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Open and FAIR data for nanofiltration in organic media: a unified approach
No full textOrganic solvent nanofiltration (OSN), also called solvent-resistant nanofiltration (SRNF), has emerged as a promising technology for the removal of impurities, recovery of solutes, and the regeneration of solvents in various industries, such as the pharmaceutical and the (petro)chemical industries. Despite the widespread use of OSN/SRNF, the presence of scattered, non-standardized data, and the absence of openly accessible data pose critical challenges to the development of new membrane materials and processes, their comparison to the state-of-the-art materials, and their fundamental understanding. To overcome these hurdles, data from peer-reviewed research articles and commercial datasheets were curated via a standardized procedure to obtain an extensive dataset on the membrane materials, synthesis parameters, operational conditions, physicochemical properties, and performance of OSN/SRNF membranes. Thanks to a truly impressive joint effort of the OSN/SRNF community, the dataset contains, as per April 2024, 5006 unique membrane filtrations from 294 publications for 42 solvents under several process parameters. This findable, accessible, interoperable, reproducible, and open (FAIR/O) dataset is available on both the OSN Database and the newly inaugurated Open Membrane Database for SRNF (OMD4SRNF). These databases provide multiple visualization and data exploration tools. Here, the standardized procedure applied to curate the data and the functionality of the databases are outlined, as well as the online user interface to deposit new data by external users on the OMD4SRNF. This community-led project has been supported by all the co-authors of this work. Most importantly, they additionally agreed to systematically deposit their future peer-reviewed data on OSN/SRNF into the databases. We thereby pave the road for FAIR/O data in the field of OSN/SRNF to increase transparency, enable more accurate data analysis, and foster collaboration and innovation
Zerspankraftmodellierung und Prozessgrenzen der Umrissbearbeitung von FKV-Bauteilen mit scheibenförmigen Werkzeugen
No full textDie Fertigung definierter Bauteilkonturen mittels spanender Umrissbearbeitung ist ein wichtiger Schritt bei der Herstellung von Schalenbauteilen aus Faser-Kunststoff-Verbund (FKV). Hierfür können neben dem Fräsen auch Verfahren mit scheibenförmigen Werkzeugen, wie das Trennschleifen und Sägen eingesetzt werden. Mit dem Curved Circular Cutting (CCC) steht zudem ein neuartiges Verfahren zur Verfügung, welches die Bearbeitung gekrümmter Konturen mit entsprechenden Werkzeugen ermöglicht. Die genannten Zerspanverfahren sind für die FKV-Bearbeitung bisher jedoch kaum untersucht. Daher fehlt es für die Prozessauslegung und -optimierung an geeigneten Simulationsmodellen und der Kenntnis der relevanten technologischen Prozessgrenzen.
Die vorliegende Arbeit adressiert diesen Bedarf und stellt ein mechanistisches Zerspankraftmodell zur Verfügung, welches für Zerspankraftsimulationen mit beliebigen scheibenförmigen Werkzeuggeometrien angewendet werden kann. Das Modell bezieht dabei erstmals schräge Schnittbedingungen mit räumlichen Trennvorgängen an den Fasern des FKV ein, die insbesondere bei Anwendung des CCC aufgrund komplexer Eingriffsbedingungen in der Kontaktzone auftreten können. Der resultierende Effekt auf das Trennverhalten des orthotropen Werkstoffs wird mittels Zerspanversuchen an unidirektionalem CFK systematisch untersucht, mathematisch abgebildet und in das Zerspankraftmodell eingebunden.
Ergänzend werden die Ergebnisse umfangreicher experimenteller Untersuchungen genutzt, um technologische Prozessgrenzen des CCC zu identifizieren und auf dieser Basis Empfehlungen für die Anwendung abzuleiten.Machining of defined part contours is an essential step in the production of fiber-reinforced plastic (FRP) shell components. Besides milling, processes with disk-shaped tools such as abrasive cutting and sawing can be used for this purpose. In addition, Curved Circular Cutting (CCC) is a novel technology that enables the machining of curved contours with these type of tools. To date, however, there has been comparatively little research into these FRP machining processes. This results in a lack of suitable simulation models and detailed knowledge of the relevant technological process limits with regard to process design and optimization.
This thesis addresses this need and provides a mechanistic cutting force model that can be used for cutting force simulations with arbitrary disk-shaped tool geometries. This model includes for the first time oblique cutting with spatial cutting phenomena at the fibers of the FRP, which can occur in the contact zone due to complex engagement conditions, especially when using the CCC technology. The resulting effect on the cutting behavior of the orthotropic material is systematically investigated by machining tests on unidirectional CFRP, mathematically modeled and integrated into the cutting force model. In addition, the results of comprehensive experimental investigations are used to identify the technological process limitations of CCC and to derive recommendations for the application
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges
No full textAutomatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the “Medico automatic polyp segmentation (Medico 2020)” and “MedAI: Transparency in Medical Image Segmentation (MedAI 2021)” competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a routine clinical examination. For the instrument segmentation task, the best team obtained a mean Intersection over union metric of 0.9364. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models’ credibility for clinical deployment. The best team obtained a final transparency score of 21 out of 25. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage subjective evaluation for building more transparent and understandable AI-based colonoscopy systems. Moreover, we discuss the need for multi-center and out-of-distribution testing to address the current limitations of the methods to reduce the cancer burden and improve patient care
Self-solvation energies: extended open database and GNN-based prediction
No full textSolvation energies play a crucial role in various chemical processes, ranging from chemical synthesis to separation techniques. To optimize these processes, it is essential to accurately predict solvation energies across different temperatures and solvents. However, most existing studies primarily focus on the standard temperature of 298.15 K. In this work, we address this limitation by creating an extensive database, which combines the DIPPR and Yaws databases. Our comprehensive dataset includes 5420 pure compounds, resulting in 71,656 data points spanning a wide range of temperatures. Additionally, besides the development of this novel database, another key contribution of this work is the coupling of the well-known Graph Convolutional Neural Network Chemprop, with our database with the aim of predicting self-solvation energies across diverse temperatures for the first time. The results presented here demonstrate the overall effectiveness of the model, evidenced by a low Mean Absolute Error (MAE) of 0.09 kcal mol−1 and a high Determination Coefficient (R²) of 0.992. Additionally, the Average Relative Deviation (ARD) of the data is 2.2 %, further confirming the accuracy of the model. In fact, the model demonstrates robust predictive performance across data of varying quality, including a significant fraction of pseudo-experimental values derived from predictive schemes. However, it is worth noting that some groups of compounds, such as small sized compounds and low-numbered ring structures, exhibited somewhat larger deviations than expected. This suggests areas for further refinement and indicates that while the model is robust, there is still room for improvement in specific cases. This approach represents an overall improvement over previous models and offers enhanced versatility for practical applications in chemical synthesis and separation processes
Social enterprise referents: how social enterprises help organize nascent fields to address complex societal problems
No full textAddressing societal challenges requires engaging diverse actors, but clashes between social and commercial interests often hinder coordination. In established fields, conflicting social interests can be integrated by challenging dominant commercial positions and rallying powerful actors. However, creating new fields without established actors and coordination mechanisms is more complex, especially when interests conflict. We explore this challenge through the development of reusable containers for takeaway food and beverages, where incompatible perspectives initially led to a field impasse. A pioneering social enterprise blending commercial and social interests emerged as a referent, facilitating collaboration and breaking the impasse. After initial field organizing succeeded, regulatory changes and increased demand exposed the shortcomings of early solutions, leading to setbacks. New social enterprises developed solutions to fill supply–demand gaps, anchoring new models in a market and driving both standardization and innovation. We introduce the concept of ‘social enterprise referents’ to highlight their essential role in organizing nascent fields to address complex societal issues. Without these referents, models for building new fields struggle to take hold. Successfully transitioning from an underorganized to an organized field requires sustained efforts from multiple social enterprise referents to anchor solutions in a market and uphold collaboration with field actors
Results of the JaCo project: fatigue strength of robot-welded tubular joints for offshore wind energy converters
No full textJacket foundations are lattice-like structures, whose assembly requires the welding of a large number of tubular joints. Such foundations type is suitable to support wind energy converters in deeper water with large turbine size. In order to increase the production speed and its quality, robot systems were developed to produce tubular joints. As fatigue is dominating the design of these structures, an assessment of the performance of tubular joints produced by four different robots was performed and compared with the performance of manually welded joints. In total, 18 large-scale tests were performed on joints with dimensions representative for offshore structures, which were produced in industrial environment. Almost all breakthrough cracks occurred through the chord, with cracks initiated at the weld toe, although in some cases cracks were also initiated and propagated between weld beads. Strain measurements have demonstrated that when multiple cracks are present in one specimen, they interact only in the last phase of the fatigue life, when they are so large that they affect the stiffness of the tubular components. The measured fatigue strengths of joints produced by robot were similar or higher than the T-curve of DNV-RP-C203. Two fabricators delivered components of which the fatigue strength was more than 20% higher than the standard curve. These results emphasize that mastering the welding process with robots is necessary to achieve superior levels of fatigue strength
Development of Global Seabound Mobility
No full textThe importance of maritime shipping for world trade is paramount. The globalization of supply chains is only made possible to its current extent by shipping and can therefore be considered one of the most important transport systems in the world. In the future, the transport of goods is forecast to triple by 2050 compared to 2015. At the same time, due to its size, global maritime shipping today accounts for approx. 3% of global greenhouse gas (GHG) emissions. With the adoption of the “Strategy to Reduce Greenhouse Gas Emissions from Ships,” the International Maritime Organization (IMO) has set a milestone in that its stated goal is to reduce GHG emissions from shipping to net zero emissions from 2008 levels by 2050. In addition to the efforts of the IMO, other political entities such as the European Union (EU) and interest groups from the shipping industry have taken measures that significantly underpin the declared goals. To achieve this, the transition of using conventional fuels consisting of heavy fuel oils (HFO) to climate-neutral and sustainable fuels must be undertaken. In this article, alternative fuels are considered and assessed in terms of their potential and availability to contribute to GHG reduction in the maritime sector. The article concludes with an analysis of the current state of renewable and low carbon fossil fuels in shipping and gives a forecast of how their distribution and development may look in the future
Information geometry of the Otto metric
No full textWe introduce the dual of the mixture connection with respect to the Otto metric which represents a new kind of exponential connection. This provides a dual structure consisting of the mixture connection, the Otto metric as a Riemannian metric, and the new exponential connection. We derive the geodesic equation of this exponential connection, which coincides with the Kolmogorov forward equation of a gradient flow. We then derive the canonical contrast function of the introduced dual structure
Novel concepts for metal hydride storage tanks – numerical modeling, simulation and evaluation
No full textThe efficient, space-saving and safe storage of hydrogen is a major challenge that needs to be overcome for enabling renewable energy systems. Metal hydrides are a possible solution. But the key challenge is the identification and development of the most promising metal hydride material as well as the ideal tank design for an efficient hydrogen absorption / desorption in terms of energy demand / storage losses and loading / unloading time. Against this background this paper aims to identify suitable combinations of medium and low-temperature metal hydride materials in combination with three different tank design concepts. The goal is to determine which material fits best for each combination and could thus be a suitable solution for a future implementation in stationary and mobile applications of metal hydride storage tanks. To achieve this goal a finite element method (FEM) modeling and simulation of materials and construction designs in COMSOL Multiphysics is realized. The results are analyzed in terms of hydrogen absorption rate, temperature profile over time, and the necessary energy demand for the overall storage process. The results show that for the low-temperature metal hydride investigated here, the tank design is of subordinate importance, allowing for more application-specific design. For medium-temperature metal hydrides, the investigated construction concepts show heterogeneous results. For fast hydrogen absorption and minimal external heating time, the suggested rectangular tank design might be a promising option, requiring only 28% / 29% of the heating energy of the cylindrical concepts. If the goal is to achieve the most complete hydrogen absorption, the base design concept investigated here, consisting of a cylindrical tank with metal hydride material rolled up in a spiral, is the most favorable solution; achieving a hydrogen loading of about 3.6 wt–% for the medium-temperature metal hydride. The low-temperature metal hydride achieves a total hydrogen absorption of around 1.4 wt-% in the optimum concept. For concepts with higher operating temperatures, preheating the storage tank before feeding in the hydrogen could improve the absorption process (only examined here)
Digital-twin-based management of sewer systems: research strategy for the KaSyTwin project
No full textSewer infrastructure is vital for flood prevention, environmental protection, and public health. As part of sewer infrastructure, sewer systems are prone to degradation. Traditional maintenance methods for sewer systems are largely manual and reactive and rely on inconsistent data, leading to inefficient maintenance. The KaSyTwin research project addresses the urgent need for efficient and resilient sewer system management methods in Germany, aiming to develop a methodology for the semi-automated development and utilization of digital twins of sewer systems to enhance data availability and operational resilience. Using advanced multi-sensor robotic platforms equipped with scanning and imaging systems, i.e., laser scanners and cameras, as well as artificial intelligence (AI), the KaSyTwin research project focuses on generating digital twin-enabled representations of sewer systems in real time. As a project report, this work outlines the research framework and proposed methodologies in the KaSyTwin research project. Digital twins of sewer systems integrated with AI technologies are expected to facilitate proactive maintenance, resilience forecasting against extreme weather events, and real-time damage detection. Furthermore, the KaSyTwin research project aspires to advance the digital management of sewer systems, ensuring long-term functionality and public welfare via on-demand structural health monitoring and non-destructive testing