Offenburg University of Applied Sciences
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AI-Based Learning Recommendations: Use in Higher Education
Get PDFWe propose the extension for Artificial Intelligence (AI)-supported learning recommendations within higher education, focusing on enhancing the widely-used Moodle Learning Management System (LMS) and extending it to the Learning eXperience Platform (LXP). The proposed LXP is an enhancement of Moodle, with an emphasis on learning support and learner motivation, incorporating various recommendation types such as content-based, collaborative, and session-based recommendations to provide the next learning resources given by lecturers and retrieved from the content curation of Open Educational Resources (OER) for the learners. In addition, we integrated a chatbot using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with AI-based recommendations to provide an effective learning experience
Modelling biological ex-situ Methanation in a Novel Inverse Membrane Reactor
No full textBiological ex situ methanation of CO2 and H2O in membrane reactors has become an interesting Power-to-Gas method for turning clean, renewable energy into fuel that can be used on a large scale. In this study, a two-zone dynamic model was used in Berkeley Madonna to predict the concentrations of H₂, CO₂, and CH₄ over time. The model includes a membrane module (MM) for gas diffusion and a downstream degassing reactor (DGR). We combined mass balances with a dual-substrate Monod kinetic expression and Henry's law (Sander, 2015)saturation calculations. Model parameters were calibrated against a 412‐day experimental dataset (Haitz et al. 2024), at loop pressures of 1.30, 1.56, and 2.30barg, with different gas-feed ratios and liquid recirculation rates.
Simulations show that membrane-controlled mass transfer, not microbial turnover, controls the formation of methane. 2.30barg was found to be the best pressure for getting ≥97vol% CH₄ purity and <5vol% leftover H₂ within 10 minutes. Lower pressures had the same effect on degassing, but hydrogen uptake was slower. This shows the trade-off between how easily something dissolves and how much energy it takes to dissolve it. The sensitivity analysis shows that increasing the volumetric mass-transfer coefficient (kₗₐ) or the membrane surface area greatly increases the methane yield. To sum up, the tested model is a reliable way to quickly evaluate different operating situations. It guides IMR design and operation for pilot-scale Power-to-Gas integration and moves forward the storage of renewable energy and the reuse of carbon
Hybrid System in Foil Containing Secure Identification and Temperature Sensing Units
No full textFlexible hybrid electronics allow for seamless integration of sensing functionalities within materials, nonconformal surfaces, and products, and thus can enable novel value chains. Next to new functionalities, product authenticity plays a crucial role in complex global supply chains. This holds especially true, when products are deployed in critical environments, such as the industrial or automotive sector, where product failure can be fatal. In this work, we present a secure hybrid system, which contains a custom-designed, thinned application-specific integrated circuit (ASIC) in foil, as well as two printed temperature sensing elements that are seamlessly embedded in an industrial process fabricated automotive coolant hose and an inkjet-printed unique identifier in the form of a physically unclonable function (PUF) to derive the system’s authenticity. We show the results of the standalone hose-integrated temperature sensors, the bulk ASIC verification results prior to thinning and foil integration, and the fully assembled integrated hybrid system. The thinned ASIC in foil communication interfaces, its circuit building blocks, and the integrated printed components were successfully commissioned. We show the obtained temperature response and the unique identification by generating the challenge-response pairs (CRPs) of the PUF over 1000 repetitions. The security circuit shows only 0.0084% of flipped bits at T=25 ∘ C, which makes it well-suited to be used as PUF
From University to Industry: Holistic Robotics Education for Tomorrow’s Challenges
No full textInnovative teaching concepts play a pivotal role in preparing students for the demands of modern industries. The Work-Life Robotics Institute (WLRI) at the University of Applied Sciences Offenburg develops educational modules that uniquely integrate theory and practice through robotics, automation, and rapid prototyping. The aim is to create a practice-oriented education that not only imparts technical knowledge but also fosters independent problem-solving skills and interdisciplinary collaboration. A particular highlight is the combination of hands-on training with innovative technologies, enabling students to experience real industrial scenarios in a practical context. This approach not only enhances technical competencies but also develops soft skills such as teamwork and analytical thinking. This contribution demonstrates how these teaching approaches contribute to a holistic education by preparing students for both technical and strategic challenges in the modern workforce
Multiphase cooling of electric motor using CFD
No full textThis thesis investigates advanced multiphase cooling strategies for electric motors using Computational Fluid Dynamics (CFD), with a focus on oil-based and hybrid air–oil cooling approaches. The study is motivated by the increasing thermal demands of high-power-density electric motors, where conventional air cooling is insufficient to maintain safe operating temperatures. Simulations are conducted using STAR-CCM+ to analyze fluid flow, heat transfer, and multiphase interactions within realistic motor geometries. A systematic evaluation of numerical solver configurations—Standard Solver Settings (SSS) and Tight Solver Settings (TSS)—is performed to assess their impact on numerical stability, convergence, and physical accuracy. The Mixture Multiphase (MMP) model is used as the primary framework and is extended through hybrid coupling with Lagrangian Multiphase (LMP) and Fluid Film (FF) models to capture dispersed droplets, wall films, and unresolved mixtures within a single simulation. The influence of motion modelling approaches, including Moving Reference Frame (MRF) and Rigid Body Motion (RBM), as well as interaction length scale and slip limiters, is examined in detail. Results demonstrate that tighter solver settings and appropriate limiter strategies significantly improve mass conservation, stability, and thermal prediction accuracy. The hybrid multiphase approach successfully captures droplet breakup, film formation, re-entrainment, and conjugate heat transfer effects, enabling realistic prediction of cooling performance in complex motor regions such as end windings. Overall, the work establishes a robust CFD methodology for simulating multiphase cooling in electric motors and provides guidance for transferring modelling strategies across different motor design
Linking User Experience and Business Outcomes: How Perceived Usefulness of AI Chatbots Predicts Satisfaction and NPS
No full textThe integration of AI-based features is rapidly transforming interactions with software systems. While these innovations aim to enhance functionality, their impact on user experience and business outcomes such as satisfaction and loyalty remains underexplored. This study investigates how the user experience (UX) of AI chat bots relates to two key user-level outcomes: Customer Satisfaction (CSAT) and Net Promoter Score (NPS). Drawing on a sample of N = 146 users, we conducted regression analyses, including interaction terms with AI usage frequency and perceived competency. Results indicate that perceived Usefulness significantly predicts both CSAT and NPS, with partial support of moderation effect by the frequency of AI use. Specifically, higher usage increases the positive impact of Usefulness on NPS. Overall, our regression models for CSAT and NPS explained around 39% and 48% of the variance, respectively. These results indicate a good model fit and underline the importance of good UX in AI systems, as this is significantly impacting the satisfaction and loyalty of users. In summary, by linking established UX metrics to strategic business indicators, we show how UX professionals can contribute to more business value and additionally offer guidance to adopt a more user-centered perspective on AI development
Resilient Pathways for Zambia's Power System: Modelling a Low-Carbon Future Using the PyPSA-Earth-Zambia Framework
No full textZambia’s electricity sector is heavily reliant on hydropower, constituting 84% of its generation capacity. This dependency exposes the nation to significant risks from climate-induced hydrological variability, as seen in recent droughts that led to severe load shedding and economic disruption. Addressing this challenge is critical to achieving universal electricity access, enhancing energy security, and aligning with global decarbonisation goals. This study employs the PyPSA-Earth Zambia framework to evaluate twelve integrated scenarios that incorporate electricity demand growth, hydrological variability, and coal power. By analysing these scenarios, the research investigates the trade-offs between maintaining grid stability, achieving climate targets, and expanding equitable access to electricity by 2030. The methodology uniquely combines detailed energy modelling with scenario-based planning to assess Zambia’s options for a resilient energy transition. Key findings highlight that solar and wind energy are pivotal in diversifying Zambia’s energy mix, reducing reliance on hydropower, and mitigating climate risks. Renewable-dominant scenarios demonstrate long-term cost advantages and environmental benefits, despite higher initial capital investments, while coal-reliant pathways undermine climate commitments. Flexible technologies, such as biomass and battery storage, are critical for maintaining grid reliability during periods of low hydropower output. The study provides actionable insights for policymakers, including prioritising decentralised renewable solutions, modernising infrastructure, and leveraging innovative financing mechanisms to attract private investment. By addressing Zambia’s climate-water-energy nexus, this research contributes a blueprint for sustainable development, offering lessons for other nations facing similar energy challenges and aligning with Sustainable Development Goals 7 (Affordable and Clean Energy) and 13 (Climate Action)
Compressor applications and comparative analysis in process industries
No full textCompressors are critical components in various process industries, used toincrease the pressure of gases or vapors for a variety of purposes such as refrigeration, pneumatic transportation, and power production. Understanding the various roles and features of these compressors is very important for improving efficiency, lowering energy consumption, and maintaining the dependability of industrial operations. Thus,this work focuses on the different types of compressors, such as reciprocating, rotary,lobe, cryogenic, and oil-free compressors. Reciprocating compressors are indispensablein various industries, including petrochemicals and chemical processing, where precisepressure control is paramount. The role of rotary compressors, particularly in HVACsystems, is briefly examined in this work. In addition to these, the various roles of thesedifferent types of compressors in various process industries such as pharmaceuticals,food and beverage, petrochemicals, and wastewater treatment industries are discussed.Each compressor type specializes in certain industrial sectors, whether due to effi-ciency, versatility, or environmental concerns. The right choice can enhance efficiency,reduce energy consumption, and contribute to the overall success of industrial opera-tions. A comparative analysis is conducted in this work for these different types of com-pressors by highlighting their respective advantages and limitations. As we navigate theever-changing terrain of process industries, it’s evident that these compressors remainfundamental in shaping the future of industrial innovation and sustainability, driving advancements that benefit both industry and the environment
Efficiency Prediction of the LIVARSA Filter Through Comprehensive Power Quality Analysis
No full textElectricity plays a crucial role in both industrial and commercial sectors, enabling efficient operations, automation, and overall business growth. However, the quality of energy is not always the same and varies depending on factors like supplier, consumer, time, grid location etc. In present days, Power Quality is becoming a popular topic and indicates the degree to which the basic energy (electrical) parameters like supply voltage, consumed current, frequency and waveform conform to the standard norms. Any deviation from the established standard leads to poor power quality. The effect of poor power quality is versatile in nature. It reduces the lifetime of costly equipment, increases power consumption by increasing losses etc. Various types of Electric filters (both passive and active) with different operating principles were developed to improve power quality as received from the transformer and sent it to the consumer. LIVARSA GmbH, a private sector company based on Switzerland and Germany, sales a unique type electric filter called “LIVARSA Efficiency Filter” to some specific European Countries which improves power quality and reduce electrical energy losses. As a competitive business model, LIVARSA proposes a performance guarantee in their techno-commercial proposal (prior to sale) which is real energy (kWh) savings (%) after installation of LIVARSA Efficiency Filter. However, proposing a specific number as guaranteed savings (%) is difficult as the energy quality of each customer is different from others. Incorrect guarantees can leads to huge financial losses. The primary objective of the thesis works is to analyse the power quality data of existing customers of LIVARSA and make a logical prediction of energy savings (%) for new customers by using suitable statistical tools (like coefficient of correlation) and basic machine learning approaches like polynomial regression analysis. In this thesis work a wide range of derived power quality factors and their individual effect on energy savings (%) are analysed. The works involves advanced Python-based data visualisation techniques, the use of various modules and functions like datetime, scipy.fft, numpy.polyfit etc