We employ accurate density functional theory
calculations to examine the electronic structure of three
Ni/SrO2 nanostructures containing single-layer, bilayer
and four-layer Ni nanosheets. The single Ni layer
interacts strongly with the topmost oxygen layer at the
Ni/SrO2 interface, resulting in significant surface
reconstruction and strong hybridisation between the O
2p and Ni 3d states. For the bilayer Ni, the layer facing
the interface also strongly interacts with the O. However,
the second layer retains its geometry. For the four-layer
system, none of the Ni layers interacted strongly with O.
According to the electronic population analysis, in the
thinnest nanosheets, the strong hybridisation with
oxygen pulls Niâ€™s 3d states away from the Fermi level
deeper into the valence band. In these cases, Niâ€™s
electronic population that is labile for catalysis in the
vicinity of the Fermi level was as little as half of the
bilayer nanosheet. Such reduction in the labile 3d
population has a detrimental effect on Niâ€™s catalytic
performance in de-hydrogenating formic acid. Our
results demonstrate that there is an optimum dimension
for Ni nanoparticles below or above which the catalytic
performance deteriorates. Consequently, reducing the
Ni dimension to maximise the surface in the hope of
better catalytic yield might not be the best strategy as
detrimental p-d hybridisation takes hold. Smaller might
not always be better after all!
The Solid Oxide Fuel Cell (SOFC) will play a crucial role in the future energy sector for green and efficient H2-fueled applications. However, the complex thermal dynamic characteristics and safety performances of SOFC/GT systems introduce significant computational challenges to design systems utilising SOFCs. A wind/P2G/SOFC/GT multi-energy system structure is presented in the paper to demonstrate integrated energy systems that achieve optimal technical and economic performance. To address the design challenge, artificial intelligence technology offers the promise of constructing an accurate SOFC model using a minimal amount of experimental data, thereby alleviating computational demands and accelerating calculation times. In this study, we have developed an ensemble learning model designed to capture the thermodynamic and safety performances of SOFC/GT systems. This approach can accelerate calculations while ensuring the validity of optimisation results.
The new energy vehicles (NEV) production in China has accounts for over 65% of the global sales. However, the unbalanced development between NEV and charging facility has brought new challenge. This essay explores domestic charging facility industry, analyzes the effects of NEV industry and charging facility on carbon emission and finally predicts the technology trends by collecting and analyzing the relative data from 2011 to September 2023. The results show that the NEV and charging facility development have made obvious progress in declining carbon emission. The article also provides suggestions and guidelines for the future development of this industry.
Advancing the transition to renewable energy necessitates significant investments, especially in energy storage solutions to mitigate the variability and intermittency of renewable electricity generation. Rock-based Thermal Energy Storage (RTES) offers the vital adaptability needed to incorporate significant amounts of renewable energy by harnessing excessive energy and storing it in rocks or geological formations. This stored energy can then be used for various applications like heating or generating electricity, providing a reliable and sustainable energy source. One of the main challenges in the widespread use of RTES is the thermal losses (mainly because of the diffusion of thermocline) and the high pressure drop in the packed beds. Hence, a novel RTES system is proposed to overcome these limitations. The design has a perforated plate that delivers air in a certain length from the inlet. The fluid flow and heat transfer inside the packed bed are studied using a 2D Computational Fluid Dynamics (CFD) model considering the local thermal non-equilibrium (LTNE) at the air-rock interface. For the optimal case (perforated length to bed length of 0.7), the proposed design is shown to increase the charging efficiency of the conventional RTES by 14 percent by decreasing the fan power requirement and increasing the stored thermal energy. This improvement is due to the emergence of a secondary thermocline that bypasses the main thermocline, and hence, utilizes the packed bed more efficiently while also reducing the fan power requirement.
Integrated energy management system (IEMS) is a must-have to ensure the operation of integrated energy systems (IES) and to provide foundation to advanced functions such as analysis, forecast and optimization. However, building an IEMS faces significant challenges, including the absence of unified data models across diversified disciplines, the dynamic and heterogeneous nature of IEMS, budget constraints for small-scale IESs, and the need for interdisciplinary cooperation. Previous research focused on proposing unified data model standards for each subfield of IES or particular IES projects. However, such approach inevitably struggles with the difficulties in covering vast and diverse topics encompassed by IES and the adoption in engineering practice. This research pivots away from attempting to create another data model standard but proposes a collaborative and software-aided method to foster community-driven data model and data integration. The method includes three key components: an IES data model framework, an IEMS data connector, and an operation strategy. The proposed method minimizes semantic ambiguity, translates human semantics to machine executions automatically, streamlines application interface connections, and fosters the development of a de-facto data model standard within the IES community. The method has been verified through a case study and theoretical criteria, offering a promising avenue for seamless data integration in IEMS.
The link between urban contextual form and building energy use has been attracting attention in the field recently. Most studies measure urban form using a set of parameters, which are then used to explain building energy use variation. Typically, the results obtained are explained with a speculation on different mechanisms and their balances. However, such an approach does not quantify the contributions of individual mechanisms. This study uses mediation models to examine urban contextual form’s influence on building energy use, using building solar insolation, context temperature, and context urban vitality as mediators. Summer electricity, winter electricity, summer gas, and winter gas use intensities are analyzed. The results show that mediators significantly influence various contextual variables, and their impact varies across seasons and types of energy use. These mediators can strengthen or undermine each other, influencing the net impact of contextual form. Thus, urban form energy efficiency interventions need to consider those mechanisms and their effects, considering the specific period and type of energy use.
Quick water breakthrough, rapid water cut rise, poor water flooding efficiency in single layer is common problem in most of thin interbed reservoirs. Finer injection-production strategy should be developed to avoid serious water channeling and ineffective water cycle. To narrow this gap, this work presents a threedimensional intelligent equilibrium displacement model (3D-IEDM) to optimize water flooding in thin interbed reservoirs. The implementation in pilot B36 well group test of PL oilfield indicate that the optimization velocity of 3D-IEDM can optimize the vertical water injection profile of thin interbed reservoirs, and improve the sweep efficiency, and the length of time is approximately 14 times less than conventional simulator-based methods. Compared with the conventional injection-production scheme, the initial productivity of pilot well group using 3D-IEDM increases by 6.45%, and the utilization factor of water injection improves by 15%.
Under the background of carbon neutrality, the energy-saving design of high-rise buildings has received widespread attention. And the differences in energy consumption in various parts of high-rise buildings have become an obstacle to energy-saving design. The edge effects is one of the main causes of the differences in energy consumption. The existing research lacks accurate quantitative assessment of edge effects, making it is difficult to guide the energy-saving design of high-rise buildings. Therefore, a modeling method for studying the distribution of building energy consumption is proposed, the edge effects energy consumption prediction model of building space is established by parametric technology, and CFD and energy simulation technology are used to carry out experiments. We conduct in-depth discussions on the influence areas of edge effects on different heights of plate and tower high-rise buildings, as well as the impact intensity of edge effects on energy consumption. In addition, an optimization strategy to reduce the impact of edge effects on the energy consumption of high-rise buildings is proposed.
The increase in CO2 emissions has led to a series of environmental problems, including global warming, making it imperative to reduce CO2 emissions. Adsorption carbon capture technologies have been widely researched, but there is currently a lack of comprehensive research on the optimization of cyclic performance that considers multiple objectives. This paper focuses on temperature swing adsorption (TSA) and develops algorithms for cyclic performance optimization. It employs machine learning techniques to conduct multi-objective optimization of the cycle. The calculation time for the surrogate model is only 1/1000 of the TSA mathematical model. The results indicate that the surrogate model obtained through machine learning accurately represents the cyclic performance under different operating parameters. There is a competitive relationship between productivity and exergy efficiency throughout the cycle. Recovery rate and exergy efficiency exhibit a dual relationship, both competitive and positively correlated. Purity and recovery rate show a purely positive relationship.