Offenburg University of Applied Sciences
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High-resolution Calculation of Hydrogen Potentials
No full textGreen hydrogen production using electrolysis powered by renewable energies has a high relevance in decarbonization and the deceleration of climate change. Having economically competitive hydrogen production costs presents major challenge in times of an increasing demand. In response, this master thesis develops a computational tool that integrates country-specific renewable electricity generation potentials, specifically from photovoltaic and onshore wind power plants to derive an optimized Levelized Cost of Hydrogen (LCOH) expression through non-linear optimization. Different capacity ratios between the electrolyser and the renewable electricity source are analyzed to calculate the usable electricity in terms of these capacity ratios, which are different and specific for each tile, highlighting the importance of having a tool with high geographical resolution. The input electricity for the electrolyser is combined when we have the availability of both photovoltaic and wind generation in the same area. Therefore, two main scenarios are evaluated: one with either PV or wind as a renewable electricity source; the other using the renewable electricity from both renewable sources. The algorithm of the tool allows it to be applied globally, supporting the scenario-based evaluations to assess the potential of new green hydrogen projects across regions.
According to results from three countries—Colombia, Germany, and New Zealand— reducing electrolyser capacity proves especially beneficial in areas with lower full load hours. The calculations from this thesis indicate that projected for 2050, the optimized LCOH for Colombia has range from 39.34€ to 64.4€ per MWh, given stable solar irradiance, while the optimal LCOH for Germany has a range around 41.81€ to 79.73€ per MWh, influenced by fewer Full Load Hours (FLH) for PV a higher FLH for wind. In New Zealand the optimized LCOH sits between 42.46€ to 62.75€ per MWh due to its combination of wind and PV resources. These findings highlight the importance of site-specific considerations in optimizing green hydrogen production costs
Evaluation of a method for the additive tooling of injection mould inserts
No full textPurpose
This study aims to investigate a systematic approach to the production and use of additively manufactured injection mould inserts in product development (PD) processes. For this purpose, an evaluation of the additive tooling design method (ATDM) is performed.
Design/methodology/approach
The evaluation of the ATDM is conducted within student workshops, where students develop products and validate them using AT-prototypes. The evaluation process includes the analysis of work results as well as the use of questionnaires and participant observation.
Findings
This study shows that the ATDM can be successfully used to assist in producing and using AT mould inserts to produce valid AT prototypes. As a reference for the implementation of AT in industrial PD, extracts from the work of the student project groups and suitable process parameters for prototype production are presented.
Originality/value
This paper presents the application and evaluation of a method to support AT in PD that has not yet been scientifically evaluated
Analysis and prediction of small-diameter TBM performance in hard rock conditions
No full textAnalysing and predicting the advance rate of a tunnel boring machine (TBM) in hard rock is integral to tunnelling project planning and execution. It has been applied in the industry for several decades with varying success. Most prediction models are based on or designed for large-diameter TBMs, and much research has been conducted on related tunnelling projects. However, only a few models incorporate information from projects with an outer diameter smaller than 5 m and no penetration prediction model for pipe jacking machines exists to date. In contrast to large TBMs, small-diameter TBMs and their projects have been considered little in research. In general, they are characterised by distinctive features, including insufficient geotechnical information, sometimes rather short drive lengths, special machine designs and partially concurring lining methods like pipe jacking and segment lining. A database which covers most of the parameters mentioned above has been compiled to investigate the performance of small-diameter TBMs in hard rock. In order to provide sufficient geological and technical variance, this database contains 37 projects with 70 geotechnically homogeneous areas. Besides the technical parameters, important geotechnical data like lithological information, unconfined compressive strength, tensile strength and point load index is included and evaluated. The analysis shows that segment lining TBMs have considerably higher penetration rates in similar geological and technical settings mostly due to their design parameters. Different methodologies for predicting TBM penetration, including state-of-the-art models from the literature as well as newly derived regression and machine learning models, are discussed and deployed for backward modelling of the projects contained in the database. New ranges of application for small-diameter tunnelling in several industry-standard penetration models are presented, and new approaches for the penetration prediction of pipe jacking machines in hard rock are proposed
Lernen von Entscheidungen und Bewegungsabläufen eines humanoiden Roboters mittels Deep Reinforcement Learning
Get PDFDie Hochschule Offenburg nimmt jedes Jahr mit dem Team magmaOffenburg am RoboCup teil. Hierbei tritt das Offenburger Team in der 3D-Simulationsliga gegen Teams aus der ganzen Welt im Fußball mit autonomen Robotern an. Nun soll der Torwart von Team magmaOffenburg mit Hilfe von Deep Reinforcement Learning verbessert werden. Hierfür wird der Algorithmus PPO unter der Nutzung der RL-X Bibliothek verwendet. Im Laufe dieser Arbeit werden mehrere Versuche durchgeführt, bei denen Modelle für Torwartbewegungen entstehen. Eine dieser Bewegungen kann auf einem sehr schwer abzusichernden Bereich des Tors, in welchem der bisherige Torwart nur 5 Prozent der Bälle halten konnte, nun 36 Prozent der Bälle halten. Diese Arbeit erläutert des Weiteren die Integration dieses Modells für den Torwart in das Spiel von Team magmaOffenburg. Hierbei konnte beim Testen gegen das eigene, vorherige Team eine Reduktion von durchschnittlich 0,135 Gegentoren pro Spiel erreicht werden. Schlussendlich befasst sich diese Arbeit auch noch mit dem Weitertrainieren dieses Modells auf einem erweiterten Torbereich per Curriculum, um die Entscheidungslogik des Torwarts teilweise in das gelernte Modell zu integrieren. Hierdurch konnten in einer Spielserie jedoch nur minimal bessere Ergebnisse von 0,08 Gegentoren weniger als beim bisherigen Torwart erzielt werden.Every year, Offenburg University participates in the RoboCup with Team magmaOffenburg. In this competetion, the team from Offenburg competes in the 3D simulation league in soccer with autonomous robots against teams from all around the globe. Now the goalkeeper of Team magmaOffenburg is supposed to be improved using deep reinforcement learning. The PPO algorithm and the RL-X library are used for this. Throughout this thesis, several experiments are conducted, which result in models for the goalkeeper's movements. One of these movements is capable of preventing 36 percent of goals in a difficult to keep area of the goal, where the previous goalkeeper was only able to prevent 5 percent. Furthermore, this thesis describes the process of integrating this model into Team magmaOffenburgs actual gameplay. Here, a reduction of on average of 0.135 goals scored fewer for the enemy team could be meassured when tested against a team using the old model for goalkeeping. Lastly, this thesis also deals with the continued training of this model on a larger area of the goal via curriculum, to integrate part of the decision-making of the goalkeeper into the learned model. Using this method only lead to a result of 0.08 goals scored fewer for the enemy team when playing against the previous goalkeeper
Grey-box Modelling of Lithium-ion Batteries and Their Slow Voltage Dynamics with Neural Ordinary Differential Equations
No full textGenerative Künstliche Intelligenz als Tool für die PR- und Pressearbeit an Schulen
No full textUnterschiedliche Formen von „Künstlicher Intelligenz“ (KI) sind aktuell auch im schulischen Umfeld in der Diskussion. Dabei liegt der Schwerpunkt der Diskussionen meist auf der Frage, wie man schriftliche Arbeiten angesichts der Möglichkeiten generativer KI-Systeme noch nutzen und bewerten kann. Diese durchaus berechtigte Diskussion verstellt leicht den Blick auf die Frage, wie man mit Hilfe unterschiedlicher KI-Systeme gerade im Bereich Schul-PR, wo viele konzeptionelle und textliche Fragen auftreten, oder auch im Rahmen der visuellen Gestaltung, die Wirksamkeit der PR-Verantwortlichen an Schulen erhöhen und die Qualität der Öffentlichkeitsarbeit und Kommunikation steigern kann
Integration und Erweiterung eines digitalen Zwillings für einen Saugarmroboter sowie Erweiterung der Robotersteuerung
No full textDiese Arbeit beschäftigt sich mit der Integration eines digitalen Zwillings in eine Robotersteuerung. Die Basis bildet der Versuch 3 "Motion Control-Saugarmgreifer" des Labors Regelungs- und Automatisierungstechnik. Unter Verwendung der Siemens Software NX wird ein digitaler Zwilling erstellt und anschließend mit SIMIT um eine kinetische Simulation ergänzt. Nach Fertigstellung des digitalen Zwillings wird dieser mit Siemens Software virtuell in Betrieb genommen und anschließend validiert. Des Weiteren werden die Robotersteuerung erweitert und vorhandene Mängel beseitigt.
Grundlage dieser Thesis bilden vorangegangene Bachelorarbeiten, in denen der digitale Zwilling von Herrn Vitaly Nikishin aufgebaut und die Robotersteuerung von Herrn Dominik Hampel erweitert wurden
Optmization and Processing of Liquid Biowaste into a Complex Medium suitable for Trichoderma reesei Growth and Proteins Production
No full textThe objective of this research was to optimise and validate the use of biowaste as a growth medium for Trichoderma reesei RL-P37, aiming to produce proteins, particularly cellulases, in a cost-effective and sustainable manner. The study consisted of four fermentation experiments conducted using various biowaste compositions, ranging from partial (60%) to complete (100%) substitution of synthetic components with biowaste. A standard laboratory medium was used as a reference to assess protein productivity and biomass yield. In addition, a preliminary T. reesei growth in shaking flasks experiment was carried out to evaluate the suitability of biowaste as a growth medium, confirming that biowaste could support the growth of T. reesei and even resulted in slightly higher biomass production compared to the laboratory medium. Despite similar final protein concentrations across all fermentations (approximately 4 g/L), variations in productivity were observed, with the laboratory medium demonstrating the highest efficiency, followed by 60% and 100% biowaste-based media, respectively. Analysis of yield coefficients revealed that biowaste can sustain growth and protein production; however, its effectiveness was influenced by lot variability and potential inhibitory compounds. Additionally, nutrient imbalances and the presence of possible inhibitory substances posed challenges, as observed in the differences between batch and fed-batch phases. This research demonstrates the potential of biowaste as a complete medium for industrial fermentations, provided nutrient imbalances and inhibitory effects are addressed through optimisation. Further refinement in biowaste preparation and supplementation is required to ensure consistent results and maximise cost-effectiveness for T. reesei fermentations
Piezoresistive-Based Physical Unclonable Function
No full textWith the expansion of Internet-of-Things (IoT) devices in many aspects of our life, the security of such systems has become an important challenge. Unlike conventional computer systems, any IoT security solution should consider the constraints of these systems such as computational capability, memory, connectivity, and energy consumption limitations. Physical unclonable functions (PUFs) with their special characteristics were introduced as hardware-based solutions to satisfy the security needs while respecting the mentioned constraints. They exploit the uncontrollable and reproducible variations of the underlying components for security applications such as identification, authentication, and secure boot. Since IoT devices are typically low cost, it is important to reuse existing elements in their hardware (for instance, sensors, analog-to-digital converters (ADCs), etc.) instead of adding extra costs for the PUF hardware. Micro-electromechanical system (MEMS) devices are widely used in IoT systems as sensors and actuators. In this work, for the first time, a lightweight MEMS-based circuit with a piezoresistive bridge is introduced as a weak PUF. The piezoresistive PUF leverages the uncontrollable variations in the parameters of the circuit elements to derive secure keys for cryptographic applications. The experimental results show that our proposed piezoresistive PUF is capable of generating enough entropy for a complex key generation, while its responses show stability in different environmental conditions. The manufactured piezoresistive PUF shows a uniqueness of 47.73% and a reliability of 94.19%. Moreover, the generated secret keys passed the National Institute of Standards and Technology (NIST) test suite for randomness
Eine Untersuchung der Auswirkungen von Instagram auf die Identitätsbildung junger Erwachsener und die Konzeption der Awareness-Kampagne 'InstAbility'
No full textInstagram ist ein wichtiger Bestandteil des Lebens vieler junger Menschen und bietet eine Bühne zur Selbstinszenierung und sozialen Orientierung. Das Ausprobieren der eigenen Persönlichkeit und der soziale Vergleich sind wesentliche Aspekte der Identitätsarbeit, laut dem Psychoanalytiker Erik H. Erikson. Diese Arbeit untersucht daher, wie Instagram die Identität junger Erwachsener beeinflusst. Eine umfassende Literaturrecherche und Analyse bestehender Studien ergaben, dass eine intensive Instagram-Nutzung häufig psychische Probleme mit sich bringt und sich negativ auf die Entwicklung der Identität auswirkt. Auf Basis dieser Erkenntnisse wurde die Awareness-Kampagne „InstAbility“ entwickelt, die jungen Menschen einen gesunden Umgang mit Instagram ermöglichen soll. Die Kampagne verfolgt das Ziel, mögliche negative Folgen zu identifizieren und Strategien (u. a. Medienkompetenz) für einen reflektierten Umgang mit Medien zu vermitteln. Die Kampagne wird auf Instagram implementiert, um die Zielgruppe direkt zu erreichen und eine positive Identitätsentwicklung zu fördern. Sie soll auch als Grundlage für andere Institutionen dienen, die ähnliche Initiativen unterstützen möchten