Fraction repetition(FR)codes are integral in distributed storage systems(DSS)with exact repair-by-transfer,while pliable fraction repetition codes are vital for DSSs in which both the per-node storage and repetition d...Fraction repetition(FR)codes are integral in distributed storage systems(DSS)with exact repair-by-transfer,while pliable fraction repetition codes are vital for DSSs in which both the per-node storage and repetition degree can easily be adjusted simultaneously.This paper introduces a new type of pliable FR codes,called absolute balanced pliable FR(ABPFR)codes,in which the access balancing in DSS is considered.Additionally,the equivalence between pliable FR codes and resolvable transversal packings in combinatorial design theory is presented.Then constructions of pliable FR codes and ABPFR codes based on resolvable transversal packings are presented.展开更多
ASEAN’s major power balancing strategy refers to the balancing strategy adopted by ASEAN and its member states to seek national and regional security and development by maintaining multi-faceted friendship and impart...ASEAN’s major power balancing strategy refers to the balancing strategy adopted by ASEAN and its member states to seek national and regional security and development by maintaining multi-faceted friendship and impartiality with surrounding major powers.The evolution of this strategy is a process of dynamic adjustment,with ASEAN and its members being the implementing subjects,major powers the objects.展开更多
Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstructio...Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstruction of Germany’s balancing group mechanism(BGM).Building on this foundation,this research pioneers the integration of virtual power plants(VPPs)with the BGM in the Chinese context to overcome the limitations of traditional single-entity regulation models in flexibility provision and economic efficiency.A balancing responsibility framework centered on VPPs is innovatively proposed and a regional multi-entity collaboration and bi-level responsibility transfer architecture is constructed.This architecture enables cross-layer coordinated optimization of regional system costs and VPP revenues.The upper layer minimizes regional operational costs,whereas the lower layer enhances the operational revenues of VPPs through dynamic gaming between deviation regulation service income and penalty costs.Compared with traditional centralized regulation models,the proposed method reduces system operational costs by 29.1%in typical regional cases and increases VPP revenues by 24.9%.These results validate its dual optimization of system economics and participant incentives through market mechanisms,providing a replicable theoretical paradigm and practical pathway for designing balancing mechanisms in new power systems.展开更多
The Internet of Things(IoT)and allied applications have made real-time responsiveness for massive devices over the Internet essential.Cloud-edge/fog ensembles handle such applications'computations.For Beyond 5 th ...The Internet of Things(IoT)and allied applications have made real-time responsiveness for massive devices over the Internet essential.Cloud-edge/fog ensembles handle such applications'computations.For Beyond 5 th Generation(B5G)communication paradigms,Edge Servers(ESs)must be placed within Information Communication Technology infrastructures to meet Quality of Service requirements like response time and resource utilisation.Due to the large number of Base Stations(BSs)and ESs and the possibility of significant variations in placing the ESs within the IoTs geographical expanse for optimising multiple objectives,the Edge Server Placement Problem(ESPP)is NP-hard.Thus,stochastic evolutionary metaheuristics are natural.This work addresses the ESPP using a Particle Swarm Optimization that initialises particles as BS positions within the geography to maintain the workload while scanning through all feasible sets of BSs as an encoded sequence.The Workload-Threshold Aware Sequence Encoding(WTASE)Scheme for ESPP provides the number of ESs to be deployed,similar to existing methodologies and exact locations for their placements without the overhead of maintaining a prohibitively large distance matrix.Simulation tests using open-source datasets show that the suggested technique improves ESs utilisation rate,workload balance,and average energy consumption by 36%,17%,and 32%,respectively,compared to prior works.展开更多
That herbs with the"hot"property used to treat"cold"syndromes is a guiding principle of clinical prescription and medication in traditional Chinese medicine(TCM).However,this theory of TCM is still...That herbs with the"hot"property used to treat"cold"syndromes is a guiding principle of clinical prescription and medication in traditional Chinese medicine(TCM).However,this theory of TCM is still in the‘black box'stage,and few in-depth studies have examined the biological mechanisms underpinning the hot properties of herbs.展开更多
The existence of the aeroengine casing,limited monitoring points,and multi-fault characteristics make obtaining the rotor’s vibration transmission characteristics challenging,resulting in difficulties accurately iden...The existence of the aeroengine casing,limited monitoring points,and multi-fault characteristics make obtaining the rotor’s vibration transmission characteristics challenging,resulting in difficulties accurately identifying the rotor unbalance.This paper utilizes a high-frequency composite sensor to monitor the engine’s blade tip clearance(BTC)and extracts unbalanced information from BTC signals for rotor dynamic balancing,while avoiding the need for the once per revolution(OPR)sensor.First,the vibration characteristics of the rotor-blade system under multi-fault conditions are investigated.Then,based on BTC measurement,a none OPR method and an unbalance identification method are proposed,in which the radial vibration of the blade tip in the BTC signals at different speeds is extracted and operated in the time domain to obtain the rotor unbalanced vibration,the signal is reconstructed,and cross-correlation analysis is used to accurately identify the magnitude and phase of the unbalanced signal.Finally,a rotor test bench is utilized for experimental verification.The results reveal that the dynamic balancing method based on the BTC signal can more precisely identify the rotor unbalance than the traditional rotor dynamic balancing method.The application of this technique will effectively improve engine health management and fault prediction.展开更多
In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overl...In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overlooking congestion issues on feeder links caused by the limited number and centralized distribution of ground stations.Hence,a multi-service routing algorithm called the Multi-service Load Bal-ancing Routing Algorithm for Traffic Return(MLB-TR)is proposed.Unlike traditional approaches,MLB-TR aims to achieve a broader and more comprehensive load balancing objective.Specifically,based on the service type,an appropriate landing satellite is first selected by considering factors such as shortest path hop count and satellite load.Then,a set of candidate paths from the source satellite to the selected landing satellite is computed.Finally,using the regional load balancing index as the optimization objective,the final transmission path is selected from the candidate path set.Simulation results show that the proposed algo-rithm outperforms the existing works.展开更多
In deep drilling applications,such as those for geothermal energy,there are many challenges,such as those related to efficient operation of the drilling fluid(mud)pumping system.Legacy drilling rigs often use paired,p...In deep drilling applications,such as those for geothermal energy,there are many challenges,such as those related to efficient operation of the drilling fluid(mud)pumping system.Legacy drilling rigs often use paired,parallel-connected independent-excitation direct-current(DC)motors for mud pumps,that are supplied by a single power converter.This configuration results in electrical power imbalance,thus reducing its efficiency.This paper investigates this power imbalance issue in such legacy DC mud pump drive systems and offers an innovative solution in the form of a closed-loop control system for electrical load balancing.The paper first analyzes the drilling fluid circulation and electrical drive layout to develop an analytical model that can be used for electrical load balancing and related energy efficiency improvements.Based on this analysis,a feedback control system(so-called“current mirror”control system)is designed to balance the electrical load(i.e.,armature currents)of parallel-connected DC machines by adjusting the excitation current of one of the DC machines,thus mitigating the power imbalance of the electrical drive.Theproposed control systemeffectiveness has been validated,first through simulations,followed by experimental testing on a deep drilling rig during commissioning and field tests.The results demonstrate the practical viability of the proposed“current mirror”control system that can effectively and rather quickly equalize the armature currents of both DC machines in a parallel-connected electrical drive,and thus balance both the electrical and mechanical load of individual DC machines under realistic operating conditions of the mud pump electrical drive.展开更多
The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is n...The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.展开更多
The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand.Modeling dynamics in the balancing market can provide valuable insights and prognosis for p...The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand.Modeling dynamics in the balancing market can provide valuable insights and prognosis for power grid stability and secure energy supply.While complex machine learning models can achieve high accuracy,their“blackbox”nature severely limits the model interpretability.In this paper,we explore the trade-off between model accuracy and interpretability for the energy balancing market.Particularly,we take the example of forecasting manual frequency restoration reserve(mFRR)activation price in the balancing market using real market data from different energy price zones.We explore the interpretability of mFRR forecasting using two models:extreme gradient boosting(XGBoost)machine and explainable boosting machine(EBM).We also integrate the two models,and we benchmark all the models against a baseline naive model.Our results show that EBM provides forecasting accuracy comparable to XGBoost while yielding a considerable level of interpretability.Our analysis also underscores the challenge of accurately predicting the mFRR price for the instances when the activation price deviates significantly from the spot price.Importantly,EBM's interpretability features reveal insights into non-linear mFRR price drivers and regional market dynamics.Our study demonstrates that EBM is a viable and valuable interpretable alternative to complex black-box AI models in the forecast for the balancing market.展开更多
Hierarchical Ni/ZSM-22-SBA-15 meso-microporous catalysts(Ni/ZS-x)with different acid properties and diffusion characteristics(acid-diffusion)properties were synthesized successfully and applied to the production of hi...Hierarchical Ni/ZSM-22-SBA-15 meso-microporous catalysts(Ni/ZS-x)with different acid properties and diffusion characteristics(acid-diffusion)properties were synthesized successfully and applied to the production of high-quality jet fuel by the efficient one-step hydrogenation(hydrodeoxygenation,isomerization,and cracking)of oleic acid.The acid-diffusion properties of the catalysts are modulated by tuning the ZSM-22 seed content,and their effects on the hydrogenation reactions were investigated.Acid properties affect the extent of isomerization and cleavage reactions,whereas diffusion properties affect the accessibility of active centers.The balanced acid-diffusion properties are conducive to efficient hydrogenation reactions of oleic acid.The optimal Ni/ZS-3 exhibits the highest jet fuel yield(56.3%,340°C)and superior iso/n-alkane ratio(i/n=3.12)because of its well-balanced acid-diffusion properties.Besides,the possible hydrogenation mechanism of oleic acid is proposed.展开更多
In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the...In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the networks presents uneven distribution.To achieve energy balance in networks,an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor(DEF)is proposed.The DEF is defined as the function of the inter-satellite link dis-tance and the cumulative network energy consumption ratio.According to the minimum sum of DEF on inter-satellite links,an energy consumption balancing algorithm based on DEF is pro-posed,which can realize dynamic traffic transmission optimiza-tion of multiple traffic services.It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network,make full use of idle nodes with low energy consumption,and optimize the energy consumption dis-tribution of the whole network according to the continuous itera-tions of each traffic service flow.Simulation results show that,compared with the traditional shortest path algorithm,the pro-posed method can improve the balancing performance of nodes by 75%under certain traffic pressure,and realize the optimiza-tion of energy consumption balancing of the whole network.展开更多
In response to the deficiencies of commonly used optimization methods for assembly lines,a production demand-oriented optimization method for assembly lines is proposed.Taking a certain compressor assembly line as an ...In response to the deficiencies of commonly used optimization methods for assembly lines,a production demand-oriented optimization method for assembly lines is proposed.Taking a certain compressor assembly line as an example,the production rhythm and the number of workstations are calculated based on production requirements and working systems.With assembly rhythm and smoothing index as optimization goals,an improved particle swarm optimization algorithm is employed for process allocation.Subsequently,Flexsim simulation is used to analyze the assembly line.The final results show that after optimization using the improved particle swarm algorithm,the assembly line balance rate increased from 71.1%to 85.9%,and the assembly line smoothing index decreased from 47.4 to 29.8,significantly enhancing assembly efficiency.This demonstrates the effectiveness of the proposed optimization method for the assembly line and provides a reference for other products in the same industry.展开更多
基金Supported in part by the National Key R&D Program of China(No.2020YFA0712300)NSFC(No.61872353)。
文摘Fraction repetition(FR)codes are integral in distributed storage systems(DSS)with exact repair-by-transfer,while pliable fraction repetition codes are vital for DSSs in which both the per-node storage and repetition degree can easily be adjusted simultaneously.This paper introduces a new type of pliable FR codes,called absolute balanced pliable FR(ABPFR)codes,in which the access balancing in DSS is considered.Additionally,the equivalence between pliable FR codes and resolvable transversal packings in combinatorial design theory is presented.Then constructions of pliable FR codes and ABPFR codes based on resolvable transversal packings are presented.
文摘ASEAN’s major power balancing strategy refers to the balancing strategy adopted by ASEAN and its member states to seek national and regional security and development by maintaining multi-faceted friendship and impartiality with surrounding major powers.The evolution of this strategy is a process of dynamic adjustment,with ASEAN and its members being the implementing subjects,major powers the objects.
基金supported by the National Natural Science Foundation of China(no.72471087)Beijing Nova Program(no.20250484853)+1 种基金Beijing Natural Science Foundation(no.9242015)National Social Science Foundation of China(no.24&ZD111).
文摘Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstruction of Germany’s balancing group mechanism(BGM).Building on this foundation,this research pioneers the integration of virtual power plants(VPPs)with the BGM in the Chinese context to overcome the limitations of traditional single-entity regulation models in flexibility provision and economic efficiency.A balancing responsibility framework centered on VPPs is innovatively proposed and a regional multi-entity collaboration and bi-level responsibility transfer architecture is constructed.This architecture enables cross-layer coordinated optimization of regional system costs and VPP revenues.The upper layer minimizes regional operational costs,whereas the lower layer enhances the operational revenues of VPPs through dynamic gaming between deviation regulation service income and penalty costs.Compared with traditional centralized regulation models,the proposed method reduces system operational costs by 29.1%in typical regional cases and increases VPP revenues by 24.9%.These results validate its dual optimization of system economics and participant incentives through market mechanisms,providing a replicable theoretical paradigm and practical pathway for designing balancing mechanisms in new power systems.
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Research Project under grant number RGP2/603/46。
文摘The Internet of Things(IoT)and allied applications have made real-time responsiveness for massive devices over the Internet essential.Cloud-edge/fog ensembles handle such applications'computations.For Beyond 5 th Generation(B5G)communication paradigms,Edge Servers(ESs)must be placed within Information Communication Technology infrastructures to meet Quality of Service requirements like response time and resource utilisation.Due to the large number of Base Stations(BSs)and ESs and the possibility of significant variations in placing the ESs within the IoTs geographical expanse for optimising multiple objectives,the Edge Server Placement Problem(ESPP)is NP-hard.Thus,stochastic evolutionary metaheuristics are natural.This work addresses the ESPP using a Particle Swarm Optimization that initialises particles as BS positions within the geography to maintain the workload while scanning through all feasible sets of BSs as an encoded sequence.The Workload-Threshold Aware Sequence Encoding(WTASE)Scheme for ESPP provides the number of ESs to be deployed,similar to existing methodologies and exact locations for their placements without the overhead of maintaining a prohibitively large distance matrix.Simulation tests using open-source datasets show that the suggested technique improves ESs utilisation rate,workload balance,and average energy consumption by 36%,17%,and 32%,respectively,compared to prior works.
基金supported by the Chief Scientist of Qi-Huang Project of the National Traditional Chinese Medicine Inheritance and Innovation“One Hundred Million”Talent Project,China(Grant No.:[2021]No.7)the National Famous Old Traditional Chinese Medicine Experts Inheritance Studio Construction Program of National Administration of Traditional Chinese Medicine,China(Grant No.:[2022]No.75)+3 种基金the Seventh Batch of National Famous Old Traditional Chinese Medicine Experts Experience Heritage Construction Program of National Administration of Traditional Chinese Medicine,China(Grant No.:[2022]No.76)Heilongjiang Touyan Innovation Team Program,China(Grant No.:[2019]No.5)the Natural Science Foundation of Zhejiang Province(Grant No.:LQN25H280009)the Research Project of Zhejiang Chinese Medical University,China(Grant No.:2023RCZXZK22).
文摘That herbs with the"hot"property used to treat"cold"syndromes is a guiding principle of clinical prescription and medication in traditional Chinese medicine(TCM).However,this theory of TCM is still in the‘black box'stage,and few in-depth studies have examined the biological mechanisms underpinning the hot properties of herbs.
基金supported by the Key Program of National Natural Science Foundation of China(No.92160203)National Natural Science Foundation of China(No.92360306).
文摘The existence of the aeroengine casing,limited monitoring points,and multi-fault characteristics make obtaining the rotor’s vibration transmission characteristics challenging,resulting in difficulties accurately identifying the rotor unbalance.This paper utilizes a high-frequency composite sensor to monitor the engine’s blade tip clearance(BTC)and extracts unbalanced information from BTC signals for rotor dynamic balancing,while avoiding the need for the once per revolution(OPR)sensor.First,the vibration characteristics of the rotor-blade system under multi-fault conditions are investigated.Then,based on BTC measurement,a none OPR method and an unbalance identification method are proposed,in which the radial vibration of the blade tip in the BTC signals at different speeds is extracted and operated in the time domain to obtain the rotor unbalanced vibration,the signal is reconstructed,and cross-correlation analysis is used to accurately identify the magnitude and phase of the unbalanced signal.Finally,a rotor test bench is utilized for experimental verification.The results reveal that the dynamic balancing method based on the BTC signal can more precisely identify the rotor unbalance than the traditional rotor dynamic balancing method.The application of this technique will effectively improve engine health management and fault prediction.
基金supported by the National Key Research and Development Program of China under Grant No.2022YFB2902501the Fundamental Research Funds for the Central Universities under Grant No.2023ZCJH09the Haidian District Golden Bridge Seed Fund of Beijing Municipality under Grant No.S2024161.
文摘In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overlooking congestion issues on feeder links caused by the limited number and centralized distribution of ground stations.Hence,a multi-service routing algorithm called the Multi-service Load Bal-ancing Routing Algorithm for Traffic Return(MLB-TR)is proposed.Unlike traditional approaches,MLB-TR aims to achieve a broader and more comprehensive load balancing objective.Specifically,based on the service type,an appropriate landing satellite is first selected by considering factors such as shortest path hop count and satellite load.Then,a set of candidate paths from the source satellite to the selected landing satellite is computed.Finally,using the regional load balancing index as the optimization objective,the final transmission path is selected from the candidate path set.Simulation results show that the proposed algo-rithm outperforms the existing works.
文摘In deep drilling applications,such as those for geothermal energy,there are many challenges,such as those related to efficient operation of the drilling fluid(mud)pumping system.Legacy drilling rigs often use paired,parallel-connected independent-excitation direct-current(DC)motors for mud pumps,that are supplied by a single power converter.This configuration results in electrical power imbalance,thus reducing its efficiency.This paper investigates this power imbalance issue in such legacy DC mud pump drive systems and offers an innovative solution in the form of a closed-loop control system for electrical load balancing.The paper first analyzes the drilling fluid circulation and electrical drive layout to develop an analytical model that can be used for electrical load balancing and related energy efficiency improvements.Based on this analysis,a feedback control system(so-called“current mirror”control system)is designed to balance the electrical load(i.e.,armature currents)of parallel-connected DC machines by adjusting the excitation current of one of the DC machines,thus mitigating the power imbalance of the electrical drive.Theproposed control systemeffectiveness has been validated,first through simulations,followed by experimental testing on a deep drilling rig during commissioning and field tests.The results demonstrate the practical viability of the proposed“current mirror”control system that can effectively and rather quickly equalize the armature currents of both DC machines in a parallel-connected electrical drive,and thus balance both the electrical and mechanical load of individual DC machines under realistic operating conditions of the mud pump electrical drive.
文摘The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.
基金PriTEM project funded by UiO:Energy Convergence Environments
文摘The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand.Modeling dynamics in the balancing market can provide valuable insights and prognosis for power grid stability and secure energy supply.While complex machine learning models can achieve high accuracy,their“blackbox”nature severely limits the model interpretability.In this paper,we explore the trade-off between model accuracy and interpretability for the energy balancing market.Particularly,we take the example of forecasting manual frequency restoration reserve(mFRR)activation price in the balancing market using real market data from different energy price zones.We explore the interpretability of mFRR forecasting using two models:extreme gradient boosting(XGBoost)machine and explainable boosting machine(EBM).We also integrate the two models,and we benchmark all the models against a baseline naive model.Our results show that EBM provides forecasting accuracy comparable to XGBoost while yielding a considerable level of interpretability.Our analysis also underscores the challenge of accurately predicting the mFRR price for the instances when the activation price deviates significantly from the spot price.Importantly,EBM's interpretability features reveal insights into non-linear mFRR price drivers and regional market dynamics.Our study demonstrates that EBM is a viable and valuable interpretable alternative to complex black-box AI models in the forecast for the balancing market.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.22308381 and 22522818)Science Foundation of China University of Petroleum-Beijing(Grant Nos.2462023QNXZ002 and 2462023QNXZ005)+1 种基金Beijing Nova Program(Grant No.20220484096)the National Key R&D Program of China(Grant No.2021YFA1501201).
文摘Hierarchical Ni/ZSM-22-SBA-15 meso-microporous catalysts(Ni/ZS-x)with different acid properties and diffusion characteristics(acid-diffusion)properties were synthesized successfully and applied to the production of high-quality jet fuel by the efficient one-step hydrogenation(hydrodeoxygenation,isomerization,and cracking)of oleic acid.The acid-diffusion properties of the catalysts are modulated by tuning the ZSM-22 seed content,and their effects on the hydrogenation reactions were investigated.Acid properties affect the extent of isomerization and cleavage reactions,whereas diffusion properties affect the accessibility of active centers.The balanced acid-diffusion properties are conducive to efficient hydrogenation reactions of oleic acid.The optimal Ni/ZS-3 exhibits the highest jet fuel yield(56.3%,340°C)and superior iso/n-alkane ratio(i/n=3.12)because of its well-balanced acid-diffusion properties.Besides,the possible hydrogenation mechanism of oleic acid is proposed.
基金supported by the National Key Research and Development Program(2021YFB2900604).
文摘In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the networks presents uneven distribution.To achieve energy balance in networks,an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor(DEF)is proposed.The DEF is defined as the function of the inter-satellite link dis-tance and the cumulative network energy consumption ratio.According to the minimum sum of DEF on inter-satellite links,an energy consumption balancing algorithm based on DEF is pro-posed,which can realize dynamic traffic transmission optimiza-tion of multiple traffic services.It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network,make full use of idle nodes with low energy consumption,and optimize the energy consumption dis-tribution of the whole network according to the continuous itera-tions of each traffic service flow.Simulation results show that,compared with the traditional shortest path algorithm,the pro-posed method can improve the balancing performance of nodes by 75%under certain traffic pressure,and realize the optimiza-tion of energy consumption balancing of the whole network.
文摘In response to the deficiencies of commonly used optimization methods for assembly lines,a production demand-oriented optimization method for assembly lines is proposed.Taking a certain compressor assembly line as an example,the production rhythm and the number of workstations are calculated based on production requirements and working systems.With assembly rhythm and smoothing index as optimization goals,an improved particle swarm optimization algorithm is employed for process allocation.Subsequently,Flexsim simulation is used to analyze the assembly line.The final results show that after optimization using the improved particle swarm algorithm,the assembly line balance rate increased from 71.1%to 85.9%,and the assembly line smoothing index decreased from 47.4 to 29.8,significantly enhancing assembly efficiency.This demonstrates the effectiveness of the proposed optimization method for the assembly line and provides a reference for other products in the same industry.
