Mathematical modeling and computer simulation of the rotating impeller particle flotation process: Part II. Particle agglomeration and flotation

Effective removal of unwanted particles from a molten metal alloy by flotation relies on purging a gas into the melt through a rotating impeller. This device is commonly known as a rotary degasser. Unwanted particles in the melt attach to the rising gas bubbles and rise to the slag layer where they are removed from the metal bulk. In addition, the turbulence created by the rotating impeller causes the randomly distributed solid particles to agglomerate into relatively large clusters. These clusters float up or settle down due to the difference between their density and that of the melt. A mathematical model has been developed to describe the particle dynamics and particle agglomeration that occur during the rotary degassing of aluminum melts. While previous investigations addressed particle collisions in low intensity turbulent fields where the size of the colliding particles is smaller than the Kolmogorov length scale, this model is more encompassing as it considers both low intensity and high intensity turbulence. Consequently, this model is more representative of a typical industrial rotary degassing operation.     

Simple concepts for ion source improvement

We report on improvements in the overall intensity of a sputter ion source that evolved originally from an NEC MCSNICS. Beam output increases benefit both AMS measurements and nuclear physics experiments using low natural abundance beams. In particular, minor changes in source geometry suggested by a combination of electrostatic calculations and simple design principles have yielded increases in extracted negative ion intensity of nearly a factor of 4.     

Cluster analysis in industrial market segmentation through artificial neural network

Market segmentation has commonly applied cluster analysis. This study intends to make the comparison of conventional two-stage method with proposed two-stage method through the simulated data. The proposed two-stage method is the combination of self-organizing feature maps and K-means method. The simulation results show that the proposed scheme is better than the conventional two-stage method based on the rate of misclassification.     

Transformed Estimates of Densities of Heavy-Tailed Distributions and Classification

Nonparametric estimation of the density of a heavy-tailed probability distribution is investigated. The initial data are transformed to a bounded interval and the distribution density is determined by an inverse transformation of the distribution density estimate of transformed data. An adaptive data transformation is studied, in which the order of decay of the tail of the true distribution density is preserved and stable estimation of the deviation in tail index estimates is guaranteed. In classification, the empirical risk of erroneous classification by the Bayes empirical classifier is used as a measure for the quality of distribution density estimates.     

Creating and Justifying Research and Development Value: Scope,Scale, Skill and Social Networking of R&D

In this paper we describe a framework for analysing the creation and justification of Research & Development. The 4S framework is developed for analysing the scope, scale, skills and social network aspects of Research & Development value. The framework is based on social system theory, a process contingency model, and recent Research & Development metrics. We present a first empirical assessment based on a workshop using the 4S framework for leveraging Research & Development. Results that assist in the assessment of value creation utilising R & D within networks are very relevant in high tech industries. The multi–dimensional process approach of this framework seems promising for understanding and managing R&D value creation, but needs further operationalisation. Case studies are described and a Dutch network on leveraging R&D has been initiated.     

Analysis of intensity interference caused by cross-phase modulationin dispersive optical fibers

Under a set of broadly justified simplifying assumptions, an analytical approach to the problem of cross phase modulation in optical fibers is presented. Simple analytical expressions describing intensity interference caused by cross-phase modulation (XPM) are derived. It turns out that within a physically realizable range of parameters the power penalties induced by XPM have a linear dependence on the power of the interfering signal. Examples of specific systems are presented and discussed