Recent advances in beneficiation for low rank coals

Fine coal dry cleaning using a vibrated gas-fluidized bed

Experimental and numerical simulation studies of the fluidization characteristics of a separating gas–solid fluidized bed

Air dense medium fluidized bed (ADMFB) offers a better alternative approach for dry coal beneficiation. In recent years, there has been a rapid advancement in the understanding of fluidized bed behavior and a great deal of work has been done to make this process competitive with conventional wet beneficiation processes. At the same time, much information is available in the general technical literature concerning various aspects of ADMFB; however, this is often uncoordinated information and widely dispersed. So, it is necessary to provide the adequate information systematically, which offers researchers an effective and easy way to obtain specific details. This review critically evaluates the understanding of different operating parameters that affect the cleaning performance of this equipment. Then, following the discussion of different operating parameters, we describe three recent practical developments in ADMFB: the dual-density fluidized bed, vibrated fluidized bed, and magnetically stabilized fluidi...

Air Dense Medium Fluidized Bed for Dry Beneficiation of Coal: Technological Challenges for Future

Recognizing and distinguishing coal and gangue are essential in engineering, such as in coal-fired power plants. This paper employed a convolutional neural network (CNN) to recognize coal and gangue images and help segregate coal and gangue. A typical workflow for CNN image recognition is presented as well as a strategy for updating the model parameters. Based on a powerful trained image recognition model, VGG16, the idea of transfer learning was introduced to build a custom CNN model to solve the problems of massive trainable parameters and limited computing power linked to the building of a brand-new model from scratch. Two hundred and forty coal and gangue images were collected in a database, including 100 training images and 20 validation images for each material. A recognition accuracy of 82.5% was obtained for the validation images, which demonstrated a decent performance of our model. According to the analysis of parameter updating in the training process, a principal constraint for obtaining a higher recognition accuracy mainly resided in a shortage of training samples. This model was also used to identify photos from a washing plant stockpiles, which verified its capability of dealing with field pictures. CNN combined with the transfer learning method we used can provide fast and robust coal/gangue distinction that does not require harsh data support and equipment support. This method will exhibit brighter prospects in engineering if the target image database (as with the coal and gangue images in this study) can be further enlarged.

https://www.mdpi.com/1996-1073/12/9/1735/pdf

Image Recognition of Coal and Coal Gangue Using a Convolutional Neural Network and Transfer Learning

Density-dependent separation of dry fine coal in a vibrated fluidized bed

Fine coal dry beneficiation using autogenous medium in a vibrated fluidized bed

Dry cleaning of fine lignite in a vibrated gas-fluidized bed: Segregation characteristics

A 40–60 t/h modularized dry coal beneficiation process with a novel method to control the bed was designed around a gas-solid fluidized bed separator. Furthermore, the hydrodynamics of medium-solids consisting of wide-size-range magnetite powder (0.3–0.06 mm) and <1 mm fine coal were numerically studied. The simulation results show that the fluidization performance of the wide-size-range medium-solid bed is good. The separation performance of the modularized system was then investigated in detail using a mixture of <0.3 mm magnetite powder (mass fraction of 0.3–0.06 mm particles is 91.38 %) and <1 mm fine coal as solid media. The experimental results show that at separation densities of 1.33 g/cm3 or 1.61 g/cm3, 50–6 mm coal can be separated effectively with probable error, E, values of 0.05 g/cm3 and 0.06 g/cm3, respectively. This technique is beneficial for saving water resources and for the clean utilization of coal.

http://www.zndxzk.com.cn/down/upfile/soft/20110318/16-p0374-E100534.pdf

Zengqiang Chen

Papers

Density-dependent separation of dry fine coal in a vibrated fluidized bed

Fine coal dry beneficiation using autogenous medium in a vibrated fluidized bed

Dry cleaning of fine lignite in a vibrated gas-fluidized bed: Segregation characteristics

Modularized dry coal beneficiation technique based on gas-solid fluidized bed

Effect of operating conditions on drying of Chinese lignite in a vibration fluidized bed