Elucidating the enhancement of kaolinite flotation by iron content through density functional theory: A study on sodium oleate adsorption efficiency

This study delves into the intricate relationship between iron(Fe)content in kaolinite and its impact on the adsorption behavior of sodium oleate.The effects of different iron concentrations on adsorption energy,hydrogen bond kinetics and adsorption efficiency were studied through simulation and experimental verification.The results show that the presence of iron in the kaolinite structure significantly improves the adsorption capacity of sodium oleate.Kaolinite samples with high iron content have better adsorption properties,lower adsorption energy levels and shorter and stronger hydrogen bonds than pure kaolinite.The optimal concentration of oleic acid ions for achieving maximum adsorption efficiency was identified as 1.2 mmol/L across different kaolinite samples.At this concentration,the adsorption rates and capacities reach their peak,with Fe-enriched kaolinite samples exhibiting notably higher flotation recovery rates.This optimal concentration represents a balance between sufficient oleic acid ion availability for surface interactions and the prevention of self-aggregation phenomena that could hinder adsorption.This study offers promising avenues for optimizing the flotation process in mineral processing applications.

A Loss-model-based Efficiency Optimization Control Method for Induction Traction System of High-speed Train under Emergency Self-propelled Mode

Increasing attention has been paid to the efficiency improvement of the induction traction system of high-speed trains due to the high demand for energy saving. In emergency self-propelled mode, however, the dc-link voltage and the traction power of the motor are significantly reduced, resulting in decreased traction efficiency due to the low load and low speed operations. Aiming to tackle this problem, a novel efficiency improved control method is introduced to the emergency mode of high-speed train traction system in this paper. In the proposed method, a total loss model of induction motor considering the behaviors of both iron and copper loss is established. An improved iterative algorithm with decreased computational burden is then introduced, resulting in a fast solving of the optimal flux reference for loss minimization at each control period. In addition, considering the parameter variation problem due to the low load and low speed operations, a parameter estimation method is integrated to improve the controller's robustness. The effectiveness of the proposed method on efficiency improvement at low voltage and low load conditions is demonstrated by simulated and experimental results.

A novel adaptive mutative scale optimization algorithm based on chaos genetic method and its optimization efficiency evaluation

By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.

Optimization of reluctance accelerator efficiency by an improved discharging circuit

In this paper,an improved discharging circuit was proposed to quicken the decay of the current in the drive coil in a reluctance accelerator when the armature reaches the center of the coil.The aim of this is to prevent the suck-back effect caused by the residual current in drive coil.The method is adding a reverse charging branch with a small capacitor in the traditional pulsed discharging circuit.The results under the traditional circuit and the improved circuit were compared in a simulation.The experiment then verified the simulations and they had good agreement.Simulation and experiment both demonstrated the improved circuit can effectively prevent the suck-back effect and increase the efficiency.At the voltage of 800 V,an efficiency increase of 36.34% was obtained.

System-L evel Efficiency Optimization of a Linear Induction Motor Drive System

Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and cut-open magnetic circuit,their efficiency and power factor are quite low,which limit their application in high power drive systems.To attempt this challenge,this work presents a system-level optimization method for a single-sided linear induction motor drive system.Not only the motor but also the control system is included in the analysis.A system-level optimization method is employed to gain optimal steady-state and dynamic performances.To validate the effectiveness of the proposed optimization method,experimental results on a linear induction motor drive are presented and discussed.

Efficiency analysis of numerical integrations for finite element substructure in real-time hybrid simulation

Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy, of numerical integrations in solving FE numerical substructure in RTHSs. First, sparse matrix storage schemes are adopted to decrease the computational time of FE numerical substructure. In this way, the task execution time（TET） decreases such that the scale of the numerical substructure model increases. Subsequently, several commonly used explicit numerical integration algorithms, including the central difference method（CDM）, the Newmark explicit method, the Chang method and the Gui-λ method, are comprehensively compared to evaluate their computational time in solving FE numerical substructure. CDM is better than the other explicit integration algorithms when the damping matrix is diagonal, while the Gui-λ（λ = 4） method is advantageous when the damping matrix is non-diagonal. Finally, the effect of time delay on the computational accuracy of RTHSs is investigated by simulating structure-foundation systems. Simulation results show that the influences of time delay on the displacement response become obvious with the mass ratio increasing, and delay compensation methods may reduce the relative error of the displacement peak value to less than 5% even under the large time-step and large time delay.

Study on maximum efficiency control strategy for induction motor

Two new techniques for efficiency-optimization control(EOC) of induction motor drives were proposed. The first method combined Loss Model and "golden section technique", which was faster than the available methods. Secondly, the low-frequency ripple torque due to decrease of rotor flux was compensated in a feedforward manner. If load torque or speed command changed, the efficiency search algorithm would be abandoned and the rated flux would be established to get the best transient response. The close agreement between the simulation and the experimental results confirmed the validity and usefulness of the proposed techniques.

Analysis on Irrational Decision Making of Tobacco Family Farmers from the Perspective of Optimal Scale of Efficiency

The larger the difference between the willingness scale of tobacco family farmers and the optimal scale of efficiency,the greater the degree of irrationality,and the higher the decision making risk.With the aid of DEA model,this study calculated the optimal scale of efficiency of Guiyang tobacco family farms.Using the ratio of willingness scale and efficiency optimal scale,it measured the degree of irrationality of family farmers.In addition,with the help of multiple linear regression model,it explained the irrational decision making mechanism of family farmers.Finally,it made a portrait of farmers who tend to make irrational decisions,to find specific farmers and guide them in their production and operation,reduce the risk of planting scale decision making and stabilize the sustainable development of the tobacco industry.

Optimal Size for Maximal Energy Efficiency in Information Processing of Biological Systems Due to Bistability

Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculate the mutual information, energy cost, and energy efficiency of an array of these bistable units. We find that the optimal number of units could maximize this array＇s energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.

A novel fractional uplink power control framework for self-organizing networks

Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interference,Fractional Uplink Power Control(FUPC)should be optimized from network-wide perspective,which has to find a better traffic distribution model.Conventionally,traffic distribution is geographic-based,and ineffective due to tricky locating efforts.This paper proposes a novel uplink power management framework for Self-Organizing Networks(SON),which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model.PathLoss-based Traffic Distribution(PLTD)aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells.Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40%compared to conventional GeoGraphic-based Traffic Distribution(GGTD).

Review of Design and Control Optimization of Axial Flux PMSM in Renewable-energy Applications

Axial flux permanent magnet synchronous motors(AFPMSMs)have been widely used in wind-power generation,electric vehicles,aircraft,and other renewable-energy applications owing to their high power density,operating efficiency,and integrability.To facilitate comprehensive research on AFPMSM,this article reviews the developments in the research on the design and control optimization of AFPMSMs.First,the basic topologies of AFPMSMs are introduced and classified.Second,the key points of the design optimization of core and coreless AFPMSMs are summarized from the aspects of parameter design,structure design,and material optimization.Third,because efficiency improvement is an issue that needs to be addressed when AFPMSMs are applied to electric or other vehicles,the development status of efficiency-optimization control strategies is reviewed.Moreover,control strategies proposed to suppress torque ripple caused by the small inductance of disc coreless permanent magnet synchronous motors(DCPMSMs)are summarized.An overview of the rotor-synchronization control strategies for disc contra-rotating permanent magnet synchronous motors(CRPMSMs)is presented.Finally,the current difficulties and development trends revealed in this review are discussed.

A dynamic-mode-decomposition-based acceleration method for unsteady adjoint equations at low Reynolds numbers

The computational cost of unsteady adjoint equations remains high in adjoint-based unsteady aerodynamic op-timization.In this letter,the solution of unsteady adjoint equations is accelerated by dynamic mode decomposi-tion(DMD).The pseudo-time marching of every real-time step is approximated as an infinite-dimensional linear dynamical system.Thereafter,DMD is utilized to analyze the adjoint vectors sampled from these pseudo-time marching.First-order zero frequency mode is selected to accelerate the pseudo-time marching of unsteady adjoint equations in every real-time step.Through flow past a stationary circular cylinder and an unsteady aerodynamic shape optimization example,the efficiency of solving unsteady adjoint equations is significantly improved.Re-sults show that one hundred adjoint vectors contains enough information about the pseudo-time dynamics,and the adjoint dominant mode can be precisely predicted only by five snapshots produced from the adjoint vectors,which indicates DMD analysis for pseudo-time marching of unsteady adjoint equations is efficient.

An OP-TEE Energy-Efficient Task Scheduling Approach Based on Mobile Application Characteristics

Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.

Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm

Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.

APPLICATION OF INTEGER CODING ACCELERATING GENETIC ALGORITHM IN RECTANGULAR CUTTING STOCK PROBLEM

An improved genetic algorithm and its application to resolve cutting stock problem arc presented. It is common to apply simple genetic algorithm （SGA） to cutting stock problem, but the huge amount of computing of SGA is a serious problem in practical application. Accelerating genetic algorithm （AGA） based on integer coding and AGA＇s detailed steps are developed to reduce the amount of computation, and a new kind of rectangular parts blank layout algorithm is designed for rectangular cutting stock problem. SGA is adopted to produce individuals within given evolution process, and the variation interval of these individuals is taken as initial domain of the next optimization process, thus shrinks searching range intensively and accelerates the evaluation process of SGA. To enhance the diversity of population and to avoid the algorithm stagnates at local optimization result, fixed number of individuals are produced randomly and replace the same number of parents in every evaluation process. According to the computational experiment, it is observed that this improved GA converges much sooner than SGA, and is able to get the balance of good result and high efficiency in the process of optimization for rectangular cutting stock problem.

Energy-Efficient Transceiver Design for Multi-Pair Two-Way Relay Systems

In this paper, we focus on energy-efficient transceiver and relay beamforming design for multi-pair two-way relay system. The multi-antenna users and the multi-antenna relay are considered in this work. Different from the existing works, the proposed algorithm is energy-efficient which is more applicable to the future green network. It considers both the sum-MSE problem and the power consumption problem for the users under the relay power constraint. Based on the optimal condition decomposition(OCD) method, the energy-efficient precoders at the users can be designed separately with limited information exchanged. The proposed relay beamforming algorithm is based on the alternative direction method of multipliers(ADMM) which has simpler iterative solution and enjoys good convergence. Simulation results demonstrate the performance of the proposed algorithms in terms of power consumption and MSE performance.

An efficient resource optimization scheme for D2D communication

With the rapid development of wireless technologies,wireless access networks have entered their Fifth-Generation(5G)system phase.The heterogeneous and complex nature of a 5G system,with its numerous technological scenarios,poses significant challenges to wireless resource management,making radio resource optimization an important aspect of Device-to-Device(D2D)communication in such systems.Cellular D2D communication can improve spectrum efficiency,increase system capacity,and reduce base station communication burdens by sharing authorized cell resources;however,can also cause serious interference.Therefore,research focusing on reducing this interference by optimizing the configuration of shared cellular resources has also grown in importance.This paper proposes a novel algorithm to address the problems of co-channel interference and energy efficiency optimization in a long-term evolution network.The proposed algorithm uses the fuzzy clustering method,which employs minimum outage probability to divide D2D users into several groups in order to improve system throughput and reduce interference between users.An efficient power control algorithm based on game theory is also proposed to optimize user transmission power within each group and thereby improve user energy efficiency.Simulation results show that these proposed algorithms can effectively improve system throughput,reduce co-channel interference,and enhance energy efficiency.

Ambient BackCom in beyond 5G NOMA networks: A multi-cell resource allocation framework

The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom), though opening new possibilities for massive connectivity, poses several challenges in dense wireless networks. One such challenge is the performance degradation of ambient BackCom in multi-cell NOMA networks under the effect of inter-cell interference. Driven by providing an efficient solution to the issue, this article proposes a new resource allocation framework that uses a duality theory approach. Specifically, the sum rate of the multi-cell network with backscatter tags and NOMA user equipment is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems, and then the closed form solution is derived. A comparison with the Orthogonal Multiple Access (OMA) ambient BackCom and pure NOMA transmission has been provided. Simulation results of the proposed NOMA ambient BackCom indicate a significant improvement over the OMA ambient BackCom and pure NOMA in terms of sum-rate gains.

Multiobjective Stochastic Linear Programming: An Overview

Many Optimization problems in engineering and economic involve the challenging task of pondering both conflicting goals and random data. In this paper, we give an up-to-date overview of how important ideas from optimization, probability theory and multicriteria decision analysis are interwoven to address situations where the presence of several objective functions and the stochastic nature of data are under one roof in a linear optimization context. In this way users of these models are not bound to caricature their problems by arbitrarily squeezing different objective functions into one and by blindly accepting fixed values in lieu of imprecise ones.

Mobile-agent-based energy-efficient scheduling with dynamic channel acquisition in mobile cloud computing

Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.