湿容量在烧结混合料制粒中的应用

优化制粒是烧结生产中提高产量和质量,降低能耗的重要措施。水分控制是制粒生产中的重要环节,它影响到制粒颗粒的强度和粒度组成,决定了生料层的透气性。为提高制粒生产中加水量的控制水平,文章对湿容量在铁矿石烧结生产中的应用作了相关研究。物料湿容量是指单位质量的烘干物料在一定温度和压力下所能容纳的最大含水量。实验室试验及工业试验表明:①物料的吸水过程规律显著,湿容量是物料重要的吸水特性参数;②湿容量与物料制粒的适宜含水量成线性关系,相关系数R=93.1%;③可通过物料的CaO、Al2O3、MgO、<0.2mm的粒级含量及0.2～0.7mm的粒级含量等理化指标来预测湿容量。为提高湿容量的预测效果,比表面积、孔隙率、接触角等物性参数的影响也必须加以考虑。

Application and optimization design of non-obstructive particle damping-phononic crystal vibration isolator in viaduct structure-borne noise reduction

The problems associated with vibrations of viaducts and low-frequency structural noise radiation caused by train excitation continue to increase in importance.A new floating-slab track vibration isolator-non-obstructive particle damping-phononic crystal vibration isolator is proposed herein,which uses the particle damping vibration absorption technology and bandgap vibration control theory.The vibration reduction performance of the NOPD-PCVI was analyzed from the perspective of vibration control.The paper explores the structure-borne noise reduction performance of the NOPD-PCVIs installed on different bridge structures under varying service conditions encountered in practical engineering applications.The load transferred to the bridge is obtained from a coupled train-FST-bridge analytical model considering the different structural parameters of bridges.The vibration responses are obtained using the finite element method,while the structural noise radiation is simulated using the frequency-domain boundary element method.Using the particle swarm optimization algorithm,the parameters of the NOPD-PCVI are optimized so that its frequency bandgap matches the dominant bridge structural noise frequency range.The noise reduction performance of the NOPD-PCVIs is compared to the steel-spring isolation under different service conditions.

Strengthening pelletization of manganese ore fines containing high combined water by high pressure roll grinding and optimized temperature elevation system

Pelletization is one of useful processes for the agglomeration of iron ore or concentrates. However, manganese ore fines are mainly agglomerated by sintering due to its high combined water which adversely affects the roasting performance of pellets. In this work, high pressure roll grinding(HPRG) process and optimization of temperature elevation system were investigated to improve the strength of fired manganese ore pellets. It is shown that the manganese ore possesses good ballability after being pretreated by HPRG twice, and good green balls were produced under the conditions of blending 2.0% bentonite in the feed, balling for 7 min at 16.00% moisture. High quality roasted pellets with the compressive strength of 2711 N per pellet were manufactured through preheating at 1050 °C for 10 min and firing at 1335 °C for 15 min by controlling the cracks formation. The fired manganese pellets keep the strength by the solid interconnection of recrystallized pyrolusite grains and the binding of manganite liquid phase which filled the pores and clearance among minerals. The product pellets contain high Mn grade and low impurities, and can be used to smelt ferromanganese, which provides a possible way to use imported manganese ore fines containing high combined water to produce high value ferromanganese.

A composite particle swarm algorithm for global optimization of multimodal functions

During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.

Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor

This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.

Advanced Coverage Optimization Techniques for Small Cell Clusters

Coverage challenge for small considered to be a optlmlzation is a main cell clusters which are promising solution to provide seamless cellular coverage for large indoor or outdoor areas. This paper focuses on small cell cluster coverage problems and proposes both centralized and distributed self-optimization methods. Modified Particle swarm optimization （MPSO） is introduced to centralized optimization which employs particle swarm optimization （PSO） and introduces a heuristic power control scheme to accelerate the algorithm to search tbr the global optimum solution. Distributed coverage optimization is modeled as a non-cooperative game, with a utility function considering both throughput and interference. An iterative power control algorithm is then proposed using game theory （DGT） which converges to Nash Equilibrium （NE）. Simulation results show that both MPSO and DGT have excellent performance in coverage optimization and outperform optimization using simulated annealing algorithm （SA）, reaching higher coverage ratio and throughput while with less iterations.

Particle Swarm Optimization based predictive control of Proton Exchange Membrane Fuel Cell (PEMFC)

Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidification system and output performance of PEMFC stack are briefly analyzed. Predictive control of PEMFC based on Support Vector Regression Machine (SVRM) is presented and the SVRM is constructed. The processing plant is modelled on SVRM and the predictive control law is obtained by using Particle Swarm Optimization (PSO). The simulation and the results showed that the SVRM and the PSO re-ceding optimization applied to the PEMFC predictive control yielded good performance.

Intelligent anti-swing control for bridge crane

A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural networks were used to approach the uncertainties of the positioning subsystem,lifting-rope subsystem and anti-swing subsystem.Then,the parameters of the controller were optimized with PSO to enable the system to have good dynamic performances.During the process of high-speed load hoisting and dropping,this method can not only realize the accurate position of the trolley and eliminate the sway of the load in spite of existing uncertainties,and the maximum swing angle is only ±0.1 rad,but also completely eliminate the chattering of conventional sliding mode control and improve the robustness of system.The simulation results show the correctness and validity of this method.

Breakage and separation mechanism of ZGM coal mill based on parameters optimization model

Sampling ports were firstly drilled on a ZGM95 coal mill in the power plant in China, and the coal samples from various points in the pulverizer were collected under the different operation conditions. The prop- erty of the sampling material from the mill was analyzed, applying the float-sink test, size distribution analysis, proximate analysis and so on. It was indicated that the ＋250 I^m fraction in the pulverized fuel accounted for only 0.02%, while it was 83.2% in the new feed. The circulating ratio and coal flow in the separator and the cone zone were calculated using the mass balance of the circulating load. So, the cir- culating ratio in the separator of the pulverizer was between 8 and 13, and the circulating ratio, the feed flow of separator and cone zone all raised with the increase of the air volume. Furthermore, the parameters of the separation functions were obtained based on the fitting method. It was shown that the mean value of the shape factor B was 0.7617, and the parameter D which is the particle size at 50% cumulative yield in the separator almost kept unchanged.

Vehicle path tracking by integrated chassis control

The control problem of trajectory based path following for passenger vehicles is studied. Comprehensive nonlinear vehicle model is utilized for simulation vehicle response during various maneuvers in MATLAB/Simulink. In order to follow desired path, a driver model is developed to enhance closed loop driver/vehicle model. Then, linear quadratic regulator(LQR) controller is developed which regulates direct yaw moment and corrective steering angle on wheels. Particle swam optimization(PSO) method is utilized to optimize the LQR controller for various dynamic conditions. Simulation results indicate that, over various maneuvers, side slip angle and lateral acceleration can be reduced by 10% and 15%, respectively, which sustain the vehicle stable. Also, anti-lock brake system is designed for longitudinal dynamics of vehicle to achieve desired slip during braking and accelerating. Proposed comprehensive controller demonstrates that vehicle steerability can increase by about 15% during severe braking by preventing wheel from locking and reducing stopping distance.

Improved S Surface Controller and Semi-physical Simulation for AUV

S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV).However, it is difficult to adjust their control parameters manually.Choosing the optimum parameters for the controller of a particular AUV is a significant challenge.To automate the process, a modified particle swarm optimization (MPSO) algorithm was proposed.It was based on immune theory, and used a nonlinear regression strategy for inertia weight to optimize AUV control parameters.A semi-physical simulation system for the AUV was developed as a platform to verify the proposed control method, and its structure was considered.The simulation results indicated that the semi-physical simulation platform was helpful, the optimization algorithm has good local and global searching abilities, and the method can be reliably used for an AUV.

PSO-based Control Algorithm for Polarization Mode Dispersion Self-adaptive Compensation

Polarization mode dispersion(PMD) is considered to be the ultimate limitation in high-speed optical fiber communication systems. Establishing an effective control algorithm for adaptive PMD compensation is a challenging task, because PMD possesses the time-varying and statistical properties. The particle swarm optimization(PSO) algorithm is introduced into self-adaptive PMD compensation as feedback control algorithm. The experiment results show that PSO-based control algorithm has some unique features of rapid convergence to the global optimum without being trapped in local sub-optima and good robustness to noise in the optical fiber transmission line that has never been achieved in PMD compensation before.

Accelerated Particle Swarm Optimization for Controlling Virtual Power Plant Consisting of Renewable Energy Sources

RES （renewable energy sources）, such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of VPP （virtual power plant） has been developed. The VPP is composed of several RES, from which at least one of them is fully controllable. Because the production of noncontrollable RES can not be forecasted perfectly, therefore an optimal dispatch schedule within VPP is needed. To address this problem, an APSO （accelerated particle swarm optimization） is used to solve the constrained optimal dispatch problem within VPP. The experimental results show that the proposed optimization method provides high quality solutions while meeting constraints.

Robust Design Method for Adjustable Mechanisms

Most conventional robust design methods assume design solutions are fixed values. Using these methods, designers set each control factor to a fixed value to maximize the robustness of objective characteristics. However, fluctuations in the objective characteristic often exceed the allowable range in a design problem. Consequently, it is difficult to obtain sufficient robustness using conventional methods. This research defines adjustable control factors whose values can be adjusted within a given range to increase robustness and proposes a method to calculate robustness, including factors to adjust the objective characteristic and derive optimum ranges of the factors. The robustness index, which indicates the feasibility that the objective characteristic values are within the tolerance by the adjustment, is calculated by the Monte Carlo method, while the range of adjustable control factors is optimized using the Vector evaluated particle swarm optimization. Finally, an engineering example is presented to demonstrate the applicability of the proposed method.

Control of Neural Network Feedback Linearization Based on Chaotic Particle Swarm Optimization

A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The new algorithm combines the particle swarm algorithm and the chaotic optimization, using randomness and ergodicity of chaos to overcome the premature convergence of the particle swarm optimization. At the same time, a new neural network feedback linearization control system is built to control the single-machine infinite-bus system. The network parameters are trained by the chaos particle swarm algorithm, which makes the control achieve optimization and the control law of prime mover output torque obtained. Finally, numerical simulation and practical application validate the effectiveness of the method.

Optimal control of a spacecraft with deployable solar arrays using particle swarm optimization algorithm

The optimal control problem of the multibody dynamics of a spacecraft in space, modeled as a central body with one-sided connected deployable solar arrays, is investigated. The dynamical equations of motion of the spacecraft with solar arrays are derived using the multibody dynamics method. The control of the attitude motion of a spacecraft system can be transformed into the motion planning problem of nonholonomic system when the initial angular momentum is zero. These are then used to investigate the motion planning of the spacecraft during solar arrays deployment via particle swarm optimization (PSO) and results are obtained with the optimal control input and the optimal trajectory. The results of numerical simulation show that this approach is effective for the control problem of the attitude of a spacecraft during the deployment process of its solar arrays.