Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

Thermal destratification of cryogenic liquid storage tanks by continuous bubbling of gases

This paper focuses on thermal destratification and pressurisation inside thermally stratified storage tanks by continuous gas bubbling. The primary purpose of doing these studies is to better understand the effect of bubble dynamics on thermal destratification and quantify the extent of destratification. The volume of fluid and interface compression method of OpenFOAM CFD code is utilised for the present analysis. Different values of inlet gas velocities (V_g), orifice diameters (d_o), and arrangement of the orifices in triangular and square fashion with different pitches (p/d_o) are considered. In addition, the effect of gravitational forces (g/g_e) on thermal destratification is also reported. For all these cases, the effectiveness of thermal destratification is quantified in terms of a newly defined parameter, the destratification index (I_d). For V_g = 1 m/s, the I_d value is maximum compared to lower V_g values. It is seen that when the gas velocity increased from 0.3 m/s to 1.0 m/s, the average effectiveness in thermal destratification (I_davg) and pressure at the ullage increased by 44.38% and by 64.81%, respectively. The I_davg and pressure at ullage increased by 96.29% and 14.91%, respectively, when the g/g_e ratio changed from 0.3 to 3. Compared to the triangular arrangement with p/d_o = 10, the calculated I_davg increased by 30.67% when gas inlets were arranged with a square pitch of 10. For p/d_o = 4, 6 and 8, the increments in I_davg are of the order of 12.86%, 19.43% and 21.92%, respectively, for gas inlets arranged in a square fashion as compared to the triangular arrangement. It is found that continuous bubbling with gas inlets arranged in square pitch p/d_o = 10 gives higher effectiveness in thermal destratification. Thus, by these studies, one can develop a thermal destratification mechanism with continuous bubbling for optimum performance. Also, these studies give an overall idea of sparger design for getting the correct gas flow rate for thermal destratification within the cryogenic liquid storage tanks.     

Development of a stakeholder identification and analysis method for human factors integration in work system design interventions – Change Agent Infrastructure

In any work system design intervention—for example, a physical workplace re-design, a work process change, or an equipment upgrade—it is often emphasized how important it is to involve stakeholders in the process of analysis and design, to gain their perspectives as input to the development, and ensure their future acceptance of the solution. While the users of an artifact or workplace are most often regarded as being the most important stakeholders in a design intervention, in a work-system context there may be additional influential stakeholders who influence and negotiate the design intervention's outcomes, resource allocation, requirements, and implementation. Literature shows that it is uncommon for empirical ergonomics and human factors (EHF) research to apply and report the use of any structured stakeholder identification method at all, leading to ad-hoc selections of whom to consider important. Conversely, other research fields offer a plethora of stakeholder identification and analysis methods, few of which seem to have been adopted in the EHF context. This article presents the development of a structured method for identification, classification, and qualitative analysis of stakeholders in EHF-related work system design intervention. It describes the method's EHF-related theoretical underpinnings, lessons learned from four use cases, and the incremental development of the method that has resulted in the current method procedure and visualization aids. The method, called Change Agent Infrastructure (abbreviated CHAI), has a mainly macroergonomic purpose, set on increasing the understanding of sociotechnical interactions that create the conditions for work system design intervention, and facilitating participative efforts.     

Effect of slot-die configurations on coating gap dependence of maximum and minimum wet thicknesses

The dependence of the maximum and minimum wet thicknesses on the coating gap is derived for the slot-die coating process, under different slot-die configurations. Analytical expressions for the wet thickness and its derivative with respect to the coating gap are obtained using a simple flow model. The results indicate that, as expected, the minimum wet thickness increases linearly with the coating gap; however, the maximum wet thickness demonstrates a counterintuitive trend of decreasing as the coating gap increases, when a specific slot-die configuration is assumed. Moreover, the results are also validated by numerically solving the complete two-dimensional (2D) Navier–Stokes equation.     

PID Control of Planar Nonlinear Uncertain Systems in the Presence of Actuator Saturation

This paper investigates PID control design for a class of planar nonlinear uncertain systems in the presence of actuator saturation. Based on the bounds on the growth rates of the nonlinear uncertain function in the system model, the system is placed in a linear differential inclusion. Each vertex system of the linear differential inclusion is a linear system subject to actuator saturation. By placing the saturated PID control into a convex hull formed by the PID controller and an auxiliary linear feedback law, we establish conditions under which an ellipsoid is contractively invariant and hence is an estimate of the domain of attraction of the equilibrium point of the closed-loop system. The equilibrium point corresponds to the desired set point for the system output. Thus, the location of the equilibrium point and the size of the domain of attraction determine, respectively, the set point that the output can achieve and the range of initial conditions from which this set point can be reached. Based on these conditions, the feasible set points can be determined and the design of the PID control law that stabilizes the nonlinear uncertain system at a feasible set point with a large domain of attraction can then be formulated and solved as a constrained optimization problem with constraints in the form of linear matrix inequalities (LMIs). Application of the proposed design to a magnetic suspension system illustrates the design process and the performance of the resulting PID control law.      

基于卷积神经网络的5G蜂窝网络无线定位方法

5G蜂窝网络发展迅猛，其覆盖面积将逐渐增大，因此使用5G蜂窝网络进行定位是有研究潜力的研究方向。本文提出一种新的深度学习技术来实现高效、高精度和低占用的定位，以代替传统指纹定位过程中繁重的指纹库生成以及距离计算。该方法建立了一个特殊的卷积神经网络，并根据5G天线信号的接收信号强度指示、相位和到达角等特征量，选择合适的输入数据格式构造样本组建训练集，对该卷积神经网络进行训练。训练得到的卷积神经网络可以替代指纹定位中的庞大指纹库，非常有利于直接在5G移动设备端实现定位。虽然卷积神经网络在训练过程中需要大量时间，但在训练完毕后直接进行分类定位的速度非常快，可以保障定位实现的实时性。本文所实现的卷积神经网络权重与偏置所占内存不到0.5 MB，且能够在实际应用环境中以95%的定位准确率以及0.1 m的平均定位精度实现高精度定位。     

Identification and U-control of a state-space system with time-delay

This article presents a state-space model with time-delay to map the relationship between known input-output data for discrete systems. For the given input-output data, a model identification algorithm combining parameter estimation and state estimation is proposed in line with the causality constraints. Consequently, this article proposes a least squares parameter estimation algorithm, and analyzes its convergence for the studied systems to prove that the parameter estimation errors converge to zero under the persistent excitation conditions. In control system design, the U-model based control is introduced to provide a unilateral platform to improve the design efficiency and generality. A simulation portfolio from modeling to control is provided with computational experiments to validate the derived results.