使用半工业化机械浮选机评估矿浆黏度变化对浮选气泡大小的影响

浮选过程中矿浆的黏稠度是由矿浆温度、矿粒浓度、矿粒细度等决定,它对浮选效率的影响一直受到工业界的极大重视。在实际生产中,一些自然因素和操作参数的变化,如季节性温度的浮动,矿石硬度、矿石性质的变化等产生的矿浆黏稠度的浮动,导致气泡尺寸和分布规律产生浮动,进而使选矿回收率等经济指标下滑。即便如此,在科研中矿浆黏稠度的相关研究并未受到重视。本研究的重点是黏稠度和气泡尺寸在浮选过程中的关系。试验采用半工业化美卓700 L机械浮选机和McGill大学独有的气泡观测仓,通过调整液体温度来改变黏稠度,在充分屏蔽其他浮选操作条件的情况下形成了气泡-黏稠度的关系图。结果显示了气泡尺寸D32和黏稠度（μ/μ20）之间呈现0.776的指数关系,有较强的关联性。本研究结果对实际生产中通过控制黏稠度来优化气泡尺寸,乃至浮选经济指标具有借鉴意义。     

Thickness and grain size monitoring in seamless tube-making process using laser ultrasonics

The seamless tube-making process often causes wall-thickness variations generally in a helical pattern along the tube length. A laser-ultrasonic system installed immediately after the final operation in tube making provides process monitoring. Tube wall thickness and temperature measurements guide the mill adjustments to achieve the desired tolerances. Using the same ultrasonic signals, additional functionality provides the ability to measure the size of austenite grains. A signal processing approach based on a single echo analysis is used for determining wall thickness and austenitic grain size in relatively thick materials. Discussions review challenges specific to on-line conditions such as limited signal-to-noise ratio. A statistical comparison with metallographic results shows that the laser-ultrasonic grain sizes determined on-line have at least the same accuracy.     

高变异药物平均生物等效性试验中两种重复交叉设计的样本量估计

目的: 探讨高变异药物生物等效性试验中监管部门推荐的两种重复交叉设计的样本量估计。方法: 在统计模型框架下推导两种重复交叉设计的方差,结合监管部门推荐的参比制剂校正方法,通过检验效能与样本量的关系得到样本量估计。同时编写了SAS程序以方便实际研究中的应用。结果: 两种重复交叉设计推导出的方差相等,结合EMA和FDA参比制剂校正方法估计样本量,EMA对高变异药物生物等效性研究需要的样本量在相同参数配置下比FDA指南需要的样本量大。CV等于30%时,EMA的样本量是连续变化的,而FDA的样本量是不连续的。当CV大于50%时,因为EMA采用了固定的等效性界值,所以样本量开始增加,而FDA则因为等效性界值继续放宽,因而样本量则变化不大。结论: 本文基于两种重复交叉设计,采用最佳线性无偏估计的方法推导的方差,结合监管部门要求的参比制剂校正方法算出的样本量估计具有严谨的数理统计基础,希望能给研究者进行高变异药物生物等效性研究时的样本量估计提供帮助。     

Selection of an optimum sample size for flatness error estimation while using coordinate measuring machine

In the present day manufacturing environments it is becoming increasingly important to be able to deliver quality products at the right time to the market at competitive costs. The quality, cost and time to market depend not only on the design and manufacturing but also on the inspection process adopted. Design specifications rely on extensive usage of form tolerances to ensure that the functionality of surfaces and features of the product are maximized. The use of the coordinate measuring machines (CMM) has greatly improved the efficacy of form tolerance measurement and is also used as the key device in this work. The focus of this work is to deal with the method and strategies for measurement of flatness error so as to be able to predict the flatness error accurately at reduced sample sizes in batch and mass production setups. Accurate evaluation of flatness will require large sample sizes which increase the cost and time of inspection and hence a need to reduce the sample sizes without compromising on the accuracy. In the absence of robust models that can predict the errors due to manufacturing processes, an alternative technique has been devised to arrive at a reduced sample size. The procedure involves using large sample data inspected on the first component as the basis for evolving smaller sample sizes for subsequent components.Experimental verification of the developed algorithm shows that flatness error can be predicted with sufficient accuracy at small sample sizes.