Investigating the impact of dynamic structural changes of Au/rutile catalysts on the catalytic activity of CO oxidation

The surface properties of oxidic supports and their interaction with the supported metals play critical roles in governing the catalytic activities of oxide‐supported metal catalysts.When metals are supported on reducible oxides,dynamic surface reconstruction phenomena,including strong metal–support interaction(SMSI)and oxygen vacancy formation,complicate the determination of the structural–functional relationship at the active sites.Here,we performed a systematic investigation of the dynamic behavior of Au nanocatalysts supported on flame‐synthesized TiO_(2),which takes predominantly a rutile phase,using CO oxidation above room temperature as a probe reaction.Our analysis conclusively elucidated a negative correlation between the catalytic activity of Au/TiO_(2) and the oxygen vacancy at the Au/TiO_(2) interface.Although the reversible formation and retracting of SMSI overlayers have been ubiquitously observed on Au/TiO_(2) samples,the catalytic consequence of SMSI remains inconclusive.Density functional theory suggests that the electron transfer from TiO_(2) to Au is correlated to the presence of the interfacial oxygen vacancies,retarding the catalytic activation of CO oxidation.

热泵在中国实现2060年碳中和目标中的作用被低估径

1.The crucial role of heating decarbonization in achieving carbon neutrality in China by 2060,The decarbonization of heating,in both buildings and industries,presents a major challenge and opportunity for China if the nation is going to meet its 2060 commitment to carbon neutrality.Currently,as shown in Fig.1(a)[1],the buildings and industrial sectors share the largest proportion(more than 70%)of end-use energy demand in China.In the buildings sector,heating accounts for half of the energy demand[2];in the industrial sector,50%-70%of the energy demand is for process heating[3].As summarized in Fig.1(b)[3],heating demand in the buildings sector usually requires temperatures below 80℃;in different areas of the industrial sector—including but not limited to distillation,drying,and dyeing—the heating demand ranges across various temperatures that are mainly lower than 170℃.On average,more than 40%of industrial heat consumption falls below 150℃[4].

Dynamic control of district heating networks with integrated emission modelling:A dynamic knowledge graph approach

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.

Dynamic knowledge graph approach for modelling the decarbonisation of power

This paper presents a dynamic knowledge graph approach that offers a reusable,interoperable,and extensible framework for modelling power systems.Domain ontologies have been developed to support a linked data representation of infrastructure data,socio-demographic data,areal attributes like demand,and models describing power systems.The knowledge graph links the data with a hierarchical representation of administrative regions,supporting geospatial queries to retrieve information about the population within the vicinity of a power plant,the number of power plants,total generation capacity,and demand within specific areas.Computational agents were developed to operate on the knowledge graph.The agents performed tasks including data uploading,updating,retrieval,processing,model construction and scenario analysis.A derived information framework was used to track the provenance of information calculated by agents involved in each scenario.The knowledge graph was populated with data describing the UK power system.Two alternative models of the transmission grid with different levels of structural resolution were instantiated,providing the foundation for the power system simulation and optimisation tasks performed by the agents.The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.

Optimal Placement of Phasor Measurement Unit in Smart Grids Considering Multiple Constraints

The distribution of measurement noise is usually assumed to be Gaussian in the optimal phasor measurement unit(PMU)placement(OPP)problem.However,this is not always accurate in practice.This paper proposes a new OPP method for smart grids in which the effects of conventional measurements,limited channels of PMUs,zero-injection buses(ZIBs),single PMU loss contingency,state estimation error(SEE),and the maximum SEE variance(MSEEV)are considered.The SEE and MSEEV are both obtained using a robust t-distribution maximum likelihood estimator(MLE)because t-distribution is more flexible for modeling both Gaussian and non-Gaussian noises.The A-and G-optimal experimental criteria are utilized to form the SEE and MSEEV constraints.This allows the optimization problem to be converted into a linear objective function subject to linear matrix inequality observability constraints.The performance of the proposed OPP method is verified by the simulations of the IEEE 14-bus,30-bus,and 118-bus systems as well as the 211-bus practical distribution system in China.

Semantic 3D City Agents- An intelligent automation for dynamic geospatial knowledge graphs

This paper presents a system of autonomous intelligent software agents,based on a cognitive architecture,capable of automated instantiation,visualisation and analysis of multifaceted City Information Models in dynamic geospatial knowledge graphs.Design of JPS Agent Framework and Routed Knowledge Graph Access components was required in order to provide backbone infrastructure for an intelligent agent system as well as technology agnostic knowledge graph access enabling automation of multi-domain data interoperability.Development of CityImportAgent,CityExportAgent and DistanceAgent showcased intelligent automation capa-bilities of the Cities Knowledge Graph.The agents successfully created a semantic model of Berlin in LOD 2,compliant with CityGML 2.0 standard and consisting of 419909661 triples described using OntoCityGML.The system of agents also visualised and analysed the model by autonomously tracking interactions with a web interface as well as enriched the model by adding new information to the knowledge graph.This way it was possible to design a geospatial information system able to meet demands imposed by the Industry 4.0 and link it with the other multi-domain knowledge representations of The World Avatar.

Semantic 3D City Database—An enabler for a dynamic geospatial knowledge graph

This paper presents a dynamic geospatial knowledge graph as part of The World Avatar project,with an underlying ontology based on CityGML 2.0 for three-dimensional geometrical city objects.We comprehensively evaluated,repaired and refined an existing CityGML ontology to produce an improved version that could pass the necessary tests and complete unit test development.A corresponding data transformation tool,originally designed to work alongside CityGML,was extended.This allowed for the transformation of original data into a form of semantic triples.We compared various scalable technologies for this semantic data storage and chose Blazegraph™as it provided the required geospatial search functionality.We also evaluated scalable hardware data solutions and file systems using the publicly available CityGML 2.0 data of Charlottenburg in Berlin,Germany as a working example.The structural isomorphism of the CityGML schemas and the OntoCityGML Tbox allowed the data to be transformed without loss of information.Efficient geospatial search algorithms allowed us to retrieve building data from any point in a city using coordinates.The use of named graphs and namespaces for data partitioning ensured the system performance stayed well below its capacity limits.This was achieved by evaluating scalable and dedicated data storage hardware capable of hosting expansible file systems,which strengthened the architectural foundations of the target system.

OntoPowSys: A power system ontology for cross domain interactions in an eco industrial park

Knowledge management in multi-domain,heterogeneous industrial networks like an Eco-Industrial Park(EIP)is a challenging task.In this paper,an ontology-based management system has been proposed for addressing this challenge.It focuses on the power systems domain and provides a framework for integrating this knowledge with the other domains of an EIP.The proposed ontology,OntoPowSys is expressed using a Description Logics(DL)syntax and the OWL2 language was used to make it alive.It is then used as a part of the Knowledge Management System(KMS)in a virtual EIP called the J-Park Simulator(JPS).The advantages of the proposed approach are demonstrated by conducting two case studies on the JPS.The first case study illustrates the application of optimal power flow(OPF)in the electrical network of the JPS.The second case study plays an important role in under-standing the cross-domain interactions between the chemical and electrical engineering domains in a bio-diesel plant of the JPS.These case studies are available as web services on the JPS website.The results showcase the advantages of using ontologies in the development of decision support tools.These tools are capable of taking into account contextual information on top of data during their decision-making processes.They are also able to exchange knowledge across different domains without the need for a communication interface.