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Utilization of coal slime: Coal and kaolinite separation by classification, forward and reverse flotation method

Shen Liang, Qiao Erle, Liu Lingyun, Xue Changguo, Liu Binghe, Min Fanfei

Physicochemical Problems of Mineral Processing

2022

Vol. 58, iss. 3

art. no. 147742

Coal slime is not only a solid waste, but also a source of energy. With the improvement of environmental protection requirements, the comprehensive utilization of slime has become an urgent problem for coal preparation plants. In this paper, we put forward a promising way of coal slime resource utilization. X-ray diffraction (XRD), X-ray fluorescence spectrometer (XRF) and laser particle sizer was used to analyze the properties of coal slime. Obtained results showed that the slime was mainly composed of the coal, kaolinite and quartz with a particle size of -100 μm. Most kaolinite minerals can be enriched in overflow when the feed pressure is 0.2 MPa by using hydrocyclone. 21.3% clean coal with ash content of 12.3% and 33.46% kaolinite with particle size of -5 μm can be recovered by forward flotation and reverse flotation respectively. Coal water slurry with 61% concentration can be prepared from reject of forward flotation and concentrate of reverse flotation at shear rate of 100 s-1. This study has an important practical application value in clean and efficient utilization of coal.

Dynamic location models of mobile sensors for travel time estimation on a freeway

Sun Weiwei, Shen Liang, Shao Hu, Liu Pengjie

International Journal of Applied Mathematics and Computer Science

2021

Vol. 31, no. 2

271--287

Travel time estimation for freeways has attracted much attention from researchers and traffic management departments. Because of various uncertain factors, travel time on a freeway is stochastic. To obtain travel time estimates for a freeway accurately, this paper proposes two traffic sensor location models that consider minimizing the error of travel time estimation and maximizing the collected traffic flow. First, a dynamic optimal location model of the mobile sensor is proposed under the assumption that there are no traffic sensors on a freeway. Next, a dynamic optimal combinatorial model of adding mobile sensors taking account of fixed sensors on a freeway is presented. It should be pointed out that the technology of data fusion will be adopted to tackle the collected data from multiple sensors in the second optimization model. Then, a simulated annealing algorithm is established to find the solutions of the proposed two optimization models. Numerical examples demonstrate that dynamic optimization of mobile sensor locations for the estimation of travel times on a freeway is more accurate than the conventional location model.