Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

Exploring the impact of economic growth and energy consumption on SO_(2) emissions in China based on the Environmental Kuznets Curve hypothesis

This study aims to analysis the influence of economic growth(EG)and energy consumption(EC)on sulfur dioxide emissions(SE)in China.Accordingly,this study explores the link between EG,EC,and SE for 30 provinces in China over the span of 2000-2019.This study also analyzes cross-sectional dependence tests,panel unit root tests,Westerlund panel cointegration tests,Dumitrescu-Hurlin(D-H)causality tests.According to the test results,there is an inverted U-shaped association between EG and SE,and the assumption of the Environmental Kuznets Curve(EKC)is verified.The signs of EG and EC in the fixed effect(FE)and random effect(RE)methods are in line with those in the dynamic ordinary least squares(DOLS),fully modified ordinary least squares(FMOLS)and autoregressive distributed lag(ARDL)estimators.Moreover,the results verified that EC can obviously positive impact the SE.To reduce SE in China,government and policymakers can improve air quality by developing cleaner energy sources and improving energy efficiency.This requires the comprehensive use of policies,regulations,economic incentives,and public participation to promote sustainable development.

Cloud-Model-Based Feature Engineering to Analyze the Energy-Water Nexus of a Full-Scale Wastewater Treatment Plant

Wastewater treatment plants(WWTPs)are important and energy-intensive municipal infrastructures.High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality.However,water-energy nexus analysis and models for WWTPs have rarely been reported to date.In this study,a cloud-model-based energy consumption analysis(CMECA)of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters.The principal component analysis(PCA)and K-means clustering were applied to classify the influent condition using water quality and volume data.The energy consumption of the WWTP is divided into five standard evaluation levels,and its cloud digital characteristics(CDCs)were extracted according to bilateral constraints and golden ratio methods.Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity.The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption,represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption.The local WWTP has high energy consumption with 0.3613 kW·h·m^(-3)despite low influent concentration and volumes,across four consumption levels from low(I)to relatively high(IV),showing an unsatisfactory operation and management level.The average oxygenation capacity,internal reflux ratio,and external reflux ratio during high energy efficiency days recognized by further clustering were obtained(0.2924-0.3703 kg O_(2)·m^(-3),1.9576-2.4787,and 0.6603-0.8361,respectively),which could be used as a guide for the days with low energy efficiency.Consequently,this study offers a water-energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.

Effects of connected automated vehicle on stability and energy consumption of heterogeneous traffic flow system

With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.

EECLP: AWireless Sensor Networks Energy Efficient Cross-Layer Protocol

Recent advancements in wireless communications have allowed the birth of novel wireless sensor networks(WSN).A sensor network comprises several micro-sensors deployed randomly in an area of interest.A micro-sensor is provided with an energy resource to supply electricity to all of its components.However,the disposed energy resource is limited and battery replacement is generally infeasible.With this restriction,the sensors must conserve energy to prolong their lifetime.Various energy conservation strategies for WSNs have been presented in the literature,from the application to the physical layer.Most of these solutions focus only on optimizing a single layer in terms of energy consumption.In this research,a novel cross-layer technique for WSNs’effective energy usage is presented.Because most energy consumption factors exist in the Medium Access Control(MAC)layer and network layer,our EECLP protocol(Energy Efficient Cross-Layer Protocol for Wireless Sensor Networks)integrates these two layers to satisfy energy efficiency criteria.To gain access to the transmission channel,we implement a communication regime at the MAC layer based on CSMA/CA(Carrier Sense Multiple Access/Collision Avoidance)techniques.Next,depending on the activity and a standby period,we employ the RTS/CTS(Request to Send/Clear to Send)method to prevent collisions and resolve hidden node concerns by utilizing the network allocation vector(NAV)to calculate the transmission duration.Employing a greedy strategy,we establish chains amongst cluster members to mitigate the issue of high energy consumption in routing data.An objective function was utilized to determine the optimal cross-chain path based on the distances to the base station(BS)and residual energy(RE).The simulation,testing,and comparison of the proposed protocol to peer protocols have shown superior outcomes and a prolonged network lifespan.Using the suggested protocol,the network lifetime increases by 10%compared to FAMACO(Fuzzy and Ant Colony Optimization based MAC/Routing Cross-layer)protocol,and it increases by 90%and 95%compared to IFUC(Improved Fuzzy Unequal Clustering)and UHEED(Unequal Hybrid Energy Efficient and Distributed)protocols successively.

Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks

Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.One such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely controlled.With the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power batteries.An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET.This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in FANET.The energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient nodes.Maintaining link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA.The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols.The evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.

Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus process.According to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious attacks.Additionally,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security.Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.

Deep Reinforcement Learning for Energy-Efficient Edge Caching in Mobile Edge Networks

Edge caching has emerged as a promising application paradigm in 5G networks,and by building edge networks to cache content,it can alleviate the traffic load brought about by the rapid growth of Internet of Things(IoT)services and applications.Due to the limitations of Edge Servers(ESs)and a large number of user demands,how to make the decision and utilize the resources of ESs are significant.In this paper,we aim to minimize the total system energy consumption in a heterogeneous network and formulate the content caching optimization problem as a Mixed Integer Non-Linear Programming(MINLP).To address the optimization problem,a Deep Q-Network(DQN)-based method is proposed to improve the overall performance of the system and reduce the backhaul traffic load.In addition,the DQN-based method can effectively solve the limitation of traditional reinforcement learning(RL)in complex scenarios.Simulation results show that the proposed DQN-based method can greatly outperform other benchmark methods,and significantly improve the cache hit rate and reduce the total system energy consumption in different scenarios.

NeTrainSim:a network-level simulator for modeling freight train longitudinal motion and energy consumption

Although train modeling research is vast, most available simulation tools are confined to city-or trip-scale analysis, primarily offering micro-level simulations of network segments. This paper addresses this void by developing the Ne Train Sim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. The main objective of this simulator is to enable a comprehensive analysis of energy consumption and the associated carbon footprint for the entire train system. Four case studies were conducted to demonstrate the simulator's performance. The first case study validates the model by comparing Ne Train Sim output to empirical trajectory data. The results demonstrate that the simulated trajectory is precise enough to estimate the train energy consumption and carbon dioxide emissions. The second application demonstrates the train-following model considering six trains following each other. The results showcase the model ability to maintain safefollowing distances between successive trains. The next study highlights the simulator's ability to resolve train conflicts for different scenarios. Finally, the suitability of the Ne Train Sim for modeling realistic railroad networks is verified through the modeling of the entire US network and comparing alternative powertrains on the fleet energy consumption.

Nano-Ni-Induced Electronic Modulation of MoS_(2) Nanosheets Enables Energy-Saving H_(2) Production and Sulfide Degradation

Electrocatalytic hydrogen evolution and sulfion(S^(2-))recycling are promising strategies for boosting H_(2)production and removing environmental pollutants.Here,a nano-Ni-functionalized molybdenum disulfide(MoS_(2))nanosheet was assembled on steel mesh(Ni-MoS_(2)/SM)for use in sulfide oxidation reaction-assisted,energy-saving H_(2)production.Experimental and theoretical calculation results revealed that anchoring nano-Ni on high-surface-area slack MoS_(2)nanosheets not only optimized catalyst adsorption of polysulfides but also played an important role in promoting hydrogen evolution reaction kinetics by absorbing OH_(ad),thereby greatly enhancing the catalytic performance toward sulfide oxidation reaction and hydrogen evolution reaction.Meanwhile,the Ni/MoS^(2-)based hydrogen evolution reaction+sulfide oxidation reaction system achieved nearly 100%hydrogen production efficiency and only consumed 61%less power per kWh than the oxygen evolution reaction+hydrogen evolution reaction system,which suggested our proposed Ni-MoS_(2)and novel hydrogen production system are promising for sustainable energy production.

A Two-Layer Active Power Optimization and Coordinated Control for Regional Power Grid Partitioning to Promote Distributed Renewable Energy Consumption

With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.

eQUEST Based Building Energy Modeling Analysis for Energy Efficiency of Buildings

Building energy performance is a function of numerous building parameters.In this study,sensitivity analysis on twenty parameters is performed to determine the top three parameters that have the most significant impact on the energy performance of buildings.Actual data from two fully operational commercial buildings were collected and used to develop a building energy model in the Quick Energy Simulation Tool(eQUEST).The model is calibrated using the Normalized Mean Bias Error(NMBE)and Coefficient of Variation of Root Mean Square Error(CV(RMSE))method.The model satisfies the NMBE and CV(RMSE)criteria set by the American Society of Heating,Refrigeration,and Air-Conditioning(ASHRAE)Guideline 14,Federal Energy Management Program(FEMP),and International Performance Measurement and Verification Protocol(IPMVP)for building energy model calibration.The values of the parameters are varied in two levels,and then the percentage change in output is calculated.Fractional factorial analysis on eight parameters with the highest percentage change in energy performance is performed at two levels in a statistical software JMP.For building A,the top 3 parameters from the percentage change method are:Heating setpoint,cooling setpoint and server room.From fractional factorial design,the top 3 parameters are:heating setpoint(p-value=0.00129),cooling setpoint(p-value=0.00133),and setback control(p-value=0.00317).For building B,the top 3 parameters from both methods are:Server room(pvalue=0.0000),heating setpoint(p-value=0.00014),and cooling setpoint(p-value=0.00035).If the best values for all top three parameters are taken simultaneously,energy efficiency improves by 29%for building A and 35%for building B.

Source-Load Coordinated Optimal Scheduling Considering the High Energy Load of Electrofused Magnesium and Wind Power Uncertainty

In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional units obviously can not solve the new energy as the main body of the scheduling problem.To enhance the systemscheduling ability,based on the participation of thermal power units,incorporate the high energy-carrying load of electro-melting magnesiuminto the regulation object,and consider the effects on the wind unpredictability of the power.Firstly,the operating characteristics of high energy load and wind power are analyzed,and the principle of the participation of electrofusedmagnesiumhigh energy-carrying loads in the elimination of obstructedwind power is studied.Second,a two-layer optimization model is suggested,with the objective function being the largest amount of wind power consumed and the lowest possible cost of system operation.In the upper model,the high energy-carrying load regulates the blocked wind power,and in the lower model,the second-order cone approximation algorithm is used to solve the optimizationmodelwithwind power uncertainty,so that a two-layer optimizationmodel that takes into account the regulation of the high energy-carrying load of the electrofused magnesium and the uncertainty of the wind power is established.Finally,the model is solved using Gurobi,and the results of the simulation demonstrate that the suggested model may successfully lower wind abandonment,lower system operation costs,increase the accuracy of day-ahead scheduling,and lower the final product error of the thermal electricity unit.

Optimising Energy Consumption in SD-DCN Networks (Software Defined-Data Center Network)

Over the last decade, the rapid growth in traffic and the number of network devices has implicitly led to an increase in network energy consumption. In this context, a new paradigm has emerged, Software-Defined Networking (SDN), which is an emerging technique that separates the control plane and the data plane of the deployed network, enabling centralized control of the network, while offering flexibility in data center network management. Some research work is moving in the direction of optimizing the energy consumption of SD-DCN, but still does not guarantee good performance and quality of service for SDN networks. To solve this problem, we propose a new mathematical model based on the principle of combinatorial optimization to dynamically solve the problem of activating and deactivating switches and unused links that consume energy in SDN networks while guaranteeing quality of service (QoS) and ensuring load balancing in the network.

QoS-Constrained,Reliable and Energy-Efficient Task Deployment in Cloud Computing

Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns.However,these three factors have intrinsic trade-off relationships.The existing studies show that load concentration can reduce the number of servers and hence save energy.In this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure.We propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and re-execution.For each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task replica.Each main task runs on an individual server.The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure.Different from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas.Specifically,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task failure.We conduct experiments through simulations.The experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.

Thermal comfort assessment and energy consumption analysis of ground-source heat pump system combined with radiant heating/cooling

A new ground source heat pump system combined with radiant heating/cooling is proposed, and the principles and the advantages of the system are analyzed. A demonstration of the system is applied to a rebuilt building： Xijindu exhibition hall, which is located in Zhenjiang city in China. Numerical studies on the thermal comfort and energy consumption of the system are carded out by using TRNSYS software. The results indicate that the system with the radiant floor method or the radiant ceiling method shows good thermal comfort without mechanical ventilation in winter. However, the system with either of the methods should add mechanical ventilation to ensure good comfort in summer. At the same level of thermal comfort, it can also be found that the annual energy consumption of the radiant ceiling system is less than that of the radiant floor system.

Energy-efficient Approach to Minimizing the Energy Consumption in An Extended Job-shop Scheduling Problem

The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production plarming and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed small- and large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.

Energy consumption in rock fragmentation at intermediate strain rate

In order to determine the relationship among energy consumption of rock and its fragmentation, dynamic strength and strain rate, granite, sandstone and limestone specimens were chosen and tested on large-diameter split Hopkinson pressure bar (SHPB) equipment with half-sine waveform loading at the strain rates ranging from 40 to 150 s- 1. With recorded signals, the energy consumption, strain rate and dynamic strength were analyzed. And the fragmentation behaviors of specimens were investigated. The experimental results show that the energy consumption density of rock increases linearly with the total incident energy. The energy consumption density is of an exponent relationship with the average size of rock fragments. The higher the energy consumption density, the more serious the fragmentation, and the better the gradation of fragments. The energy consumption density takes a good logarithm relationship with the dynamic strength of rock. The dynamic strength of rock increases with the increase of strain rate, indicating higher strain rate sensitivity.

Fragmentation Energy-Saving Theory of Full Face Rock Tunnel Boring Machine Disc Cutters

Attempts to minimize energy consumption of a tunnel boring machine disc cutter during the process of fragmentation have largely focused on optimizing disc- cutter spacing, as determined by the minimum specific energy required for fragmentation; however, indentation tests showed that rock deforms plastically beneath the cutters. Equations for thrust were developed for both the traditional, popularly employed disc cutter and anew design based on three-dimensional theory. The respective energy consumption for penetration, rolling, and side-slip fragmentations were obtained. A change in disc-cutter fragmentation angles resulted in a change in the nature of the interaction between the cutter and rock, which lowered the specific energy of fragmentation. During actual field excavations to the same penetration length, the combined energy consumption for fragmentation using the newly designed cutters was 15% lower than that when using the traditional design. This paper presents a theory for energy saving in tunnel boring machines. Investigation results showed that the disc cutters designed using this theory were more durable than traditional designs, and effectively lowered the energy consumption.

A review of China's energy consumption structure and outlook based on a long-range energy alternatives modeling tool

China＇s energy consumption experienced rapid growth over the past three decades, raising great concerns for the future adjustment of China＇s energy consumption structure. This paper first presents the historical evidence on China＇s energy consumption by the fuel types and sectors. Then, by establishing a bottom-up accounting framework and using long-range energy alternatives plan- ning energy modeling tool, the future of China＇s energy consumption structure under three scenarios is forecast. According to the estimates, China＇s total energy con- sumption will increase from 3014 million tonnes oil equivalent （Mtoe） in 2015 to 4470 Mtoe in 2040 under the current policies scenario, 4040 Mtoe in 2040 under the moderate policies scenario and 3320 Mtoe in 2040 under the strong policies scenario, respectively, lower than those of the IEA＇s estimations. In addition, the clean fuels （gas, nuclear and renewables） could be an effective alternative to the conventional fossil fuels （coal and oil） and offer much more potential. Furthermore, the industry sector has much strong reduction potentials than the other sectors. Finally, this paper suggests that the Chinese government should incorporate consideration of adjustment of the energy consumption structure into existing energy policies and measures in the future.