Modeling analysis of cobalt-based Fischer-Tropsch catalyst particles

The influences of particle size,shape,and catalyst distribution on the reactivity and hydrocarbon product selectivity of a cobalt-based catalyst for Fischer-Tropsch synthesis were investigated in the present work.A self-consistent kinetic model for Fischer-Tropsch reaction proposed here was found to correlate experimental data well and hence was used to describe the consumption rates of reactants and formation rates of hydrocarbon products.The perturbed-chain statistical associating fluid theory equation of state was used to describe vapor-liquid equilibrium behavior associated with Fischer-Tropsch reaction.Local interaction between intraparticle diffusion and Fischer-Tropsch reaction was investigated in detail.Results showed that in order to avoid the adverse influence of intraparticle diffusional limitations on catalyst reactivity and product selectivity,the use of small particles is necessary.Large eggshell spherical particles are shown to keep the original catalyst reactivity and enhance the selectivity of heavy hydrocarbon products.The suitable layer thickness for a spherical particle with a diameter of 2 mm is nearly 0.15 mm.With the same outer diameter of 2 mm,the catalyst reactivity and heavy product selectivity of hollow cylindrical particles with a layer thickness of 0.25 mm are found to be larger than eggshell spherical particles.From the viewpoint of catalytic performance,hollow cylindrical particles are a better choice for industrial applications.

Updated Lagrangian Particle Hydrodynamics (ULPH)Modeling of Natural Convection Problems

Natural convection is a heat transfer mechanism driven by temperature or density differences,leading to fluid motion without external influence.It occurs in various natural and engineering phenomena,influencing heat transfer,climate,and fluid mixing in industrial processes.This work aims to use the Updated Lagrangian Particle Hydrodynamics(ULPH)theory to address natural convection problems.The Navier-Stokes equation is discretized using second-order nonlocal differential operators,allowing a direct solution of the Laplace operator for temperature in the energy equation.Various numerical simulations,including cases such as natural convection in square cavities and two concentric cylinders,were conducted to validate the reliability of the model.The results demonstrate that the proposed model exhibits excellent accuracy and performance,providing a promising and effective numerical approach for natural convection problems.

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.

Calculation of Particle Decay Times in the Standard Model

We present here a two-step method of classification and calculation for decay rates in the Standard Model. The first step is a phenomenological classification method, which is an extended and improved schematic experimental formula for decay width originally introduced by Chang. This schematic formula separates decays into seven classes. Furthermore, from it is derived a process-specific interaction energy mX. The second step is a numerical calculation method, which calculates this interaction energy mX numerically by minimization of action from the Lagrangian of the process, from which follows the decay width via the phenomenological formula. The Lagrangian is based on an extension of the Standard Model, the extended SU(4)-preon-model. A comparison of numerically calculated and observed decay widths for a large selection of decays shows a good agreement.

Influence of syngas components and ash particles on the radiative heat transfer in a radiant syngas cooler

Radiant syngas cooler(RSC)is widely used as a waste heat recovery equipment in industrial gasification.In this work,an RSC with radiation screens is established and the impact of gaseous radiative property models,gas components,and ash particles on heat transfer is investigated by the numerical simulation method.Considering the syngas components and the pressure environment of the RSC,a modified weighted-sum-of-gray-gases model was developed.The modified model shows high accuracy in validation.In computational fluid dynamics simulation,the calculated steam production is only 0.63%in error with the industrial data.Compared with Smith's model,the temperature decay along the axial direction calculated by the modified model is faster.Syngas components are of great significance to heat recovery capacity,especially when the absorbing gas fraction is less than 10%.After considering the influence of particles,the outlet temperature and the proportion of radiative heat transfer are less affected,but the difference in steam output reaches 2.7 t·h^(-1).The particle deposition on the wall greatly reduces the heat recovery performance of an RSC.

Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.

Determination of the Pile Drivability Using Random Forest Optimized by Particle Swarm Optimization and Bayesian Optimizer

Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical applications.Conventional methods of predicting pile drivability often rely on simplified physicalmodels or empirical formulas,whichmay lack accuracy or applicability in complex geological conditions.Therefore,this study presents a practical machine learning approach,namely a Random Forest(RF)optimized by Bayesian Optimization(BO)and Particle Swarm Optimization(PSO),which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles(i.e.,maximumcompressive stress,maximum tensile stress,and blow per foot).In addition,support vector regression,extreme gradient boosting,k nearest neighbor,and decision tree are also used and applied for comparison purposes.In order to train and test these models,among the 4072 datasets collected with 17model inputs,3258 datasets were randomly selected for training,and the remaining 814 datasets were used for model testing.Lastly,the results of these models were compared and evaluated using two performance indices,i.e.,the root mean square error(RMSE)and the coefficient of determination(R2).The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters,specifically 0.044,0.438,and 0.146;and higher R^(2) values than other implemented techniques,specifically 0.966,0.884,and 0.977.In addition,the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo(MC)simulation.It can be concluded that the optimized RF model could be used to predict the performance of the pile,and it may provide a useful reference for solving some problems under similar engineering conditions.

Numerical Simulations of Snow Accumulation in the Bogie Region of a Train Considering Snow Particle Rotation

To investigate the influence of snow particle rotational motion on the accumulation of snow in the bogie region of high-speed trains,an Euler‒Lagrange numerical approach is adopted.The study examines the effects of snow particle diameter and train speed on the ensuing dynamics.It is shown that considering snow particle rotational motion causes significant deviation in the particle trajectories with respect to non-rotating particles.Such a deviation increases with larger snow particle diameters and higher train speeds.The snow accumulation on the overall surface of the bogie increases,and the amount of snow on the vibration reduction device varies greatly.In certain conditions,the amount of accumulated snow can increase by several orders of magnitudes.

Monitoring shear deformation of sliding zone via fiber Bragg grating and particle image velocimetry

Monitoring shear deformation of sliding zones is of great significance for understanding the landslide evolution mechanism,in which fiber optic strain sensing has shown great potential.However,the cor-relation between strain measurements of quasi-distributed fiber Bragg grating(FBG)sensing arrays and shear displacements of surrounding soil remains elusive.In this study,a direct shear model test was conducted to simulate the shear deformation of sliding zones,in which the soil internal deformation was captured using FBG strain sensors and the soil surface deformation was measured by particle image velocimetry(PIV).The test results show that there were two main slip surfaces and two secondary ones,developing a spindle-shaped shear band in the soil.The formation of the shear band was successfully captured by FBG sensors.A sinusoidal model was proposed to describe the fiber optic cable deformation behavior.On this basis,the shear displacements and shear band widths were calculated by using strain measurements.This work provides important insight into the deduction of soil shear deformation using soil-embedded FBG strain sensors.

Direct measurement and theoretical prediction model of interparticle adhesion force between irregular planetary regolith particles

Interparticle adhesion force has a controlling effect on the physical and mechanical properties of planetary regolith and rocks.The current research on the adhesion force of planetary regolith and rock particles has been primarily based on the assumption of smooth spherical particles to calculate the intergranular adhesion force;this approach lacks consideration for the adhesion force between irregular shaped particles.In our study,an innovative approach was established to directly measure the adhesion force between the arbitrary irregular shaped particles;the probe was modified using simulated lunar soil particles that were a typical representation of planetary regolith.The experimental results showed that for irregular shaped mineral particles,the particle size and mineral composition had no significant influence on the interparticle adhesion force;however,the complex morphology of the contact surface predominantly controlled the adhesion force.As the contact surface roughness increased,the adhesion force gradually decreased,and the rate of decrease gradually slowed;these results were consistent with the change trend predicted via the theoretical models of quantum electrodynamics.Moreover,a theoretical model to predict the adhesion force between the irregular shaped particles was constructed based on Rabinovich’s theory,and the prediction results were correlated with the experimental measurements.

Particles Composition and Interactions Using the Nuon Model

The Standard Model in Particle Physics has been able to make many predictions confirmed later with a flow of experimental results. With the discovery of the Higgs boson at the LHC, one is full of admiration for the people contributing to this model fifty years ago and its predictions that have been confirmed gradually. The original particle quark constituent model has evolved with the deep inelastic experiments to a quark and gluons system, then to a more general system with virtual quarks. This work is the result of observations while working at CERN in Geneva with many different experiments at the ISR, SPS, LEP, LHC colliders. A new model based on nuons is introduced, that allows accurate evaluations of the particle masses (mesons and baryons) and magnetic moment, computes very accurately the kinematics distributions for particles and jets observed in the p-p collisions at the LHC (elastic and inelastic) and at lower energy machines. This new model looks at a first glance in contradiction with the quark model because it can build the elementary particles with nuons only, i.e. electrons and neutrinos. However, all the existing physics involved in electron, positron and neutrino interactions may be used to explain interactions between composite particles such as protons or heavy ions.

Mechanical responses and acoustic emission behaviors of coal under compressive differential cyclic loading(DCL):a numerical study via 3D heterogeneous particle model

The stability of coal walls(pillars)can be seriously undermined by diverse in-situ dynamic disturbances.Based on a 3D par-ticle model,this work strives to numerically replicate the major mechanical responses and acoustic emission(AE)behaviors of coal samples under multi-stage compressive cyclic loading with different loading and unloading rates,which is termed differential cyclic loading(DCL).A Weibull-distribution-based model with heterogeneous bond strengths is constructed by both considering the stress-strain relations and AE parameters.Six previously loaded samples were respectively grouped to indicate two DCL regimes,the damage mechanisms for the two groups are explicitly characterized via the time-stress-dependent variation of bond size multiplier,and it is found the two regimes correlate with distinct damage patterns,which involves the competition between stiffness hardening and softening.The numerical b-value is calculated based on the mag-nitudes of AE energy,the results show that both stress level and bond radius multiplier can impact the numerical b-value.The proposed numerical model succeeds in replicating the stress-strain relations of lab data as well as the elastic-after effect in DCL tests.The effect of damping on energy dissipation and phase shift in numerical model is summarized.

Co-digestion of Waste Coffee and Cocoa Hulls: Modeling of Biogas Production by the Particle Swarm Method

Energy is a crucial material for the development of our economy.Access to sufficient energy remains a major concern for developing countries,particularly those in sub-Saharan Africa.The major challenge lies in access to clean,environmentally friendly,quality and low-cost energy in different households in our municipalities.To cope with this vast energy gap,many households are dependent on fossil fuels.In Cameroon,the consumption of wood for the supply of energy is increasing by 4%per year.Overall,approximately 80%of households in Cameroon depend on woody biomass as the sole main source of energy supply in Cameroon and demand is growing over time.In view of the climatic variations that our countries,particularly Cameroon,undergo through deforestation,the use of wood as a source of energy is expensive and harmful to the environment,hence the urgency of replacing wood with renewable energy.Biogas is one of the most versatile sources of renewable energy.On an industrial scale,it is important to automate the process control.The main objective of the present work is to model the anaerobic digestion of coffee and cocoa hulls using the particle swarm optimisation method.Pretreatment using the organosolv process was done.This resulted in 48%lignin removal and 22%cellulose increase.For the pretreated biomass,the maximum production rate was 21 NmLCH4 per day with a biomethane yield of 90 NmLCH4/gVS.This represents an enhancement of 117%in biomethane yield.A positive flammability test was recorded after the 10th day of retention time.Moreover,the data collected during anaerobic digestion allowed implementation of a two-phase mathematical model.The thirteen parameters of the model were estimated with particle swarm optimisation method in Matlab.The model was able to simulate the biomethane production kinetics and variation of volatile fatty acid concentration.

Tribological properties and wear prediction model of TiC particles reinforced Ni-base alloy composite coatings

TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite coatings under dry friction were researched. The wear prediction model of the composite coatings was established based on the least square support vector machine (LS-SVM). The results show that the composite coatings exhibit smaller friction coefficients and wear losses than the Ni-based alloy coatings under different friction conditions. The predicting time of the LS-SVM model is only 12.93%of that of the BP-ANN model, and the predicting accuracies on friction coefficients and wear losses of the former are increased by 58.74%and 41.87%compared with the latter. The LS-SVM model can effectively predict the tribological behavior of the TiCP/Ni-base alloy composite coatings under dry friction.

Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model

Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.

Working Condition Real-Time Monitoring Model of Lithium Ion Batteries Based on Distributed Parameter System and Single Particle Model

Lithium ion batteries are complicated distributed parameter systems that can be described preferably by partial differential equations and a field theory. To reduce the solution difficulty and the calculation amount, if a distributed parameter system is described by ordinary differential equations （ODE） during the analysis and the design of distributed parameter system, the reliability of the system description will be reduced, and the systemic errors will be introduced. Studies on working condition real-time monitoring can improve the security because the rechargeable LIBs are widely used in many electronic systems and electromechanical equipment. Single particle model （SPM） is the simplification of LIB under some approximations, and can estimate the working parameters of a LIB at the faster simulation speed. A LIB modelling algorithm based on PDEs and SPM is proposed to monitor the working condition of LIBs in real time. Although the lithium ion concentration is an unmeasurable distributed parameter in the anode of LIB, the working condition monitoring model can track the real time lithium ion concentration in the anode of LIB, and calculate the residual which is the difference between the ideal data and the measured data. A fault alarm can be triggered when the residual is beyond the preset threshold. A simulation example verifies that the effectiveness and the accuracy of the working condition real-time monitoring model of LIB based on PDEs and SPM.

Ceramic particles reinforced copper matrix composites manufactured by advanced powder metallurgy:preparation, performance, and mechanisms

Copper matrix composites doped with ceramic particles are known to effectively enhance the mechanical properties,thermal expansion behavior and high-temperature stability of copper while maintaining high thermal and electrical conductivity.This greatly expands the applications of copper as a functional material in thermal and conductive components,including electronic packaging materials and heat sinks,brushes,integrated circuit lead frames.So far,endeavors have been focusing on how to choose suitable ceramic components and fully exert strengthening effect of ceramic particles in the copper matrix.This article reviews and analyzes the effects of preparation techniques and the characteristics of ceramic particles,including ceramic particle content,size,morphology and interfacial bonding,on the diathermancy,electrical conductivity and mechanical behavior of copper matrix composites.The corresponding models and influencing mechanisms are also elaborated in depth.This review contributes to a deep understanding of the strengthening mechanisms and microstructural regulation of ceramic particle reinforced copper matrix composites.By more precise design and manipulation of composite microstructure,the comprehensive properties could be further improved to meet the growing demands of copper matrix composites in a wide range of application fields.

Thermo-mechanical coupled particle model for rock

A thermo-mechanical coupled particle model for simulation of thermally-induced rock damage based on the particle simulation method was proposed.The simulation results of three verification examples,for which the analytical solutions are available,demonstrate the correctness and usefulness of the thermo-mechanical coupled particle model.This model is applied to simulating an application example with two cases：one is temperature-independent elastic modulus and strength,while the other is temperature-dependent elastic modulus and strength.The related simulation results demonstrate that microscopic crack initiation and propagation process with consideration of temperature-independent and temperature-dependent elastic modulus and strength are different and therefore,the corresponding macroscopic failure patterns of rock are also different.On the contrary,considering the temperature-dependent elastic modulus and strength has no or little effect on the heating conduction behavior.Numerical results,which are obtained by using the proposed model with temperature-dependent elastic modulus and strength,agree well with the experimental results.This also reveals that the rock subjected to heating experiences much more cracking than the rock subjected to cooling.

Modeling of Motion of Particle Clouds Formed by Dumping Dredged Material

The motion of particle clouds formed by dumping dredged material into quiescent waters is experimentally and numerically studied. In the numerical model, the particle phase is modeled by the dispersion model, and turbulence is calculated by the large eddy simulation. The governing equations, including the filtered Navier-Stokes equations and mass transport equation, are solved based on the operator-splitting algorithm and an implicit cubic spline interpolation scheme. The eddy viscosity is evaluated by the modified Smagorinsky model including the buoyancy term. Comparisons of main flow characteristics, including shape, size, average density excess, moving speed and the amount of particles deposited on the bed, between experimental and computational results show that the numerical model well predicts the motion of the cloud from the falling to spreading stage. The effects of silt-fence on the motion of the particle cloud are also investigated.

Modeling of gas-solid flow in a CFB riser based on computational particle fluid dynamics

A three-dimensional model for gas-solid flow in a circulating fluidized bed（CFB） riser was developed based on computational particle fluid dynamics（CPFD）.The model was used to simulate the gas-solid flow behavior inside a circulating fluidized bed riser operating at various superficial gas velocities and solids mass fluxes in two fluidization regimes,a dilute phase transport（DPT） regime and a fast fluidization（FF） regime.The simulation results were evaluated based on comparison with experimental data of solids velocity and holdup,obtained from non-invasive automated radioactive particle tracking and gamma-ray tomography techniques,respectively.The agreement of the predicted solids velocity and holdup with experimental data validated the CPFD model for the CFB riser.The model predicted the main features of the gas-solid flows in the two regimes;the uniform dilute phase in the DPT regime,and the coexistence of the dilute phase in the upper region and the dense phase in the lower region in the FF regime.The clustering and solids back mixing in the FF regime were stronger than those in the DPT regime.