Self-assembly of Binary Particles with Electrostatic and van der Waals Interactions

Nanoparticles with competitive interactions in solution can aggregate into complex structures. In this work, the synergistic self-assembles of binary particles with electrostatic and van der Waals interactions are studied with the particle Langevin dynamics simulation using a simple coarse-grained particle model. Various aggregations such as spherical, stacking-disk and tube structures are observed by varying the particles size and the interaction strength. The aggregation structures are explained with the packing theories of amphiphilic molecules in solution and dibolck copolymers in bulk. When the opposite ions are introduced into solution, the distribution of structures in the phase diagram appears an obvious offset. The simulation result is helpful to deeply understand the formation mechanism of complex nanostructures of multicomponent particles in solution.

Effect of moving baffle on average velocity and mixing of binary particles in rotating drums

Adding a moving baffle to the drum is a new way to enhance the motion and mixing of particles in rotating drums.To obtain its influence on binary particles,horizontal rotating drums provided with a moving baffle were investigated by discrete element method(DEM).AtΩ=15 r/min,increasing the length of moving baffle can increase the fluctuation amplitude of average particle velocity.AtΩ=60 r/min,the influence of the moving baffle on the average velocity fluctuation tends to be more random.At both rotational speeds,the moving baffle causes the average particle velocity to fluctuate more sharply.The moving baffle can enhance particle mixing.AtΩ=15 r/min,the moving baffle with length ofδ=1/3 can best enhance particle mixing.However,atΩ=60 r/min,only the moving baffle with a specific length(δ=1/4)can enhance mixing.This basic research has a positive reference value for the application of the moving baffle in industry.

Experimental and computational studies on flow behavior of gas-solid fluidized bed with disparately sized binary particles

This paper presents experimental and computational studies on the flow behavior of a gas-solid fluidized bed with disparately sized binary particle mixtures. The mixing/segregation behavior and segregation efficiency of the small and large particles are investigated experimentally. Particle composition and operating conditions that influence the fluidization behavior of mixing/segregation are examined. Based on the granular kinetics theory, a multi-fluid CFD model has been developed and verified against the experimental results. The simulation results are in reasonable agreement with experimental data. The results showed that the smaller particles are found near the bed surface while the larger particles tend to settle down to the bed bottom in turbulent fluidized bed. However, complete segregation of the binary particles does not occur in the gas velocity range of 0.695-0.904 m/s. Segregation efficiency increases with increasing gas velocity and mean residence time of the binary particles, but decreases with increasing the small particle concentration. The calculated results also show that the small particles move downward in the wall region and upward in the core. Due to the effect of large particles on the movement of small particles, the small particles present a more turbulent velocity profile in the dense phase than that in the dilute phase.

Multi-fluid Eulerian simulation of mixing of binary particles in a gas-solid fluidized bed with a cohesive particle-particle drag model

A particle-particle(p-p)drag model is extended to cohesive particle flow by introducing solid surface energy to characterize cohesive collision energy loss.The effects of the proportion of cohesive particles on the mixing of binary particles were numerically investigated with the use of a Eulerian multiphase flow model incorporating the p-p drag model.The bed expansion,mixing,and segregation of Geldart-A and C particles were simulated with varying superficial velocities and Geldart-C particle proportions,from which we found that the p-p drag model can reasonably predict bed expansion of binary particles.Two segregation types of jetsam-mixture-flotsam and mixture-flotsam processes were observed during the fluidization processes for the Geldart-A and C binary particle system.The mixing processes of the binary particle system can be divided into three scales:macro-scale mixing,meso-scale mixing,and micro-scale mixing.At a constant superficial velocity the optimal mixing was observed for a certain cohesive particle proportion.

Rotation characteristic and granular temperature analysis in a bubbling fluidized bed of binary particles

In this work, a discrete particle model （DPM） was applied to investigate the dynamic characteristics in a gas-solid bubbling fluidized bed of binary solid particles. The solid phase was simulated by the hard- sphere discrete particle model. The large eddy simulation （LES） method was used to simulate the gas phase. To improve the accuracy of the simulation, an improved sub-grid scale （SGS） model in the LES method was also applied. The mutative Smagorinsky constant case was compared with the previously published experimental data. The simulation by the mutative Smagorinsky constant model exhibited better agreement with the experimental data than that by the common invariant Smagorinsky constant model. Various restitution coefficients and different compositions of binary solids were investigated to determine their influences on the rotation characteristics and granular temperatures of the particles. The particle translational and rotational characteristic distributions were related to certain simulation parameters.

SECURE STEGANOGRAPHY BASED ON BINARY PARTICLE SWARM OPTIMIZATION

The objective of steganography is to hide message securely in cover objects for secret communication.How to design a secure steganographic algorithm is still major challenge in this re-search field.In this letter,developing secure steganography is formulated as solving a constrained IP(Integer Programming) problem,which takes the relative entropy of cover and stego distributions as the objective function.Furthermore,a novel method is introduced based on BPSO(Binary Particle Swarm Optimization) for achieving the optimal solution of this programming problem.Experimental results show that the proposed method can achieve excellent performance on preserving neighboring co-occurrence features for JPEG steganography.

Coordinated Controller Tuning of a Boiler Turbine Unit with New Binary Particle Swarm Optimization Algorithm

Coordinated controller tuning of the boiler turbine unit is a challenging task due to the nonlinear and coupling characteristics of the system.In this paper,a new variant of binary particle swarm optimization （PSO） algorithm,called probability based binary PSO （PBPSO）,is presented to tune the parameters of a coordinated controller.The simulation results show that PBPSO can effectively optimize the control parameters and achieves better control performance than those based on standard discrete binary PSO,modified binary PSO,and standard continuous PSO.

Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method

The optimal seismic design of structures requires that time history analyses （THA） be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function （RBF） neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization （BPSO） is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.

ANALYSIS OF AFFECTIVE ECG SIGNALS TOWARD EMOTION RECOGNITION

Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform,an amount of ECG features was extracted sufficiently.Since feature selection is a combination optimization problem,Improved Binary Particle Swarm Optimization(IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor(KNN) classifier.In the experiment,it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently.

Fault location of distribution networks based on multi-source information

In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.

Hierarchical particle filter tracking algorithm based on multi-feature fusion

A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.

Optimal Allocation of a Hybrid Wind Energy-Fuel Cell System Using Different Optimization Techniques in the Egyptian Distribution Network

This paper presents an optimal proposed allocating procedure for hybrid wind energy combined with proton exchange membrane fuel cell (WE/PEMFC) system to improve the operation performance of the electrical distribution system (EDS). Egypt has an excellent wind regime with wind speeds of about 10 m/s at many areas. The disadvantage of wind energy is its seasonal variations. So, if wind power is to supply a significant portion of the demand, either backup power or electrical energy storage (EES) system is needed to ensure that loads will be supplied in reliable way. So, the hybrid WE/PEMFC system is designed to completely supply a part of the Egyptian distribution system, in attempt to isolate it from the grid. However, the optimal allocation of the hybrid units is obtained, in order to enhance their benefits in the distribution networks. The critical buses that are necessary to install the hybrid WE/ PEMFC system, are chosen using sensitivity analysis. Then, the binary Crow search algorithm (BCSA), discrete Jaya algorithm (DJA) and binary particle swarm optimization (BPSO) techniques are proposed to determine the optimal operation of power systems using single and multi-objective functions (SOF/MOF). Then, the results of the three optimization techniques are compared with each other. Three sensitivity factors are employed in this paper, which are voltage sensitivity factor (VSF), active losses sensitivity factor (ALSF) and reactive losses sensitivity factor (RLSF). The effects of the sensitivity factors (SFs) on the SOF/MOF are studied. The improvement of voltage profile and minimizing active and reactive power losses of the EDS are considered as objective functions. Backward/forward sweep (BFS) method is used for the load flow calculations. The system load demand is predicted up to year 2022 for Mersi-Matrouh City as a part of Egyptian distribution network, and the design of the hybrid WE/PEMFC system is applied. The PEMFC system is designed considering simplified mathematical expressions. The economics of operation of both WE and PEMFC system are also presented. The results prove the capability of the proposed procedure to find the optimal allocation for the hybrid WE/PEMFC system to improve the system voltage profile and to minimize both active and reactive power losses for the EDS of Mersi-Matrough City.

Mixing and segregation of solid particles in a conical spouted bed： Effect of particle size and density

In this work, the mixing and segregation of binary mixtures of particles with different sizes and densities in a pseudo-2D spouted bed were studied experimentally. A binary mixture of solid particles including sand, gypsum, and polyurethane was used. To determine the particles mass fraction, and their mixing and segregation in the bed, an image-processing technique was developed and used. Important hydrodynamic parameters, such as the axial and radial segregation profiles of the solid particles, were measured. The effects of air velocity, particle size, and particle mass fraction were also evaluated. The flow regime in the spouted bed and the time required for reaching the equilibrium state of the solid particles were discussed. The results showed that the segregation of solid particles and the time to equilibrium both decreased when the air velocity increased to much larger than the minimum spouting velocity. The axia! segregation increased with the diameter ratio of the particles. Upon completion of the test, coarse particles were concentrated mainly in the spout region, while fine particles were aggregated in the annulus region. Examination of the flow pattern in the spouted bed showed that the particles near the wall had longer flow paths, while those near the spout region had shorter flow paths.

Three-dimensional multi-phase simulation of the mixing and segregation of binary particle mixtures in a two-jet spout fluidized bed

This study presents a three-dimensional numerical study of the mixing and segregation of binary particle mixtures in a two-jet spout fluidized bed based on an Eulerian-Eulerian three-fluid model. Initially, the particle mixtures were premixed and packed in a rectangular fluidized bed. As the calculation began, the gas stream was injected into the bed from the distributor and jet nozzles. The model was validated by comparing the simulated jet penetration depths with corresponding experimental data. The main features of the complex gas-solid flow behaviors and the mechanism of mixing and segregation of the binary mixtures were analyzed, Moreover, further simulations were carried out to evaluate the effects of operating conditions on the mixing and segregation of binary particle mixtures. The results illustrate that mixing can be enhanced by increasing the jet velocity or enlarging the difference of initial proportions of binary particle mixtures.

Three-dimensional multiphase full-loop simulation of directional separation of binary particle mixtures in high-flux coal-direct chemical-looping combustion system

Coal-direct chemical-looping combustion(CDCLC)is a promising coal combustion technique that provides CO2 capture with a low energy penalty.In this study,we developed a three-dimensional Eulerian-Eulerian multiphase full-loop model for simulating the circulation and separation of binary particle mixtures in a novel high-flux CDCLC system.This model comprised a high-flux circulating fluidized bed as the fuel reactor(FR),a counter-flow moving bed as the air reactor(AR),a high-flux carbon stripper,two downcomers,and two J-valves.This model predicted the main features of complex gas-solid flow behaviors in the system.The simulation results showed that quasi-stable solid circulation in the whole system could be achieved,and the FR,AR,and J-valves operated in a dense suspension upflow regime,a near-plug-flow regime,and a bubbling fluidization regime,respectively.The multiphase flow model of binary particle mixtures was used to predict the mechanisms of directional separation of binary particle mixtures of an oxygen carrier(OC)and coal throughout the system.A decrease in the baffle aspect ratio of the inertial separator improved the coal selective separation efficiency but resulted in a slight decline in the OC selective separation;this is believed to be the result of weakening of particle collisions with the baffle.A higher FR gas velocity had a slightly negative effect on the OC selective separation efficiency,but improved the coal selective separation efficiency;this can be attributed to an increase in the particle-carrying capacity of the gas stream.A decrease in the coal particle size led to better entrainment of the coal particles by the gas stream and this increased the coal selective separation efficiency.In real CDCLC applications,the operating variables for separation of binary particle mixtures should be comprehensively assessed to determine their positive and negative effects on the carbon capture efficiency,OC regeneration efficiency,gas leakage restraint,energy consumption,and fuel conversion.

Experimental study on the 3D vibrated packing densification of binary sphere mixtures

The packing densification of binary spherical mixtures under 3D mechanical vibration was studied experimentally. The influences of vibration frequency （ω）, volume fraction of large spheres （XL）, sphere size ratio （r, diameter ratio of small to large spheres）, and container size （D） on the random binary packing density （p） were systematically analyzed. For any given set of conditions, there exist optimal ω and XL to realize the densest random binary packing; too large or small ω and XL is not helpful for densification. The influences of both r and D on p are monotonic; either reducing r or increasing D leads to a high value of p. With all other parameters held constant, the densest random packing occurs when XL is dominant, which is in good agreement with the Furnas relation. Moreover, the highest random binary packing density obtained in our work agrees well with corresponding numerical and analytical results in the literature.

Experimental and numerical investigation of liquid-solid binary fluidized beds： Radioactive particle tracking technique and dense discrete phase model simulations

Liquid-solid binary fluidized beds are widely used in many industries. However, the flow behavior of such beds is not well understood due to the lack of accurate experimental and numerical data. In the current study, the behavior of monodisperse and binary liquid-solid fluidized beds of the same density but dif- ferent sizes is investigated using radioactive particle tracking （RPT） technique and a dense discrete phase model （DDPM）. Experiments and simulations are performed in monodisperse fluidized beds containing two different sizes of glass beads （0.6 and I mm） and a binary fluidized bed of the same particles for vari- ous bed compositions. The results show that both RPT and DDPM can predict the mixing and segregation pattern in liquid-solid binary fluidized beds. The mean velocity predictions of DDPM are in good agree- ment with the experimental findings for both monodisperse and binary fluidized beds. However, the axial root mean square velocity predictions are only reasonable for bigger particles. Particle-particle interac- tions are found to be critical for predicting the flow behavior of solids in liquid-solid binary fluidized beds.

A Binary Particle Swarm Optimization for the Minimum Weight Dominating Set Problem

The minimum weight dominating set problem （MWDSP） is an NP-hard problem with a lot of real-world applications. Several heuristic algorithms have been presented to produce good quality solutions. However, the solution time of them grows very quickly as the size of the instance increases. In this paper, we propose a binary particle swarm optimization （FBPSO） for solving the MWDSP approximately. Based on the characteristic of MWDSP, this approach designs a new position updating rule to guide the search to a promising area. An iterated greedy tabu search is used to enhance the solution quality quickly. In addition, several stochastic strategies are employed to diversify the search and prevent premature convergence. These methods maintain a good balance between the exploration and the exploitation. Experimental studies on 106 groups of 1 060 instances show that FBPSO is able to identify near optimal solutions in a short running time. The average deviation between the solutions obtained by FBPSO and the best known solutions is 0.441%. Moreover, the average solution time of FBPSO is much less than that of other existing algorithms. In particular, with the increasing of instance size, the solution time of FBPSO grows much more slowly than that of other existing algorithms.

Multi-objective allocation of measuring binary particle swarm optimization

Due to the size and complexity of power network and the cost of monitoring and telecommunication equipment, it is unfeasible to monitor the whole system variables. All system analyzers use voltages and currents of the network. Thus, monitoring scheme plays a main role in system analysis, control, and protection. To monitor the whole system using distributed measurements, strategic placement of them is needed. This paper improves a topological circuit observation method to minimize essential monitors. Besides the observability under normal condition of power networks, the observability of abnormal network is considered. Consequently, a high level of system reliability is carried out. In terms of reliability constraint, identification of bad measurement data in a given measurement system by making theme sure to be detectable is well done. Furthermore, it is maintained by a certain level of reliability against the single-line outages. Thus, observability is satisfied if all possible single line outages are plausible. Consideration of these limitations clears the role of utilizing an optimization algorithm. Hence, particle swarm optimization （PSO） is used to minimize monitoring cost and removing unobser-vable states under abnormal condition, simultaneously. The algorithm is tested in IEEE 14 and 30-bus test systems and Iranian （Mazandaran） Regional Electric Company.

Multi-objective Dynamic Reconfiguration for Urban Distribution Network Considering Multi-level Switching Modes

The increasing integration of photovoltaic generators(PVGs) and the uneven economic development in different regions may cause the unbalanced spatial-temporal distribution of load demands in an urban distribution network(UDN). This may lead to undesired consequences, including PVG curtailment, load shedding, and equipment inefficiency, etc. Global dynamic reconfiguration provides a promising method to solve those challenges. However, the power flow transfer capabilities for different kinds of switches are diverse, and the willingness of distribution system operators(DSOs) to select them is also different. In this paper, we formulate a multi-objective dynamic reconfiguration optimization model suitable for multi-level switching modes to minimize the operation cost, load imbalance, and the PVG curtailment. The multi-level switching includes feeder-level switching, transformer-level switching, and substation-level switching. A novel load balancing index is devised to quantify the global load balancing degree at different levels. Then, a stochastic programming model based on selected scenarios is established to address the uncertainties of PVGs and loads. Afterward, the fuzzy c-means(FCMs) clustering is applied to divide the time periods of reconfiguration. Furthermore, the modified binary particle swarm optimization(BPSO)and Cplex solver are combined to solve the proposed mixed-integer second-order cone programming(MISOCP) model. Numerical results based on the 148-node and 297-node systems are obtained to validate the effectiveness of the proposed method.