Multi-response non-parametric profiling using Taguchi's qualimetric engineering and neurocomputing methods: Screening a foaming process in a solar collector assembly

Taguchi's data programming techniques in synergy with data analysis tactics based on artificial neural networks have been fruitful in illuminating intricate manufacturing phenomena. We present a non-parametric approach to treat multi-response multi-factorial datasets created with Taguchi's orthogonal-array samplers. Replicated response datasets are compressed utilizing the signal to noise ratio and then they are homogenized by simple rank-ordering. The multiple response layout is reduced to the more tractable uni-response arrangement by using the super-ranking concept to enact the fusing of the individual responses. The ‘ranked-and-fused’ dataset is subjected to conversion by linear and three-layer perceptrons. The performance of a group of examined effects is assessed according to the perceptrons’ sensitivity analysis output. Using Wilcoxon's one-sample test, statistical significance is assigned to the accumulated ranking scores obtainable from a series of independent perceptron runs. We discuss the efficiency status for each of the two engaged perceptron options on affecting prediction accuracy as well as the influence of data fusion on the SNR-compressed datasets. Our robust neurocomputing solver is elucidated in a concurrent screening of three foam characteristics which are encountered in popular solar-collector assembly operations. Seven controlling factors were profiled and it was found that the temperature of the polyol additive is the sole statistically predominant effect. Finally, through our industrial paradigm, we illustrate the superiority of the fusing principle for downsizing stochastically multiple characteristics and thus gaining faster and more accurate perceptron predictions. We show that the proposed method outperforms the outcomes obtained by the desirability analysis. We identify the points of superiority to the crisper resolution in locating effect dominance accompanied with recovered stochastic significance.     

泵站机组测温系统原理及常见故障分析

泵站机组测温系统是监视、保护机组机电部分安全运行的重要系统。测温热电阻的测量原理是测温热电阻值随测点温度变化而变化,根据反馈电压的大小,测量出热电阻阻值,从而计算出测点温度,测温热电阻的常见故障是热电阻的短路和断路,有效地检测测温系统中经常出现的故障并及时处理,是泵站安全运行的有力保障。     

Role of cement content on the properties of self-flowing Al2O3 refractory castables

In this work, Al₂O₃ self-flowing castables (SFCs) were produced based on various cement contents. The SFCs were sintered at 1273 K, 1573 K and 1773 K and the exhibited properties were experimentally determined. Among the properties determined in this work are bulk density (BD), apparent porosity (AP), water absorption (WA), cold crushing strength (CCS), modulus of rupture (MOR) and fracture toughness (K_IC). It is found that additions of 5% cement lead to SFCs with maximum MOR and K_IC values after firing at 1773 K. Firing at 1573 K leads to a reduction in both, MOR and K_IC. In SFC containing 3% cement, maximum K_IC values of 3.53 MPa m^1/2 were achieved after firing at 1573 K. In the low cement SFCs (1 wt%) after firing at 1773 K the exhibited K_IC values were below those obtained in either the SFC-3 or SFC-5, but they were significantly high (3.43 MPa m^1/2).     

黄铜矿型CuCrS2电子结构的第一性原理计算

利用基于密度泛函理论的第一性原理研究了黄铜矿CuCrS2的电子结构。结果表明:CuCrS2是一种半金属铁磁体,其半金属隙为0.18eV。每个CuCrS2分子的磁矩为3.00μB,Cr原子是分子磁矩的主要贡献者。晶胞体积变化时,各原子磁矩呈现不同的变化趋势,但Cr原子仍为分子磁矩的主要来源,晶胞体积增大5%和10%时,CuCrS2依然能表现出良好的半金属性。     

Combined Application of Experimental Design and Artificial Neural Networks in Modeling and Characterization of Spray Drying Drug: Cyclodextrin Complexes

The aim of this study was to investigate the usefulness of combined application of quality by design tools such as central composite design (CCD), response surface methodology (RSM), and artificial neural networks (ANN) in the characterization, modeling, and optimizaton of spray drying of a poorly soluble drug : cyclodextrin complex. Models were developed by RSM and ANN from different pools of data. The model with best predictability was the ANN multilayer perceptron (MLP)1 model developed from the largest group of data (R ² for response yield 0.854, moisture content 0.886). On the other hand, analysis of equations derived from the application of RSM contributed in better understanding the complex relationships between input and output variables. By application of a desirability function approach, optimal process parameters that resulted in the best process yield (86%) and minimal moisture content in the powder (3.3%) were established (25% feed concentration, 180°C inlet air temperature, 10% pump speed).     

Numerical simulation of particle formation in the rapid expansion of supercritical solution process

In this paper, we numerically study particle formation in the rapid expansion of supercritical solution (RESS) process in a two dimensional, axisymmetric geometry, for a benzoic acid + CO₂ system. The fluid is described by the classical Navier–Stokes equation, with the thermodynamic pressure being replaced by a generalized pressure tensor. Homogenous particle nucleation, transport, condensation and coagulation are described by a general dynamic equation, which is solved using the method of moments. The results show that the maximal nucleation rate and number density occurs near the nozzle exit, and particle precipitation inside the nozzle might not be ignored. Particles grow mainly across the shocks. Fluid in the shear layer of the jet shows a relatively low temperature, high nucleation rate, and carries particles with small sizes. On the plate, particles within the jet have smaller average size and higher geometric mean, while particles outside the jet shows a larger average size and a lower geometric mean. Increasing the preexpansion temperature will increase both the average particle size and standard deviation. The preexpansion pressure does not show a monotonic dependency with the average particle size. Increasing the distance between the plate and the nozzle exit might decrease the particle size. For all the cases in this paper, the average particle size on the plate is on the order of tens of nanometers.     

格拉斯霍夫数物理意义的种解释方法简

格拉斯霍夫数是将影响对流传热膜系数因素进行量纲分析得到的无量纲数。国内外几乎所有《化工原理》教材均将其解释为表征自然对流的影响,但解释过程均较为模糊。本文以一垂直平板与大空间液体的自然对流传热过程为例,将流体在垂直平板附近的热边界层内流动等效到圆管内流动,直接用泊稷叶公式计算出热边界层内流体流动为层流时的环流速度,取等效圆管的当量直径为特征尺寸,明确得到格拉斯霍夫数是雷诺数的一种变形,表征自然对流时惯性力与黏性力之比。     

工程材料及机械制造基础课程立体化教学与改革的探讨

本文阐述了工程材料及机械制造基础课程整合与课程教学改革的原因、改革的措施。从结果上看,激发和提高了学生的学习兴趣,加强了理论联系实际和工程应用能力培养,这一课程整合与教学改革达到了目标,取得了良好的实践效果,探索了该课程立体化教学和教学实践改革的思路与方法。     

化工原理课教学方式的改革与探索

为调动学生自主学习兴趣,培养其解决工程实际问题的能力和创新能力,我们对化工原理课程(理论课、实验课和课程设计)的授课方式等进行了改进,即在教师的配备、教学内容及时间的设置上,进行了改进,并提出了改进的具体措施,取得了成效。     

基于“卓越计划”的应用型人才培养模式改革——以化学工程与工艺专业为例

"卓越计划"是教育部贯彻教育规划纲要精神启动的一项重大改革计划,以培养创新能力强、适应经济社会发展需要的高质量各类型工程技术人才为目标。本文以教育部试点专业为基础,按照化工卓越工程师培养的目标要求,建立以培养学生工程实践能力为目标的培养方案,以工程教育理念为核心,课程优化整合、教学改革为重点,采取走出去、请进来等多种形式实施"卓越计划",取得了显著效果。