Electrical, Information Engineering and Mechatronics 2011

The grinding process plays a crucial role in industry, allowing for the reduction of particle sizes of raw materials and substances to the required fineness—either as a finished product or for further technological processes. The high demand for micro- and nanopowders or suspensions is associated with the high energy consumption of the milling process. Therefore, optimizing the milling process, including correctly selecting grinding media, is essential to reduce energy consumption. This article presents experimental studies of the grinding process of a model material (quartz sand) in a laboratory vibratory mill. Five sets of grinding media with different diameters were used in the research, and grinding was conducted for various durations. The studies showed that the vibratory grinding process is efficient for each set of grinding media and grinding durations. The research has shown that conducting studies on the proper selection of mills is beneficial, especially regarding very fine grinding of various materials. The study confirmed that properly selecting grinding media sets can significantly accelerate the grinding process. For the selected technological variant, it was demonstrated that using 15 mm grinding media, compared to 12 mm, resulted in a 22.5% reduction in grinding time to achieve a specified particle size class of 0–10 μm. 

The Influence of the Grinding Media Diameter on Grinding Efficiency in a Vibratory Ball Mill

Experiments of ultra-fine comminution by HPGR and separation on V-Ti magnetite in Panxi were done. When d80 of feeding is 15.5 mm, the yield of -3.2 mm for the roll grinding centre product is 91.05%, the yield of -0.074 mm is 15.29% and P80 of roll grinding products is reduced to 1.55 mm. The effect of circulation technology of edge product and closed-circuit on the size characteristics of crushed product was very obvious. Separating tests were performed on -3.2 mm Panxi V-Ti magnetite from ultra-fine comminution by HPGR using process of parallel separation of Fe and Ti. The results show when grind fineness is -0.074 mm 45%, Fe grade of iron concentrate is 55.05% and recovery is 70.64% in iron separation flowsheet, while TiO2 grade of titanium concentrate is 47.78% and recovery is 35.16% in titanium separation flowsheet at -0.074 mm 80%.

Influence of Ultra-fine Comminution by HPGR on Separation of V-Ti Magnetite in Panxi

In order to improve grinding effect of high pressure grinding rolls (HPGR), it is needed to optimize its performance parameters. According to nonlinear mapping function of artificial neural network and global optimal value search function of genetic algorithm (GA), this chapter, first, setted up the mathematical relationship between the given product size-reduction percentage M and performance parameters, such as working pressure P, roll speed V and original rolls gap S based on back propagation (BP) neural network, then, searched optimal performance parameters by GA. The result shows that using BP neural network with GA to optimize performance parameters of HPGR is accurate, fast and effective, which provides a novel approach for the selection of performance parameters.

Optimization of Performance Parameters of High Pressure Grinding Rolls

To reduce the calculation of signal sparse decomposition, put forward to use the hybrid algorithm, DEPSO which mixed the characteristics of particle swarm optimization (PSO) and difference optimization algorithm (DE), to complete the searching of the best atoms of signal sparse decomposition based on matching pursuit (MP) algorithm. This method not only helps to get the best matching parameters with high precision, but also avoids falling into the partial optimal solution which is common to single optimization algorithm. The result of experiment shows that, compared with the single algorithm, using DEPSO greatly improved the searching speed, efficiency and accuracy of decomposition. Besides, the calculation has also dropped down dramatically.

Application of Signal Sparse Decomposition Based on DEPSO

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Optimization of Performance Parameters of High Pressure Grinding Rolls

Application of Signal Sparse Decomposition Based on DEPSO