Investigation of energy-efficient heat pump assisted heterogeneous azeotropic distillation for separating of acetonitrile/ethyl acetate/n-hexane mixture

The conventional distillation is hard to accomplish the separation of acetonitrile/ethyl acetate/n-hexane mixture. Herein, a heterogeneous azeotropic distillation(HAD) without adding entrainer is proposed to separate ternary mixture. The proposed scheme is optimized via the simulated annealing algorithm and minimum total annual cost(TAC) is used as objective functions. To minimize energy consumption,heat pump is added on the basis of optimal heterogeneous azeotropic distillation and heat integration technology is used to further improve the energy recovery. The TAC, gas emission, energy consumption and exergy destruction are used to discuss the economy and environmental protection of processes.Among all the processes, the heat pump with higher preheating temperature(HPT) assisted HAD process by combining with heat integration(HAD-HPT-HI) has best performances on economic, environment,energy and exergy. Compared with conventional HAD process, the HAD-HPT-HI achieves the reductions of 52.17%, 68.86%, 65.87% and 65.46% on TAC, total energy consumption, gas emissions and exergy destruction, respectively.

Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing

To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.

Energy-efficient virtual machine consolidation algorithm in cloud data centers

Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine(VM) consolidation algorithm named PVDE(prediction-based VM deployment algorithm for energy efficiency) is presented. The proposed algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center, based on the predicted host load, into four classes for the purpose of VMs migration. We also propose four types of VM selection algorithms for the purpose of determining potential VMs to be migrated. We performed extensive performance analysis of the proposed algorithms. Experimental results show that, in contrast to other energy-saving algorithms, the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement(SLA) violations.

Energy-Efficient Approaches for a Machine Tool Building in a University through Field Measurement and Energy Modelling

The heating,ventilating,and air conditioning(HVAC)system consumes nearly 50%of the building’s energy,especially in Taiwan with a hot and humid climate.Due to the challenges in obtaining energy sources and the negative impacts of excessive energy use on the environment,it is essential to employ an energy-efficient HVAC system.This study conducted the machine tools building in a university.The field measurement was carried out,and the data were used to conduct energymodelling with EnergyPlus(EP)in order to discover some improvements in energy-efficient design.The validation between fieldmeasurement and energymodelling was performed,and the error rate was less than 10%.The following strategies were proposed in this study based on several energy-efficient approaches,including room temperature settings,chilled water supply temperature settings,chiller coefficient of performance(COP),shading,and building location.Energy-efficient approaches have been evaluated and could reduce energy consumption annually.The results reveal that the proposed energy-efficient approaches of room temperature settings(3.8%),chilled water supply temperature settings(2.1%),chiller COP(5.9%),using shading(9.1%),and building location(3.0%),respectively,could reduce energy consumption.The analysis discovered that using a well-performing HVAC system and building shading were effective in lowering the amount of energy used,and the energy modelling method could be an effective and satisfactory tool in determining potential energy savings.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

Energy-Efficient Low-Complexity Algorithm in 5G Massive MIMO Systems

Energy efficiency(EE)is a critical design when taking into account circuit power consumption(CPC)in fifth-generation cellular networks.These problems arise because of the increasing number of antennas in massive multiple-input multiple-output(MIMO)systems,attributable to inter-cell interference for channel state information.Apart from that,a higher number of radio frequency(RF)chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers.Therefore,antenna selection,user selection,optimal transmission power,and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems.This work aims to investigate joint antenna selection,optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE,with complete knowledge of large-scale fading with maximum ratio transmission.It also accounts for channel estimation and eliminating pilot contamination as antennas M→∞.This formulates the optimization problem of joint optimal antenna selection,transmits power allocation and joint user selection to mitigate inter-cellinterference in downlink multi-cell massive MIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm(LCA)for Newton’s methods and Lagrange multipliers.To analyze the precise power consumption,a novel power consumption scheme is proposed for each individual antenna,based on the transmit power amplifier and CPC.Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power,in the case the noise power is less than the received power pilot.The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse,in the case the transmit power allocationρd=40 dBm,and the optimal EE=71.232 Mb/j.

An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical resources.By grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the organization.Nevertheless,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions negatively.In this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based IoT.Three consecutive segments are suggested.For the covering of adjusted clusters,an ideal number of clusters is determined first.Then,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor hubs.Cluster heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster members.Specifically,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long time.The assessment results demonstrate that the IEPA performs better than existing conventions.

An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings.Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks(WSN)is one of the most difficult areas of study.As every sensor node has afinite amount of energy.Battery power is the most significant source in the WSN.Clustering is a well-known technique for enhan-cing the power feature in WSN.In the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing opti-mization.By using distributed data transmission modification,an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area.To begin,a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy con-sumption.The Multi-Swarm optimization(MSO)based Genetic Algorithms are proposed to select an efficient Cluster Head(CH).It also improves the network’s longevity and optimizes the routing.As a result of the study’sfindings,the pro-posed MSO-Genetic Algorithm with Hill climbing(GAHC)is effective,as it increases the number of clusters created,average energy expended,lifespan com-putation reduces average packet loss,and end-to-end delay.

EA-DFPSO:An intelligent energy-efficient scheduling algorithm for mobile edge networks

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile terminals.Mobile edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission delays.Compute nodes are usually distributed in edge environments,enabling crucially efficient task scheduling among those nodes to achieve reduced processing time.Moreover,it is imperative to conserve edge server energy,enhancing their lifetimes.To this end,this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization(EA-DFPSO)that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time.The proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for tasks.Extensive experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.

Algorithm Selection Method Based on Coupling Strength for Partitioned Analysis of Structure-Piezoelectric-Circuit Coupling

In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis ofstructure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectriccoupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulationsare used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely(1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weaklycoupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) a partially stronglycoupled and partially weakly coupled algorithm that uses an implicit formulation and an explicit formulation forthe two types of coupling, respectively.Numerical examples using a piezoelectric energy harvester,which is a typicalstructure-piezoelectric-circuit coupling problem, demonstrate that the proposed method selects the most costeffectivealgorithm.

Hybrid Optimization Algorithm for Handwritten Document Enhancement

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.

Multi-Objective Optimization of VBHF in Deep Drawing Based on the Improved QO-Jaya Algorithm

Blank holder force(BHF)is a crucial parameter in deep drawing,having close relation with the forming quality of sheet metal.However,there are different BHFs maintaining the best forming effect in different stages of deep drawing.The variable blank holder force(VBHF)varying with the drawing stage can overcome this problem at an extent.The optimization of VBHF is to determine the optimal BHF in every deep drawing stage.In this paper,a new heuristic optimization algorithm named Jaya is introduced to solve the optimization efficiently.An improved“Quasi-oppositional”strategy is added to Jaya algorithm for improving population diversity.Meanwhile,an innovated stop criterion is added for better convergence.Firstly,the quality evaluation criteria for wrinkling and tearing are built.Secondly,the Kriging models are developed to approximate and quantify the relation between VBHF and forming defects under random sampling.Finally,the optimization models are established and solved by the improved QO-Jaya algorithm.A VBHF optimization example of component with complicated shape and thin wall is studied to prove the effectiveness of the improved Jaya algorithm.The optimization results are compared with that obtained by other algorithms based on the TOPSIS method.

Synergistic Swarm Optimization Algorithm

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

An Optimal Node Localization in WSN Based on Siege Whale Optimization Algorithm

Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave environments.FFO preferentially selects the best-performing individuals.This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area.To address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search capability.Secondly,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality.Finally,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions.The results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

Research on Total Electric Field PredictionMethod of Ultra-High Voltage Direct Current Transmission Line Based on Stacking Algorithm

Ultra-high voltage(UHV)transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment.The ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power grid.Yet,the accurate prediction of the ground total electric field remains a technical challenge.In this work,we collected the total electric field data from the Ningdong-Zhejiang±800 kV UHVDC transmission project,as of the Ling Shao line,and perform an outlier analysis of the total electric field data.We show that the Local Outlier Factor(LOF)elimination algorithm has a small average difference and overcomes the performance of Density-Based Spatial Clustering of Applications with Noise(DBSCAN)and Isolated Forest elimination algorithms.Moreover,the Stacking algorithm has been found to have superior prediction accuracy than a variety of similar prediction algorithms,including the traditional finite element.The low prediction error of the Stacking algorithm highlights the superior ability to accurately forecast the ground total electric field of UHVDC transmission lines.