediction in thermal plants. Energy Conversion and Management: X 26:100950. https://doi.org/10.1016/j.ecmx.2025.100950
7.
Prokhorskii G, Rudra S, Preißinger M, Eder E (2024) A data-driven regression model for predicting thermal plant performance under load fluctuations. Carbon Neutrality 3(1):32. https://doi.org/10.1007/s43979-024-00108-5
8.
Wohlgenannt P, Moosbrugger L, Vetter V, et al (2025) Energy cost and emission optimization in food industry using deep reinforcement learning and Transformer-based load forecasting. Energy Conversion and Management: X 28:101364. https://doi.org/10.1016/j.ecmx.2025.101364
9.
Vetter V, Wohlgenannt P, Kepplinger P, Eder E (2025) Deep Reinforcement Learning Approaches the MILP Optimum of a Multi-Energy Optimization in Energy Communities. Energies 18(17). https://doi.org/10.3390/en18174489
10.
Wohlgenannt P, Hegenbart S, Eder E, Kolhe M, Kepplinger P (2024) Energy Demand Response in a Food-Processing Plant: A Deep Reinforcement Learning Approach. Energies 17(24). https://doi.org/10.3390/en17246430