Fakultät für Elektrotechnik und Informatik
https://www.repo.uni-hannover.de/handle/123456789/4
Frei zugängliche Publikationen aus der Fakultät für Elektrotechnik und Informatik2024-03-19T04:28:25ZEnergetic evaluation and optimization of hydrogen generation and compression pathways considering PEM water electrolyzers and electrochemical hydrogen compressors
https://www.repo.uni-hannover.de/handle/123456789/16774
Energetic evaluation and optimization of hydrogen generation and compression pathways considering PEM water electrolyzers and electrochemical hydrogen compressors
Zachert, Lars; Suermann, Michel; Bensmann, Boris; Hanke-Rauschenbach, Richard
Electrochemical hydrogen compression is seen as a promising alternative to mechanical compression in the context of power-togas plants. It can be carried out either as direct co-compression in a water electrolyzer (WE) or via a separate electrochemical hydrogen compressor (EHC). This study analyzes the specific energy demand of different hydrogen generation and compression pathways using WEs and EHCs, both based on proton exchange membrane (PEM) technology, for pressures up to 1000 bar. The energy demand is systematically investigated as a function of design parameters such as pressure, current density, temperature and membrane thickness and presented in overpotential-specific and gas-crossover dependent shares. The analysis reveals intrinsic differences in the compression behavior of WEs and EHCs. In the EHC, permeated hydrogen is simply re-compressed back to the cathode. In the WE, instead, water has to be split again to compensate for the hydrogen loss, causing energetic disadvantages with increasing hydrogen pressure. Moreover, using an EHC enables design parameters to be optimized separately regarding hydrogen generation and compression. Therefore, at low current densities, compression via EHC is already favorable to co-compression via WE for pressures above 4 bar. With increasing current density, however, this intersection point shifts up to pressures above 200 bar.
2021-01-01T00:00:00ZESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set
https://www.repo.uni-hannover.de/handle/123456789/16720
ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set
Günther, Sebastian; Brandt, Jonathan; Bensmann, Astrid; Hanke-Rauschenbach, Richard
This paper introduces an univariate application-independent set of load profiles or time series derived from real-world energy system data. The generation involved a two-step process: manifolding the initial dataset through signal processors to increase diversity and heterogeneity, followed by a declustering process that removes data redundancy. The study employed common feature engineering and machine learning techniques: the time series are transformed into a normalized feature space, followed by a dimensionality reduction via hierarchical clustering, and optimization. The resulting dataset is uniformly distributed across multiple feature space dimensions while retaining typical time and frequency domain characteristics inherent in energy system time series. This data serves various purposes, including algorithm testing, uncovering functional relationships between time series features and system performance, and training machine learning models. Two case studies demonstrate the claims: one focused on the suitability of hybrid energy storage systems and the other on quantifying the onsite hydrogen supply cost in green hydrogen production sites. The declustering algorithm, although a bys study, shows promise for further scientific exploration. The data and source code are openly accessible, providing a robust platform for future comparative studies. This work also offers smaller subsets for computationally intensive research. Data and source code can be found at https://github.com/s-guenther/estss and https://zenodo.org/records/10213145 .
2024-01-01T00:00:00ZPreparation of Dräger Atlan A350 and General Electric Healthcare Carestation 650 anesthesia workstations for malignant hyperthermia susceptible patients
https://www.repo.uni-hannover.de/handle/123456789/16693
Preparation of Dräger Atlan A350 and General Electric Healthcare Carestation 650 anesthesia workstations for malignant hyperthermia susceptible patients
Heiderich, Sebastian; Thoben, Christian; Dennhardt, Nils; Krauß, Terence; Sümpelmann, Robert; Zimmermann, Stefan; Reitz, Michael; Rüffert, Henrik
Background: Patients at risk of malignant hyperthermia need trigger-free anesthesia. Therefore, anesthesia machines prepared for safe use in predisposed patients should be free of volatile anesthetics. The washout time depends on the composition of rubber and plastic in the anesthesia machine. Therefore, new anesthesia machines should be evaluated regarding the safe preparation for trigger-free anesthesia. This study investigates wash out procedures of volatile anesthetics for two new anesthetic workstations: Dräger Atlan A350 and General Electric Healthcare (GE) Carestation 650 and compare it with preparation using activated charcoal filters (ACF). Methods: A Dräger Atlan and a Carestation 650 were contaminated with 4% sevoflurane for 90 min. The machines were decontaminated with method (M1): using ACF, method 2 (M2): a wash out method that included exchange of internal parts, breathing circuits and soda lime canister followed by ventilating a test lung using a preliminary protocol provided by Dräger or method 3 (M3): a universal wash out instruction of GE, method 4 (M4): M3 plus exchange of breathing system and bellows. Decontamination was followed by a simulated trigger-free ventilation. All experiments were repeated with 8% desflurane contaminated machines. Volatile anesthetics were detected with a closed gas loop high-resolution ion mobility spectrometer with gas chromatographic pre-separation attached to the bacterial filter of the breathing circuits. Primary outcome was time until < 5 ppm of volatile anesthetics and total preparation time. Results: Time to < 5 ppm for the Atlan was 17 min (desflurane) and 50 min (sevoflurane), wash out continued for a total of 60 min according to protocol resulting in a total preparation time of 96-122 min. The Carestation needed 66 min (desflurane) and 24 min (sevoflurane) which could be abbreviated to 24 min (desflurane) if breathing system and bellows were changed. Total preparation time was 30-73 min. When using active charcoal filters time to < 5 ppm was 0 min for both machines, and total preparation time < 5 min. Conclusion: Both wash out protocols resulted in a significant reduction of trace gas concentrations. However, due to the complexity of the protocols and prolonged total preparation time, feasibility in clinical practice remains questionable. Especially when time is limited preparation of the anesthetic machines using ACF remain superior.
2021-01-01T00:00:00ZDeep Learning Based Classification of Pedestrian Vulnerability Trained on Synthetic Datasets
https://www.repo.uni-hannover.de/handle/123456789/16689
Deep Learning Based Classification of Pedestrian Vulnerability Trained on Synthetic Datasets
Schleusner, Jens; Neu, Lothar; Behmann, Nicolai; Blume, Holger
The reliable detection of vulnerable road users and the assessment of the actual vulnerability is an important task for the collision warning algorithms of driver assistance systems. Current systems make assumptions about the road geometry which can lead to misclassification. We propose a deep learning-based approach to reliably detect pedestrians and classify their vulnerability based on the traffic area they are walking in. Since there are no pre-labeled datasets available for this task, we developed a method to train a network first on custom synthetic data and then use the network to augment a customer-provided training dataset for a neural network working on real world images. The evaluation shows that our network is able to accurately classify the vulnerability of pedestrians in complex real world scenarios without making assumptions on road geometry.
2019-01-01T00:00:00Z