@inproceedings{SchmittAntonovSchmittetal., author = {Schmitt, Anna-Maria and Antonov, Anna and Schmitt, Jan and Engelmann, Bastian}, title = {Classification of Production Process Phases with Multivariate Time Series Techniques}, series = {2024 22nd International Conference on Research and Education in Mechatronics (REM)}, booktitle = {2024 22nd International Conference on Research and Education in Mechatronics (REM)}, doi = {10.1109/REM63063.2024.10735481}, language = {en} } @inproceedings{LerchSchellerBruckner, author = {Lerch, Philipp and Scheller, Fabian and Bruckner, Thomas}, title = {Potential of demand response for chlor-alkali electrolysis processes}, series = {19th International Conference on the European Energy Market (EEM)}, booktitle = {19th International Conference on the European Energy Market (EEM)}, isbn = {979-8-3503-1258-4}, doi = {10.1109/eem58374.2023.10161892}, pages = {1 -- 7}, abstract = {Chlor-alkali electrolysis indicates significant demand response potential, accounting for over 2\% of Germany's total electricity demand. To fully analyze this potential, digital models or digital twins are necessary. In this study, we use the IRPopt modeling framework to develop a digital model of an electrolysis process and examine the cost-optimal load shifting application in the day-ahead spot and balancing reserve market for various price scenarios (2019, 2030, 2040). We also investigate the associated CO 2 emissions. Combined optimization at both markets results in greater and more robust cost savings of 16.1\% but cannibalizes the savings that are possible through optimization separately at each market. In future scenarios, the shares of savings from spot and reserve market could potentially reverse. CO 2 savings between 2.5\% and 9.2\% appear only through optimization at the spot market and could even turn negative if optimized solely at the reserve market.}, language = {en} } @article{LerchSchellerReicheltetal., author = {Lerch, Philipp and Scheller, Fabian and Reichelt, David G. and Menzel, Katharina and Bruckner, Thomas}, title = {Electricity cost and CO2 savings potential for chlor-alkali electrolysis plants: Benefits of electricity price dependent demand response}, series = {Applied Energy}, volume = {355}, journal = {Applied Energy}, issn = {0306-2619}, doi = {10.1016/j.apenergy.2023.122263}, abstract = {Chlor-alkali electrolysis plays a significant role in Germany's electricity demand, with a share of >2\%. It offers a promising avenue for leveraging demand response strategies. In times of escalating electricity prices, load shifting can help to maintain economic competitiveness of domestic industries. This study aims to assess the potential for electricity cost savings and related CO2 emission reductions through optimal load shifting practices for chlor-alkali electrolysis, considering both current and projected future electricity prices. The research employs the mixed-integer energy system modeling framework IRPopt to model and optimize a chlorine value chain under various scenarios. A sensitivity analysis is conducted to identify the primary factors driving electricity cost savings. The findings reveal that, based on the 2019 electricity price distribution (before pandemic and energy crisis), load shifting can yield electricity cost savings of 5.8\% and CO2 emission reductions of 2.7\%. As the share of variable renewable energy sources increases along with other driving factors, the electricity price spread is projected to rise from 28\% in 2019 to an average of 87\% in 2040 scenarios. Consequently, electricity cost savings rise up to 22\% and CO2 emission reductions up to 10\%. The primary drivers behind electricity cost savings include electricity price spreads and the utilization and operating range of the electrolyzer. In addition to the business-level advantages, the resulting decrease in residual load and lower CO2 emissions contribute to the integration of variable renewable energy sources and the achievement of decarbonization targets at the economic level.}, language = {en} } @article{SchellerMorrisseyNeuhoffetal., author = {Scheller, Fabian and Morrissey, Karyn and Neuhoff, Karsten and Keles, Dogan}, title = {Green or greedy: the relationship between perceived benefits and homeowners' intention to adopt residential low-carbon technologies}, series = {Energy Research \& Social Science}, volume = {108}, journal = {Energy Research \& Social Science}, issn = {2214-6296}, doi = {10.1016/j.erss.2023.103388}, abstract = {Transitioning to a net-zero economy requires a nuanced understanding of homeowners' decision-making pathways when considering the adoption of Low Carbon Technologies (LCTs). These LCTs present both personal and collective benefits, with positive perceptions critically influencing attitudes and intentions. Our study analyses the relationship between two primary benefits: the household-level financial gain and the broader environmental advantage. Focusing on the German homeowners' intention to adopt Rooftop Photovoltaic Systems, Energy Efficient Appliances, and Green Electricity Tariffs, we employ Partial Least Squares Structural Equation Modeling to demonstrate that the adoption intention of the LCTs is underpinned by the Theory of Planned Behaviour. Our research also highlights the pivotal role of perceived benefits in shaping attitudes and intentions. Attitudes towards the LCTs are more strongly related to product-specific benefits than affective constructs. In terms of evaluative benefits, environmental benefits exhibit a higher positive association with attitude formation compared to financial benefits. However, this relationship switches as homeowners move through the decision process with the financial benefits of selected LCTs having a consistently higher association with adoption intention. At the same time, financial benefits also positively affect attitudes. Observing this trend across both low- and high-cost LCTs, we recommend that policymakers and businesses enhance homeowners' awareness of the personal advantages and implement strategies to make these financial benefits more tangible and accessible.}, language = {en} } @inproceedings{HartmannDobhanEngelmannetal., author = {Hartmann, J{\"u}rgen and Dobhan, Alexander and Engelmann, Bastian and Eberhardt, Lars and Heusinger, Moritz and Raab, C and Schleif, Frank-Michael and T{\"u}rk, M.}, title = {Optimierung von Prozessen und Werkzeugmaschinen durch Bereitstellung, Analyse und Soll-Ist-Vergleich von Produktionsdaten: Digitalkonferenz}, language = {en} } @article{SchmittEngelmannManghisietal., author = {Schmitt, Jan and Engelmann, Bastian and Manghisi, Vito Modesto and Wilhelm, Markus and Uva, Antonello and Fiorentino, Michele}, title = {Towards gestured-based technologies for human-centred smart factories}, series = {International Journal of Computer Integrated Manufacturing}, volume = {36}, journal = {International Journal of Computer Integrated Manufacturing}, number = {1}, issn = {1362-3052}, pages = {110 -- 127}, abstract = {Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, - monitoring or - control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator - ErgoTakt - derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers' ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown.}, language = {en} } @techreport{HuttelmaierHesselbachHeigl, type = {Working Paper}, author = {Huttelmaier, Hannes and Hesselbach, Nicolas and Heigl, Julia}, title = {Stand von Marketing Automation 2022: Einsatz und Nutzung in deutschen Unternehmen}, doi = {10.57714/h41w-2h59}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-19933}, pages = {31}, abstract = {Diese Studie untersucht die Einf{\"u}hrung und die Nutzung von Marketing Automation in deutschen Unternehmen. Hierzu wurden im Dezember 2021 und Januar 2022 173 in Marketing oder Vertrieb t{\"a}tige und/oder {\"u}ber Erfahrung im Bereich Marketing Automation verf{\"u}gende Personen aus Unternehmen verschiedener Branchen mit einem Schwerpunkt auf Maschinen- und Anlagenbau sowie Software/IT in Deutschland befragt. Die Ergebnisse liefern interessante Erkenntnisse f{\"u}r Wissenschaft und Praxis.}, language = {de} } @article{SchmittSeitzScherdeletal., author = {Schmitt, Jan and Seitz, Philipp and Scherdel, Christian and Reichenauer, Gudrun}, title = {Machine Learning in the development of Si-based anodes using Small-Angle X-ray Scattering for structural property analysis}, series = {Computational Materials Science}, volume = {218}, journal = {Computational Materials Science}, issn = {1879-0801}, abstract = {Material development processes are highly iterative and driven by the experience and intuition of the researcher. This can lead to time consuming procedures. Data-driven approaches such as Machine Learning can support decision processes with trained and validated models to predict certain output parameter. In a multifaceted process chain of material synthesis of electrochemical materials and their characterization, Machine Learning has a huge potential to shorten development processes. Based on this, the contribution presents a novel approach to utilize data derived from Small-Angle X-ray Scattering (SAXS) of SiO_2 matrix materials for battery anodes with Neural Networks. Here, we use SAXS as an intermediate, high-throughput method to characterize sol-gel based porous materials. A multi-step-method is presented where a Feed Forward Net is connected to a pretrained autoencoder to reliably map parameters of the material synthesis to the SAXS curve of the resulting material. In addition, a direct comparison shows that the prediction error of Neural Networks can be greatly reduced by training each output variable with a separate independent Neural Network.}, language = {en} } @inproceedings{RosiliusWilhelmvonEitzenetal., author = {Rosilius, Maximilian and Wilhelm, Markus and von Eitzen, Ingo and Decker, Steffen and Damek, Sebastian and Br{\"a}utigam, Volker}, title = {Sustainable Solutions by the Use of Immersive Technologies for Repurposing Buildings}, series = {Global Conference on Sustainable Manufacturing}, booktitle = {Global Conference on Sustainable Manufacturing}, publisher = {Springer}, doi = {10.1007/978-3-031-28839-5_62}, pages = {551 -- 558}, abstract = {In the context of urban production and sustainable reuse of existing buildings, a detailed planning of the later usage is indispensable. One approach is to enable large-scale AR simulation on site with a sufficient Level of Detail (LoD) and stability. To determine performance metrics, a technology-stack is created and presented that enables a realistic field experiment in an industrial environment (area of 1,314 m2) using Microsoft HoloLens 2. For the experiment, a 3D model was instantiated as often as possible up to the limit of system stability and in different LoDs (100\% down to 10\%). The result shows that it is feasible to represent 2.63 million polygons (equivalent to about 1,909 m3 of augmented space) on LOD-35\%; LoD-100\% is equivalent to 327.38 m3 and 1,284 million polygons. Polygonal density [polygons/m3] is introduced as new indicator for better comparability when using 3D models. Thus, it is possible to immersively visualize urban production planning processes in large-scale scenarios. This expands the functional planning space of Urban Production and overcomes previous technical limitations.}, language = {en} } @article{FischerKeuppPaethetal., author = {Fischer, Sophie and Keupp, Luzia and Paeth, Heiko and G{\"o}hlich, Michael and Schmitt, Jan}, title = {Climate adaptation as organizational learning: a grounded theory study on manufacturing companies in a Bavarian Region}, series = {Education Sciences}, volume = {12}, journal = {Education Sciences}, number = {1}, pages = {22 -- 22}, language = {en} }