Pneumatic jigging: Influence of operating parameters on separation efficiency of solid waste materials.
This article aims to provide insights into the factors that contribute to the separation efficiency of solid particles. In this study, a pneumatic jigging technique was used to assess the separation of solid waste materials that consisted of copper, glass and rubber insulator. Several initial experiments were carried out to evaluate the strengths and limitations of the technique. It is found that despite some limitations of the technique, all the samples prepared for the experiments were successfully separated. The follow-up experiments were then carried out to further assess the separation of copper wire and rubber insulator. The effects of air flow and pulse rates on the separation process were examined. The data for these follow-up experiments were analysed using a sink float analysis technique. The analysis shows that the air flow rate was very important in determining the separation efficiency. However, the separation efficiency may be influenced by the type of materials used.
The effect of jigging time and air cycle on bed stratification in a pilot scale Baum jig
A highly instrumented and controlled pilot jig was constructed to study water motion behaviour or oscillation with the object of optimizing bed stratification of coal washing jigs. Durable synthetic material, colour coded for density, was used as the bed material in a series of 114 experiments. The bed was fully analysed layer by layer for size and density distribution. It was found that jigging time is a very significant parameter affecting the bed stratification. In general, bed stratification improves quickly up to a jigging time of 180 s, and thereafter more slowly. When the jigging time exceeds 300 s, the bed is in a state of dynamic equilibrium for material coarser than 8 mm; however, the finer particles (< 8mm) need more time to achieve such a state. To improve the bed stratification, operating parameters should be adjusted to generate suitable air and water behaviour in the jig. A cycle with a long inlet period (199-220 °), which is seldom used at present, appears to provide such behaviour. Improvement of bed stratification by using an appropriate air cycle will increase the separation efficiency and yield of product in coal washing jigs.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.