Publikationen
2022
Breunig, P.
Der Wirtschaftlichkeit auf der Spur Artikel
In: Ausg. 7/2022, 2022.
BibTeX | Schlagwörter: Direct Marketing, Direktvermarktung, Energy Flexibility, model based optimization, Sustainability, Systemdienstleistungen
@article{nokey,
title = {Der Wirtschaftlichkeit auf der Spur},
author = {P. Breunig },
editor = {DLG-Mitteilungen},
year = {2022},
date = {2022-07-01},
urldate = {2022-07-01},
issue = {7/2022},
keywords = {Direct Marketing, Direktvermarktung, Energy Flexibility, model based optimization, Sustainability, Systemdienstleistungen},
pubstate = {published},
tppubtype = {article}
}
Hümmer, C.
Quantification, Benchmarking and Stewardship of Veterinary Antimicrobial Usage Proceedings Article
In: Third International Conference ; 5-6 May 2022 Hannover, Germany & Online (Hrsg.): Third International Conference ; 5-6 May 2022 Hannover, Germany & Online, S. 24, 2022.
BibTeX | Schlagwörter: habitat characteristics, model based optimization
@inproceedings{nokey,
title = {Quantification, Benchmarking and Stewardship of Veterinary Antimicrobial Usage},
author = {C. H\"{u}mmer },
editor = {Third International Conference ; 5-6 May 2022 Hannover, Germany \& Online
},
year = {2022},
date = {2022-05-06},
urldate = {2022-05-06},
booktitle = {Third International Conference ; 5-6 May 2022 Hannover, Germany \& Online},
journal = {Abstracts Book},
issue = {1},
pages = {24},
keywords = {habitat characteristics, model based optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
Dettelbacher, J.; Wagner, D.; Buchele, A.; Schlüter, W.
Kopplung einer Material- und Energieflusssimulation mit Reinforcement-Learning-Algorithmen Artikel
In: Ausg. 19, 2022, (ASIM Mitteilung 179, S. 21-24.).
BibTeX | Schlagwörter: model based optimization, Simulation
@article{nokey,
title = {Kopplung einer Material- und Energieflusssimulation mit Reinforcement-Learning-Algorithmen},
author = {J. Dettelbacher and D. Wagner and A. Buchele and W. Schl\"{u}ter },
editor = {Proceedings Kurzbeitr\"{a}ge ASIM SST 2022, ARGESIM Report},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
issue = {19},
note = {ASIM Mitteilung 179, S. 21-24.},
keywords = {model based optimization, Simulation},
pubstate = {published},
tppubtype = {article}
}
2021
Ratka, A.; Hofmann, J.; Ernst, W.
Model Based Optimization of an Insulating Panel Made from Biogenic Residues Artikel
In: Construction and Building Materials, Bd. Volume 318, S. 125807, 2021, ISSN: 0950-0618.
Abstract | BibTeX | Schlagwörter: Biogenic Residues, Energy Flexibility, Insulating panels, model based optimization, Sustainability, Thermal conductivity | Links:
@article{nokey,
title = {Model Based Optimization of an Insulating Panel Made from Biogenic Residues},
author = {A. Ratka and J. Hofmann and W. Ernst
},
editor = {Elsevier},
doi = {https://doi.org/10.1016/j.conbuildmat.2021.125807},
issn = {0950-0618},
year = {2021},
date = {2021-12-01},
urldate = {2021-12-01},
journal = {Construction and Building Materials},
volume = {Volume 318},
pages = {125807},
abstract = {Within the scope of this article is the development of a mathematical model of an insulating panel made from grain husks and the optimization of the panel regarding the thermal characteristics by using this self-developed thermodynamically model. The model comprises one husk as the smallest unit of the insulating panel. In order to solve the model analytically, the following relevant parameters have been determined:
− husk geometry
− density of the pure husk material
− thermal conductivity of the pure husk material
− emission coefficient of the husk's surface
− convection coefficient inside the husks
− density of the binder
− thickness of the binder
− thermal conductivity of the binder
The solved model is validated with produced insulating panels out of grain husks. The optimization of the effective thermal conductivity λeff is done regarding the following parameters:
− the filling gas
− the edge length of the husks
According to the results of the simulation, by using Xenon as filling gas instead of air, the effective thermal conductivity λeff can be reduced by 21.6 %. The optimal value of the husk’s edge length a depends on the filling gas und therefore varies between 4 and 6 mm. The optimization of the edge length leads to a reduction of the effective thermal conductivity λeff of 7.5 %, according to the model.
When implementing individual calculated optimizations in real insulation boards, the thermal conductivity could be improved by up to 32 %. The project is funded by BMWi, the German ministry of economy and energy, and supervised by AiF, the working group of industrial research associations.},
keywords = {Biogenic Residues, Energy Flexibility, Insulating panels, model based optimization, Sustainability, Thermal conductivity},
pubstate = {published},
tppubtype = {article}
}
− husk geometry
− density of the pure husk material
− thermal conductivity of the pure husk material
− emission coefficient of the husk's surface
− convection coefficient inside the husks
− density of the binder
− thickness of the binder
− thermal conductivity of the binder
The solved model is validated with produced insulating panels out of grain husks. The optimization of the effective thermal conductivity λeff is done regarding the following parameters:
− the filling gas
− the edge length of the husks
According to the results of the simulation, by using Xenon as filling gas instead of air, the effective thermal conductivity λeff can be reduced by 21.6 %. The optimal value of the husk’s edge length a depends on the filling gas und therefore varies between 4 and 6 mm. The optimization of the edge length leads to a reduction of the effective thermal conductivity λeff of 7.5 %, according to the model.
When implementing individual calculated optimizations in real insulation boards, the thermal conductivity could be improved by up to 32 %. The project is funded by BMWi, the German ministry of economy and energy, and supervised by AiF, the working group of industrial research associations.
2020
Schlüter, W.; Wagner, D.
Optimization of Operational Parameters in Biogas Plants using the Anaerobic Digestion Model Number 1 (ADM1) Artikel Open Access
In: Simulation Notes Europe, Bd. 30, S. 11–14, 2020, ISBN: 2305-9974.
Abstract | BibTeX | Schlagwörter: Biogas plants, Biogasanlagen, model based optimization, Nachwachsende Rohstoffe | Links:
@article{WagnerD..,
title = {Optimization of Operational Parameters in Biogas Plants using the Anaerobic Digestion Model Number 1 (ADM1)},
author = {W. Schl\"{u}ter and D. Wagner },
url = {https://www.sne-journal.org/fileadmin/user_upload_sne/SNE_Issues_OA/SNE_30_1/articles/sne.30.1.10503.sn.OA.pdf},
doi = {0.11128/sne.30.sn.10503},
isbn = {2305-9974},
year = {2020},
date = {2020-10-10},
urldate = {2020-10-10},
journal = {Simulation Notes Europe},
volume = {30},
pages = {11--14},
abstract = {While the main objective in energy production is the reduction of fossil fuels, CO2-production by fossil fuels increased over the last decade. Therefore the need for usage of regenerative energies is obvious. Biogas plants are advantageous because they can be used with-out spatial limitation and their substrate is abundant ubiquitously as it covers the whole range of produced organic matter from photosynthesis, municipal, industri-al and animal waste. Although the need for optimization strategies is given, the fermentation process in biogas plants is complex and therefore traditional optimization approaches are cumbersome and carry the risk of com-plete plant failure. In this paper the optimization poten-tial of the ADM1, which represents a detailed description of the anaerobic digestion process is analysed and com-pared to the standard ADM1 setup. Technical parame-ters like substrate composition and dilution rate are optimized to yield a high methane gas flow. It is shown that the optimization of substrate composition has a direct impact on the maximum applicable dilution rate. It is also shown that the feeding rate can be increased to yield higher productivities with optimized substrate compositions.},
keywords = {Biogas plants, Biogasanlagen, model based optimization, Nachwachsende Rohstoffe},
pubstate = {published},
tppubtype = {article}
}
2019
Kapischke, J.; Petsch, R.
Fachsymposium 2019: „Innovative Sensorik, verteilte Sensorsysteme, neue Technologien und Anwendungsfelder“: Modellbasierte Detektion motorischer NO-und NO2-Emissionen Proceedings Article
In: Karlsruher Institut für Technologie (KIT), 2019.
BibTeX | Schlagwörter: model based optimization
@inproceedings{R.Petsch.2019,
title = {Fachsymposium 2019: „Innovative Sensorik, verteilte Sensorsysteme, neue Technologien und Anwendungsfelder“: Modellbasierte Detektion motorischer NO-und NO2-Emissionen},
author = {J. Kapischke and R. Petsch },
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
publisher = {Karlsruher Institut f\"{u}r Technologie (KIT)},
institution = {Karlsruher Institut f\"{u}r Technologie},
keywords = {model based optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
Schlüter, W.; Wagner, D.
Model-based computation of critical operation points in biogas producing plants Artikel
In: Applied Mechanics and Materials (AMM), Nr. 882, 2018.
Abstract | BibTeX | Schlagwörter: Biogas plants, Biogasanlagen, model based optimization | Links:
@article{WagnerD..2018,
title = {Model-based computation of critical operation points in biogas producing plants},
author = {W. Schl\"{u}ter and D. Wagner },
url = {https://www.researchgate.net/publication/326553770_Model-Based_Computation_of_Critical_Operation_Points_in_Biogas_Producing_Plants},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Applied Mechanics and Materials (AMM)},
number = {882},
abstract = {Reducing the use of fossil fuels for energy production is one of the main objectives in 21 st century. In order to achieve this target renewable energy resources (like agricultural waste) in biogas plants can be used. An anaerobic bacterial fermentation process digests the substrates into methane and carbon dioxide. The process itself has strong fluctuations in terms of net methane yield due to different amounts and composition of agricultural influents. For increasing the space time yield two main difficulties are encountered. The first one is system-specific and includes stirrer design and reactor geometry. The second affects the biotechnological fermentation process. The following work is focusing on the fermentation process. The determination of critical parameters for the optimization of the anaerobic microbial digestion is investigated. An economic approach for solving these problems is only feasible by using mathematical models and simulation. Consequently two fermentation models are compared by regarding parameter sensitivity and critical operational points. The first one is based on simple Monod-kinetics while the second one is extended with two steps of fermentation and therefore two different microbial consortia and additive inhibition effects. The complex model is able to describe different phenomena in more detail. But its estimability and therefore its validation is difficult without further investigation of the model structure and the reduction of the model complexity. One important result of the investigation is that stable process conditions with simultaneous high yields are depending on a careful adjustment of the loading rate and therefore requiring precise model parameters.},
keywords = {Biogas plants, Biogasanlagen, model based optimization},
pubstate = {published},
tppubtype = {article}
}