On the optimization of froth flotation by the use of an artificial neural network

A multi layered, feed forward Artificial Neural Network (ANN) was used to study the effect of feed mean size, collector dosage and impeller speed on flotation recovery and grade. The results of 30 flotation experiments conducted on Jordanian siliceous phosphate were used for training the network while another 10 experiments were used for validation. Simulation results showed that a four layer network with a [9 11 5 9 2] architecture was the one that gave the least mean squared error (MSE). Using this ANN to optimize the flotation process showed that the optimum flotation parameters were 321.28 μm for the feed mean size, 0.7354 kg/TOF for the collector dosage and 1225.25 RPM for the impeller speed. Studying the effect of these parameters on flotation recovery and grade was done by analysis of variance, ANOVA. The results showed that grade was more sensitive to changes in flotation parameters than was recovery. They also showed that changes in collector dosage had a more significant effect on flotation grade and recovery than did changes in feed mean size or impeller speed.

Noise Detection in the Photogrammetric Images Using Wavelets

The paper presents the method of the valuation of random noise in the photogrammetric images, based on wavelets. The proposed method involves the analysis of the dynamics of the components of wavelet decomposition on several resolution levels. The hypothesis was made that the noise-free images are characterized by systematically growing variances of the single components with growing decomposition. This hypothesis was. studied on several dozen fragments of airborne images recorded both with a photogrammetric analogue camera and digital camera. For all the studied photos taken with a digital camera, the hypothesis of growing variances of details was confirmed. The images from an analogue camera had different dynamics of variance, and the cause was recognized as random noise, caused by the grains from of the photographs. Referring to earlier applications of wavelets to noise evaluation, the proposed method is characterized by smaller dependence upon the structure and texture of the image.

Vibration induced flow in hoppers:DEM 2D polygon model

A two-dimensional discrete element model （DEM） simulation of cohesive polygonal particles has been developed to assess the benefit of point source vibration to induce flow in wedge-shaped hoppers. The particle-particle interaction model used is based on a multi-contact principle. The first part of the study investigated particle discharge under gravity without vibration to determine the critical orifice size （Bc） to just sustain flow as a function of particle shape. It is shown that polygonal-shaped particles need a larger orifice than circular particles. It is also shown that Bc decreases as the number of particle vertices increases. Addition of circular particles promotes flow of polygons in a linear manner. The second part of the study showed that vibration could enhance flow, effectively reducing Bc. The model demonstrated the importance of vibrator location （height）, consistent with previous continuum model results, and vibration amplitude in enhancing flow.