Computer modeling of high-pressure leaching process of nickel laterite by design of experiments and neural networks

Due to the complex chemical composition of nickel ores, the requests for the decrease of production costs, and the increase of nickel extraction in the existing depletion of high-grade sulfide ores around the world, computer modeling of nickel ore leaching process be- came a need and a challenge. In this paper, the design of experiments （DOE） theory was used to determine the optimal experimental design plan matrix based on the D optimality criterion. In the high-pressure sulfuric acid leaching （HPSAL） process for nickel laterite in ＂Rudjinci＂ ore in Serbia, the temperature, the sulfuric acid to ore ratio, the stirring speed, and the leaching time as the predictor variables, and the degree of nickel extraction as the response have been considered. To model the process, the multiple linear regression （MLR） and response surface method （RSM）, together with the two-level and four-factor full factorial central composite design （CCD） plan, were used. The proposed re- gression models have not been proven adequate. Therefore, the artificial neural network （ANN） approach with the same experimental plan was used in order to reduce operational costs, give a better modeling accuracy, and provide a more successful process optimization. The model is based on the multi-layer neural networks with the back-propagation （BP） learning algorithm and the bipolar sigmoid activation function.

Design of Experiment (DoE): Implementation in Determining Optimum Design Parameters of Portable Workstation

In the modern era of manufacturing, it is important to optimize every design parameter in product development stage to reduce cost, material usage and to achieve the desired efficacy level. There are various models which serve those purposes, for instance, Design of Experiment (DoE) is used to check the parameters after adopting optimization tactics which results in reduced cost or saving operating time. In this regard, this research aims to construct a DoE model on a portable workstation to optimize its design parameters. The methodology of DOE would be a 2 level 3 factors full factorial DOE which is conducted to determine the optimal value for three design parameters (factors) which are material density, the length of the table and the length of the table stand in terms of the response which is the required time of fold ability function of the portable workstation. Based upon the evaluated interactions between the parameters, the optimized parameters are chosen for responses. Here, the resultant design parameters are at their lowest level, so the goal of time efficiency in fold ability function is achieved. This similar sort of DoE can be implemented in the furniture and other manufacturing industries who wish to optimize their material usage as well as increase efficiency and reduce cycle time.

光学玻璃磨削亚表面损伤预测模型及DOE实验设计

为了掌握光学玻璃材料杯型砂轮研磨与表面粗糙度(SR)和亚表面损伤(SSD)机理,本文建立BK7光学玻璃杯型砂轮研磨表面粗糙度的预测模型,通过改变磨削参数来研究对表面粗糙度的影响。设计DOE试验,研究影响SR与SSD的显著性特征因子,并分析了各因子的交互作用。实验结果表明预测模型的可靠性,得到表面粗糙度的预测模型数据与实验数据的平均误差为5.47%。采用角抛光法,通过电子显微镜观测表面裂纹,并测量裂纹的深度。最后,基于Li的模型,建立基于磨削工艺参数的亚表面损伤的新预测模型。实验结果表明:实验和预测模型结果具有很好的一致性,模型数据与实验数据的平均误差为6.19%,并且新预测模型结果要优于Li的模型。

Application of quality by design in the current drug development

Quality by Test was the only way to guarantee quality of drug products before FDA launched current Good Manufacturing Practice. To clearly understand the manufacture processes, FDA generalized Quality by Design(QbD) in the field of pharmacy, which is based on the thorough understanding of how materials and process parameters affect the quality profile of final products. The application of QbD in drug formulation and process design is based on a good understanding of the sources of variability and the manufacture process. In this paper,the basic knowledge of QbD, the elements of QbD, steps and tools for QbD implementation in pharmaceutics field, including risk assessment, design of experiment, and process analytical technology(PAT), are introduced briefly. Moreover, the concrete applications of QbD in various pharmaceutical related unit operations are summarized and presented.

Applications of quality by design(QbD) and its tools in drug delivery

Quality by Test (Qb T) was the only way to guarantee the quality of drug products before FDA launches current Good Manufacturing Practice (c GMP)[1], which is an approach without clear understanding of the processes. In order to solve this problem,FDA generalized Quality by Design (QbD) in the field of pharmacy (2)In pharmaceutical industry, Qb D brings cost-efficiency and simplicity of manufacturing process into reality.

Design of experiment study on hardness variations in friction stir welding of AM60 Mg alloy

Identification of process parameters,their effects and contributions to the outcomes of the system using experimental approach could be a daunting,time consuming,and costly course.Using proper statistical methods,i.e.,Taguchi method,could significantly reduce the number of required experiments and statistical significance of the parameter can be identified.Friction stir welding is one of those welding techniques with many parameters which have different effects on the quality of the welds.In friction stir welding the tool rotational speed(RPM)and transverse speed(mm/min)influence the strength(i.e.,hardness distribution)of the stirred zone.In this study,these two factors are investigated to determine the effect they will have on the hardness in the stirred zone of the friction stir welds and how the two factors are related to one another for as-cast magnesium alloy AM60 with nominal chemical composition of Mg-(5.5-6.5)Al-(0.24-0.6)Mn-0.22Zn-0.1Si.Experimental data was taken at three different tool rotational speeds and three different transverse speeds.The data obtained was then analyzed using a 32 factorial design to find the contribution of these parameters.It was determined that both tool rotational speed and transverse speed possess significant effects on the stir zone hardness.Also,the interactions between the two factors were statistically assessed.

Analytical quality-by-design approach for sample treatment of BSA-containing solutions

The sample preparation of samples conlaining bovine serum albumin（BSA）,e.g..as used in transdermal Franz diffusion cell（FDC） solutions,was evaluated using an analytical qualily-by-design（QbD）approach.Traditional precipitation of BSA by adding an equal volume of organic solvent,often successfully used with conventional HPLC-PDA,was found insufficiently robust when novel fused-core HPLC and/or UPLC-MS methods were used.In this study,three factors（acetonitrile（%）.formic acid（%） and boiling time（min）） were included in the experimental design to determine an optimal and more suitable sample treatment of BSAcontaining FDC solutions.Using a QbD and Derringer desirability（D） approach,combining BSA loss,dilution factor and variability,we constructed an optimal working space with the edge of failure defined as D〈0.9.The design space is modelled and is confirmed to have an ACN range of 83 ± 3% and FA content of 1 ±0.25%.

Optimizing Bucket Elevator Performance through a Blend of Discrete Element Method, Response Surface Methodology, and Firefly Algorithm Approaches

This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization algorithms.Specifically,the study employs the firefly algorithm(FA),a metaheuristic optimization technique,to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions.The experimental methodology involves several key steps:screening experiments to identify significant factors affecting bucket elevator operation,central composite design(CCD)experiments to further explore these factors,and response surface methodology(RSM)to create predictive models for transport mass and mass flow rate discharge.The FA algorithm is then applied to optimize these models,and the results are validated through simulation and empirical experiments.The study validates the optimized parameters through simulation and empirical experiments,comparing results with DEM simulation.The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters,showcasing less than 10%and 15%deviation for transport mass and mass flow rate discharge,respectively,between predicted and actual values.Overall,this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters,contributing to more efficient material handling in various industrial applications.

Bayesian Variable Selection for Mixture Process Variable Design Experiment

This paper discussed Bayesian variable selection methods for models from split-plot mixture designs using samples from Metropolis-Hastings within the Gibbs sampling algorithm. Bayesian variable selection is easy to implement due to the improvement in computing via MCMC sampling. We described the Bayesian methodology by introducing the Bayesian framework, and explaining Markov Chain Monte Carlo (MCMC) sampling. The Metropolis-Hastings within Gibbs sampling was used to draw dependent samples from the full conditional distributions which were explained. In mixture experiments with process variables, the response depends not only on the proportions of the mixture components but also on the effects of the process variables. In many such mixture-process variable experiments, constraints such as time or cost prohibit the selection of treatments completely at random. In these situations, restrictions on the randomisation force the level combinations of one group of factors to be fixed and the combinations of the other group of factors are run. Then a new level of the first-factor group is set and combinations of the other factors are run. We discussed the computational algorithm for the Stochastic Search Variable Selection (SSVS) in linear mixed models. We extended the computational algorithm of SSVS to fit models from split-plot mixture design by introducing the algorithm of the Stochastic Search Variable Selection for Split-plot Design (SSVS-SPD). The motivation of this extension is that we have two different levels of the experimental units, one for the whole plots and the other for subplots in the split-plot mixture design.

Performance Analysis and Improvement of Flat Torque Converters Using DOE Method

Automotive torque converters have recently been designed with an increasingly narrower profile for the purpose of achieving a smaller axial size and reducing weight. Design of experiment(DOE) and computational fluid dynamics(CFD) techniques are applied to improve the performance of a flat torque converter. Four torque converters with different flatness ratios(0.204, 0.186, 0.172, and 0.158) are designed and simulated first to investigate the effects of flatness ratio on their overall performance, including efficiency, torque ratio, and impeller torque factor. The simulation results show that the overall performance tends to deteriorate as the flatness ratio decreases. Then a parametric study covering six geometric parameters, namely, inlet and outlet angles of impeller, turbine, and stator is carried out. The results demonstrate that the inlet and outlet angles play an important role in determining the performance characteristics of a torque converter. Furthermore, the relative importance of the six design parameters is investigated using DOE method for each response(stall torque ratio and peak efficiency). The turbine outlet angle is found to exert the greatest influence on both responses. After DOE analysis, an optimized design for the flat torque converter geometry is obtained. Compared to the conventional product, the width of the optimized flat torque converter torus is reduced by about 20% while the values of stall torque ratio and peak efficiency are only decreased by 0.4% and 1.7%, respectively.The proposed new optimization strategy based on DOE method together with desirability function approach can be used for performance enhancement in the design process of flat torque converters.

基于DOE的加工汽车碳纤维材料的新型钻头设计与优化

针对加工汽车碳纤维材料的钻头寿命短、加工质量差的问题,设计了一种新型钻头。通过DOE(design of experiment)试验方法,给出了钻头齿数、前角和后角等3个参数的全因子试验方案。利用MINITAB软件对试验数据进行回归分析,并利用响应优化获得最优设计参数。结果表明:齿数和后角对钻孔个数及钻头寿命影响不显著,前角对钻头寿命有显著影响,后角与前角的交互作用对钻头寿命也有显著影响。齿数、前角和后角对第一孔钻孔时间及钻孔速度有显著影响,齿数与后角的交互作用以及前角与后角的交互作用对钻孔速度有显著影响。通过试验测试验证优化后的最佳设计参数是有效合理的。

Combined Size and Shape Optimization of Structures with DOE,RSM and GA

In this paper,size and shape optimization problem of a machine gun system is addressed with an efficient hybrid method,in which a novel and flexible mesh morphing technique is employed to achieve fast parameterization and modification of complexity structure without going back to CAD for reconstruction of geometric models or to finite element analysis（ FEA） for remodeling. Design of experiments（ DOE） and response surface method（ RSM） are applied to approximate the constitutive parameters of a machine gun system based on experimental tests. Further FEA,secondary development technique and genetic algorithm（ GA） are introduced to find all the optimal solutions in one go and the optimal design of the demonstrated machine gun system is obtained. Results of the rigid-flexible coupling dynamic analysis and exterior ballistics calculation validate the proposed methodology,which is relatively time-saving,reliable and has the potential to solve similar problems.

Research on Design Optimization Strategy in Virtual Product Development

Simulation and optimization are the key points of virtual product development (VPD). Traditional engineering simulation software and optimization methods are inadequate to analyze the optimization problems because of its computational inefficiency. A systematic design optimization strategy by using statistical methods and mathematical optimization technologies is proposed. This method extends the design of experiments (DOE) and the simulation metamodel technologies. Metamodels are built to in place of detailed simulation codes based on effectively DOE, and then be linked to optimization routines for fast analysis, or serve as a bridge for integrating simulation software across different domains. A design optimization of composite material structure is used to demonstrate the newly introduced methodology.

基于DOE的电机多目标多变量快速参数化设计分析

以一台9槽6极洗碗机用水泵电机为例,选取了绕组匝数、铁心高度、槽口大小、气隙直径、气隙大小、定子齿宽、定子齿高为变量,以交直轴电感、反电动势为优化目标,通过基于DOE的多目标多变量的电机快速参数化设计优化方法,实现了通过部分析因实验设计完成了常规参数化扫描最少2 187个节点的设计目标,大大节约了项目资源,提高了设计人员的设计效率,缩短了项目的交付周期。给出了各变量与优化目标间的定量关系,提高了设计人员对产品的理解以及经验设计和理论设计的水平。

基于变截面积流道的液力变矩器叶栅系统设计

流道截流面积分布对液力旋转机械的液力性能具有重要影响.为了减少扩散等液力损失,变矩器流道截面积分布通常采用等截面积分布的形式,但并未充分发挥截面积分布参数在流场调节和性能优化方面的优势.因此,文中研究提出了过流截面积表征模型,并建立了基于可变截面积分布的变矩器循环圆及叶栅设计系统,对变截面积流道的液力变矩器循环圆设计方法进行了研究.通过设计多组不同截面积分布的实例,采用DOE方法进行比较分析,进一步研究了叶轮截面积分布形式对变矩器性能的影响.同时,通过对不同叶轮的组合设计,获得了组合型性能优化的循环圆设计结果.实验结果表明,基于变截面积流道优化设计的变矩器泵轮转矩提升了18.5%,起动变矩比也提高了3.9%.说明基于变截面积分布的液力变矩器循环圆设计方法是一种有效的优化设计方法,能够在保持外形尺寸的同时提升变矩器性能.

基于DOE和改进模拟退火算法的消声器优化设计

为提高消声器的优化效率,在考虑流速对消声器传递损失数值建模的基础上,采用试验设计(DOE)中的拉丁超立方设计对消声器参数进行分析,结合改进模拟退火算法分别建立消声器在排气噪声峰值频率处传递损失的单目标和多目标优化模型,开展消声器的优化设计研究。研究结果表明:DOE方法能有效辨识出各参数对消声器传递损失贡献度的大小,简化了消声器的优化模型。消声器内气体的平均流速对消声器的优化结果影响较大。单目标优化可使相应峰值频率处的传递损失达到最大值,多目标优化能使全频段整体优化效果较好,优化设计后排气噪声最大降低31.73d B,优于单目标优化结果。研究结果为消声器的优化设计提供了新思路。

纯电动环卫车电池托架的轻量化设计

轻量化是提升纯电动环卫车续航里程及工作时间的重要方式,其中电池托架是承载电池模组的重要结构部件。为探究更有效的构件布置方式,建立电池托架的有限元模型,并使用拓扑优化方法得到改进结构,使电池托架在各工况下的位移和应力显著下降。为进一步减轻质量,采用Hammersley试验方法对模型中各构件的壁厚进行采样;基于采样数据,使用2种策略的自适应响应面法对主要构件进行尺寸优化,在满足应力及位移的许用要求下,得到优化设计方案,质量减轻40%,实现了轻量化目标。

基于DOE及RSM的液力变矩器叶片数对性能的影响及优化

为解决一维束流理论难以有效地对液力变矩器叶片数进行优化的问题,建立液力变矩器三维流动设计分析平台,利用实验设计方法,研究了各叶片数对液力变矩器性能的影响,并在响应曲面基础上对叶片数进行了优化.结果表明,泵轮、涡轮叶片数对最高效率和起动转矩比有较大影响,泵轮、导轮叶片数对泵轮扭矩系数有较大影响.优化后,液力变矩器各性能指标均有提高.

DOE方法在焊接工艺研究中的应用现状

焊接工艺参数直接影响着焊缝成形及接头性能的好坏。由于焊接过程涉及的参数较多,采用传统实验方法研究往往不够现实。将实验设计DOE(Design of Experiment)等优化设计方法应用于焊接工艺研究,通过科学严谨的实验设计,建立相关数学模型,找出焊接工艺参数与焊缝质量参数间的影响关系,实现焊接过程优化设计和焊接主要工艺参数组合预测,从而获得良好的焊缝成形和接头性能,对于焊接试验研究和焊接实际生产过程优化都具有重要意义。着重介绍了国内外DOE方法在焊接过程实验设计和焊缝质量最优化等方面的研究应用和进展。

基于DOE和神经网络的增压柴油机实时模型建模方法研究

基于一维仿真物理模型,研究了发动机实时模型的建模理论和方法。将发动机划分为进排气管路、中冷器、涡轮增压器和气缸四个子系统,分别探讨了各个子系统的建模方法,并重点研究了利用DOE和人工神经网络构建发动机气缸模型的方法。在此基础上,以道依茨BFM1015增压中冷柴油机为对象,对该方法进行了验证。研究结果表明:与传统的面向控制的平均值模型相比,运用新方法建立的面向控制的发动机实时模型误差小于5%,具有精度高、对试验数据依赖低等特点。