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Oriented conversion of low-value heavy oil to acetylene and hydrogen using pulsed spark discharge
No full textDischarge plasma presents a promising approach for converting heavy oil into light hydrocarbons under room temperature and atmospheric pressure. Among various techniques, repetitive pulsed discharge plasma is preferred for its potential to improve energy efficiency, however the decomposition characteristics and gaseous production pathways of long-chain hydrocarbons remain insufficiently understood. In this study, the decomposed products and process of mineral transformer oil under repetitively pulsed spark discharge in the liquid phase are analyzed by gas chromatography (GC) and optical emission spectroscopy (OES). Experiment results reveal that the proportion of H2 gas production decreases, while that of C2H2 increases with the prolonged reaction time. Besides, the production of C2H2 is significantly higher at a pulse frequency of 1000 Hz compared to 10 Hz. OES analysis further shows that the electron density decreases as the repetitive pulse frequency increases, a trend that contrasts with prior observations of heavy oil cracking in gas and liquid-gas reaction systems. Despite the reduced energy per pulse at higher frequencies, the total number of breakdown events over the same reaction duration is larger, contributing to enhanced reaction outcomes. These experimental findings are confirmed by plasma kinetics and molecular dynamics simulation, which identified a continuous dehydrogenation process involving H radical reactions with C2H4 as the primary pathway for C2H2 and H2 production. The study demonstrates the feasibility of C2-oriented conversion through the decomposition of heavy oil decomposition under spark discharge, with adjustments to repetitive pulse parameters offering a promising avenue for optimization
A comparative study of a Backward Euler and exponential update for a viscoelastic Maxwell Model within the material point method
No full textControl of covalent bond enables efficient magnetic cooling
No full textMagnetic cooling, harnessing the temperature change in matter when exposed to a magnetic field, presents an energy‐efficient and climate‐friendly alternative to traditional vapor‐compression refrigeration systems, with a significantly lower global warming potential. The advancement of this technology would be accelerated if irreversible losses arising from hysteresis in magnetocaloric materials are minimized. Despite extensive efforts to manipulate crystal lattice constants at the unit‐cell level, mitigating hysteresis often compromises cooling performance. Herein, we address this persistent challenge by forming Sn(Ge) 3 −Sn(Ge) 3 bonds within the unit cell of the Gd 5 Ge 4 compound. This approach enables an energetically favorable phase transition, leading to the elimination of thermal hysteresis. Consequently, we achieve a synergistic improvement of two key magnetocaloric figures of merit: a larger magnetic entropy change and a twofold increase in the reversible adiabatic temperature change (from 3.8 to 8 K) in the Gd 5 Sn 2 Ge 2 compound. Such synergies can be extended over a wide temperature range of 40–160 K. This study demonstrates a paradigm shift in mastering hysteresis toward simultaneously achieving exceptional magnetocaloric metrics and opens up promising avenues for gas liquefaction applications in the longstanding pursuit of sustainable energy solutions
Humanize: Neurowissenschaftliche Messmethoden zu menschenzentrierten Innenstadtkonzepten
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The Impact of Combating Bribery and Corruption Report Assurance on Financial Analysts' Decisions, in: International Journal of Auditing
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Study on the load profile characteristics of machine tools in machining operations
No full textDue to rising costs and the need for a more sustainable use of resources, there is an increasing focus on energy use in industrial production. As a result, energy-related data, for example from machine tools, is increasingly being collected. In addition to information for the energy evaluation of individual systems and processes, load profiles of machine tools offer further opportunities for process monitoring, such as tracking of production lots. As sensors for electrical power monitoring can be retrofitted without interfering with the process or the machine control unit, load profiles offer a cost-effective data source for data mining and machine learning applications. In order to support the generalisability of such applications, this paper describes the load profiles of machine tools and presents an overview on characteristics and the variety of load profiles of turning, grinding and milling machines in industrial use cases. Load profiles of 18 machine tools from machinery, automotive and aerospace production were analysed with regard to statistical characteristics during machining cycles. In particular, typical value ranges and statistical figures of load profiles and the influence of the sampling rate on the time series are presented
A dataspace-driven edge computing and federated learning framework for sensory tooling systems
No full textAlthough manufacturing becomes increasingly data-driven, sensor data is typically available only locally, with limited integration across systems or organizations. This lack of available, heterogeneous data prevents the development of robust AI models to predict key machining outcomes such as tool wear and surface roughness. To overcome this challenge, a cooperative approach that leverages Dataspaces and Compute-to-Data is proposed. This enables access to heterogeneous data and the subsequent development of robust algorithms without sharing the actual data, thereby keeping know-how and intellectual property secure. Therfore, an edge computing architecture is proposed, integrating sensory tool holders with machine tools and enabling high-frequency measurements of acceleration during machining. These measurements are transmitted via Bluetooth from the tool to a stationary transceiver unit, labeled and subsequently stored on a local data storage. The data is then made accessible through a Dataspace. To prevent stakeholders from accessing raw data of each other, the AI model training is conducted in Compute-to-Data environments. The approach is illustrated through an exemplary case study, focusing on the prediction of surface roughness during machining. Thus, this work contributes to the cooperative creation of intelligent machine tools. Future research can build on this approach and explore more industrialized implementations