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The influence of micro-morphology and micro-structure on fly ash triboelectrostatic beneficiation

Wang Wenping, Chen Yinghua, Haisheng Li, Dong Haoran, Wang Guanghui

Physicochemical Problems of Mineral Processing

2021

Vol. 57, iss. 1

53--63

Fly ash is a complex system with a variety of fine particles. The complex relationship between unburned carbon and ash particles has an important influence on the efficiency of fly ash triboelectrostatic beneficiation. The particles adhered to the two electrode plates are collected through the triboelectrostatic beneficiation experiment. The scanning electron microscopy and X-ray fluorescence are used to detect the microscopic differences between the particles of positive and negative plates. The results show that the flaky carbon particles in the raw ash and the ash particles larger than 4µm are more easily separated, while it is converse for the ash particles with particle size less than 4µm. With the particle size less than 4µm, it is gradually more obvious for the influence of adhesion caused by the roughness surface of spherical unburned carbon particles, and the surface pores structure of porous carbon particles. The binding structure between unburned carbon and ash particles is complex and changeable. It is not beneficial to improve the separation efficiency. Therefore, the micro-structure and micro-morphology have an important effect on fly ash triboelectrostatic beneficiation. Some suggestions were proposed from the microscopic point to improve the efficiency of fly ash triboelectrostatic beneficiation.

Optimization of soft-sensing model for ash content prediction of flotation tailings by image features tailings based on GA-SVMR

Wang Guanghui, He Ting, Kuang Yali, Lin Zhe

Physicochemical Problems of Mineral Processing

2020

Vol. 56, iss. 4

590--598

Ash content is one of the most important properties of coal quality and the ash prediction of coal slurry in floatation is urgent and important for automation of the floatation process. The aim of this paper is to propose a method of ash content prediction for flotation tailings by the use of image analysis. The mean gray value, energy, skewness and coal slurry concentration are highly correlated with coal slurry ash content by correlation analysis based on experiments while the particles’ size has little effect on the ash. Single variable linear prediction model between coal ash content and mean gray value was developed by the LS and its prediction errors were below 7%. For improving the prediction results, an ash prediction model based on GA-SVMR was established with additional three input parameters: energy, skewness, coal slurry concentration. This model has a higher accuracy with predictive errors all below 5% and 80% of them less than 3%. Results indicate that GA-SVMR model has a higher precision compared with LS model and PSO-SVMR model and soft-sensing model based on image features of the slurry can be used as a new method for ash detection of floatation tailings in automatic control process of coal flotation.