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Biogenic and anthropogenic feedstocks can be converted into high-quality and hydrogen-rich synthesis gas through high-pressure entrained flow gasification. However, robust pilot-scale process data is essential for the optimisation, design and scale-up of this process. Therefore, this study conducted pilot-scale experiments, developed balancing and equilibrium models for performance analysis and derived input and validation data for CFD models. The experiments were carried out at the bioliqEntrained Flow Gasifier plant using mixtures of ethylene glycol or beech wood pyrolysis oil with glass beads, thermal inputs of up to 5 MW and operating pressures of 40 bar. The cooling screen was recoated before the experiments to ensure well-defined heat transfer conditions. The data from on-line measurements and off-line analyses was evaluated with emphasis on the synthesis gas condition before quenching, the heat extraction from the inner reactor chamber and the carbon conversion. The results show that the balancing model provides consistent and accurate predictions and the equilibrium model is able to track the generated process data. Specifically, the balancing predictions are accurate if the solution of CO$_2$ in the quench water is accounted for, if undetected intermediates are described as lost carbon and lost atomic hydrogen and if further chemical reactions in the quench water are avoided by appropriate operating conditions.

Hydrogen is an energy carrier that can support the development of sustainable and flexible energy systems. However, decarbonization can occur when green sources are used for energy production and appropriate water use is manifested. This work aims to propose a socio-economic analysis of hydrogen production from an integrated wind and electrolysis plant in southern Italy. The estimated production amounts to about 1.8 million kg and the LCOH is calculated to be 3.60 €/kg in the base scenario. Analyses of the alternative scenarios allow us to observe that with a high probability the value ranges between 3.20–4.00 €/kg and that the capacity factor is the factor that most affects the economic results. Social analysis, conducted through an online survey, shows a strong knowledge gap as only 27.5 % claim to know the difference between green and grey hydrogen. There is a slight propensity to install systems near their homes, but this tends to increase due to increased knowledge on the topic. Respondents state sustainable behaviours, and this study suggests that these aspects should also be transformed into the energy choices that are implemented every day. The study suggests information to policy-makers, businesses and citizens as it outlines that green hydrogen is an operations strategy that moves toward sustainable development.

Anion exchange membrane water electrolysis (AEMWE) holds the potential to become a key technology for future hydrogen production. In the present study, the influence of contact pressure on hydrogen crossover and polarization behavior is systematically investigated in a range from 0.5 MPa to 2.5 MPa in 0.5 MPa increments. The electrodes were prepared as catalyst-coated substrate (CCS), applying 3 mg cm−2 NiFe2O4 on the anode substrate and 0.5 mg cm−2 Pt on the cathode substrate. It is demonstrated that an elevated contact pressure results in a decreased high frequency resistance (R HF), while simultaneously leading to a significantly increased hydrogen content on the anode side. At 3 A cm−2 the ohmic resistance decreases by approx. 30 mΩ cm2 when increasing the contact pressure from 0.5 MPa to 2.5 MPa, whereas the anodic hydrogen content increased by approx. 1.5 vol.% respectively. Additionally, it can be observed that the selection of the gas diffusion layer (GDL) material has a strong effect on hydrogen crossover, while the influence on cell voltage is insignificant. Overall, these results show a promising starting point for further investigations on the interactions between cathode properties, cell compression and anodic gas contamination.

Aim:: This article describes the use of graphite(Gr) and boron carbide (B4C) as multiple nanoparticle reinforcements in LM25 aluminum alloy. Because boron carbide naturally absorbs neutron radiation, aluminium alloy reinforced with boron carbide metal matrix composite has gained interest in nuclear shielding applications. The primary goal of the endeavor is to create composite materials with high wear resistance, high microhardness, and high ultimate tensile strength for use in nuclear applications. Patents on Gr and B4C can cover a wide range of subjects, including the synthesis and production methods of structural, armor materials, abrasives, and nuclear shielding. Background:: Science and Technology have brought a vast change to human life. The human burden has been minimized by the use of innovation in developing new and innovative technologies. To improve the quality of human life, fresh, lightweight, and creative materials are being used, which play a vital role in science and technology and reduce the human workload. Composite materials made of metal are being used because they are lightweight. Neutron absorption, high ultimate strength, high wear resistance, high microhardness, high thermal and electrical conductivity, high vacuum environmental resistance, and low coefficient of thermal expansion under static and dynamic conditions are all demands for the hybrid metal matrix composites utilized in nuclear applications. Objective:: Stir casting is used to create the novel LM 25 aluminum alloy/graphite and boron carbide hybrid nanocomposites. • The mechanical properties such as ultimate tensile strength, yield strength, percentage of elongation, microhardness, and wear behavior are calculated. • Three analyses are performed: microstructure, worn surface analysis, and fracture analysis of the tensile specimen. Method:: • Stir casting process • Tensile, Hardness, Wear Test • Materials Characterization – FESEM, Optical Microscopy, EDS Results:: The mechanical properties values are 308.76 MPa, 293.51 MPa, 7.8, 169.2 VHN, and 0.01854mm3/m intended for ultimate tensile strength, yield strength, percentage of elongation, microhardness, and wear behavior, respectively. This implies that the synthesized composite may be used in nuclear applications successfully Conclusion:: The subsequent explanation was drawn from this investigative work: • The LM 25/B4C/Gr hybrid nanocomposite was successfully manufactured by employing the stir casting technique. For nuclear shielding applications, these composites were prepared with three different weight percentages of nanoparticle reinforcements in 2,4,6% Boron carbide and constant 4 wt.% graphite. • The microhardness values of the three-hybrid nanocomposite fabricated castings were determined to be 143.4VHN, 156.7VHN, and 169.2VHN, respectively. • The hybrid nano composite's microstructure revealed that the underlying LM 25 aluminum alloy matrix's finegrained, evenly dispersed nanoparticles of graphite and boron carbide were present. • The microtensile test was carried out and it was found that the ultimate tensile strength, yield strength and percentage of elongation values are 281.35MPa, 296.52MPa, 308.76MPa, 269.43, 274.69, 293.51 and 3.4, 5.7, 7.8 respectively. • Deformation caused the hybrid LM 25/B4C/Gr nanocomposite to fracture in ductile mode. Dimples and cavities are seen in the fracture because of the nanoparticle reinforcements and the matrix's tight connection. • The wear loss of nanocomposite based on the input parameter applied load, sliding velocity and sliding distance values are 0.02456, 0.02189, 0.01854, 0.02892, 0.02586, 0.02315 and 0.02682, 0.02254, 0.02015 mm3/m, respectively. • The LM 25 alloy's elemental analysis displays the aluminum alloy phase as the largest peak and the remaining elements as smaller peaks; also, the spectral analysis reveals the presence of boron (B), graphite (C), silicon, and ferrous in the aluminum alloy LM 25. • Through worn surface FESEM investigation, it was shown that under sliding and high load situations, debris, delamination, and groove develop. Further rupture, fine, and continuous grooves were seen when low stress and sliding circumstances were applied to the LM 25/B4C/Gr and stir cast specimen. This result implies the presence of mild adhesive and delamination wear processes.

Dislocations were introduced into BaTiO3 single crystals and become catalytically active centers.

The end of Moore’s law and Dennard scaling emphasizes the need for application-specific computing architectures to achieve high resource and energy efficiency and real-time performance. The concept of a silicon compiler remains an enduring aspiration for design time reduction. In order to generate hardware implementations at register transfer level from behavioral descriptions, design automation tools must address challenging and interdependent problems, including allocation, scheduling, and binding. Additionally, manual intervention by the user is necessary to balance the resources vs. performance tradeoff via, for example, function inlining or loop unrolling/pipelining. Existing approaches typically solve these problems sequentially, compromising optimality in favor of simplicity and runtime. Here we show how to model the whole model-based design flow as one holistic integer linear programming (ILP) formulation aiming at consistently deriving the optimal microarchitecture for any given application. Incorporating clock gating minimizes the number of useless operations with negligible resource overhead (if any), while always guaranteeing optimal throughput. The unified nature of the proposed ILP model enables implementations unmatched by state-of-the-art approaches in terms of resource efficiency and measured power consumption. These results facilitate a streamlined design flow for highly optimized embedded systems in the context of model-based design.

Batteries can effectively improve the security of energy systems and mitigate climate change by facilitating wind and solar power. The installed capacity of battery energy storage system (BESS), mainly the lithium ion batteries are increasing significantly in recent years. However, the battery degradation cannot be accurately quantified and integrated into energy management system with existing heuristic battery degradation models. This paper proposed a hierarchical deep learning based battery degradation quantification (HDL-BDQ) model to quantify the battery degradation given scheduled BESS daily operations. Particularly, two sequential and cohesive deep neural networks are proposed to accurately estimate the degree of degradation using inputs of battery operational profiles and it can significantly outperform existing fixed or linear rate based degradation models as well as single-stage deep neural models. Training results show the high accuracy of the proposed system. Moreover, a learning and optimization decoupled algorithm is implemented to strategically take advantage of the proposed HDL-BDQ model in optimization-based look-ahead scheduling (LAS) problems. Case studies demonstrate the effectiveness of the proposed HDL-BDQ model in LAS of a microgrid testbed. 12 pages

The recently completed Horizon-2020 project “Management and Uncertainties of Severe Accidents (MUSA)” has reviewed uncertainty sources and Uncertainty Quantification methodology for assessing Severe Accidents (SA), and has made a substantial effort at stimulating uncertainty applications in predicting the radiological Source Term of reactor and Spent Fuel Pool accident scenarios. The key motivation of the project has been to bring the advantages of the Best Estimate Plus Uncertainty approach to the field of Severe Accident modelling. With respect to deterministic analyses, expected gains are avoiding adopting conservative assumptions, identifying uncertainty bands of estimates, and gaining insights into dominating uncertain parameters. Also, the benefits for understanding and improving Accident Management were to be explored. The reactor applications brought together a large group of participants that set out to apply uncertainty analysis (UA) within their field of SA modelling expertise – in particular reactor types, but also SA code used (ASTEC, MELCOR, MAAP, RELAP/SCDAPSIM), uncertainty quantification tools used (DAKOTA, SUSA, URANIE, self-developed tools based on Python code), detailed accident scenarios, and in some cases SAM actions. The setting up of the analyses, challenges faced during that phase, and solutions explored, are described in Brumm et al. ANE 191 (2023). This paper synthesizes the reactor-application work at the end of the project. Analyses of 23 partners are presented in different categories, depending on whether their main goal is/are (i) uncertainty bands of simulation results; (ii) the understanding of dominating uncertainties in specific sub-models of the SA code; (iii) improving the understanding of specific accident scenarios, with or without the application of SAM actions; or, (iv) a demonstration of the tools used and developed, and of the capability to carry out an uncertainty analysis in the presence of the challenges faced. A cross-section of the partners’ results is presented and briefly discussed, to provide an overview of the work done, and to encourage accessing and studying the project deliverables that are open to the public. Furthermore, the partners’ experiences made during the project have been evaluated and are presented as good practice recommendations. The paper ends with conclusions on the level of readiness of UA in SA modelling, on the determination of governing uncertainties, and on the analysis of SAM actions.