Abstract:Dense medium drum (DMD) separators are unit processes that are typically used to beneficiate coal, iron ore and other minerals by making use of density separation. Some coal dense medium separation plants typically include a DMD separator. The operational management of this unit process is often limited to localised control of medium density and feed mass flow rate. Dynamic models for coal dense medium separation have been developed by the authors with the intention of using them for dynamic control.A suitable… Show more
“…8 a) is a large open tank with the circulation of finely pulverized magnetite suspension while the dense medium drum (DMD) ( Fig. 8 b) is a rotating longitudinal cylindrical vessel where waste rocks sink and are collected by lifters to be discharged into the launder [ 119 ]. Fig.…”
Section: Conventional Gravity Separation For Coal Cleaningmentioning
“…8 a) is a large open tank with the circulation of finely pulverized magnetite suspension while the dense medium drum (DMD) ( Fig. 8 b) is a rotating longitudinal cylindrical vessel where waste rocks sink and are collected by lifters to be discharged into the launder [ 119 ]. Fig.…”
Section: Conventional Gravity Separation For Coal Cleaningmentioning
“…The outer ring is the feedback control of clean coal ash, which is used to control the quality of clean coal products; the inner ring is the feedback control of suspension density. In a traditional dense medium separation process, feedback is adjusted through manual operations, thus, a substantial time lag exists in all the operational aspects such as production and detection [15][16][17]. Therefore, the dense medium separation control system is divided into three parts: clean coal ash feedback, suspension density prediction, and suspension density control.…”
In the dense medium separation system of coal preparation plant, the fluctuation of raw coal ash and lag of suspension density adjustment often causes the instability of product quality. To solve this problem, this study established a suspension density prediction model for the dense medium separation system based on Long Short-Term Memory (LSTM). First, the historical data in the dense medium separation system of a coal preparation plant were collected and preprocessed. Moving average and cubic exponential smoothing methods were used to replace abnormal data and to fill in the missing data, respectively. Second, a LSTM network was used to construct the density prediction model, and the optimal number of time steps, hidden layers, and nodes was determined. Finally, the model was employed on a testing set for prediction, and a Back-Propagation (BP) network without a time series was used for comparison. Root Mean Squared Error (RMSE) were the minimum when the number of the hidden layers, nodes, and time steps was 6, 12, and 5, respectively. In this case, the RMSE and Mean Absolute Percent Error (MAPE) of the LSTM method were 0.009 and 0.007, respectively, while those of the BP method were 0.019 and 0.015, respectively. Therefore, the model established using LSTM can be used to accurately predict the suspension density of the dense medium separation system.
“…The manual sorting method is gradually eliminated because of its poor working environment and low efficiency. Mechanical washing mainly includes the heavy medium method [3], jigging method [4] and flotation method [5], which have problems such as easy loss of separation equipment, waste of water resources, increase in production cost caused by drying treatment and medium recovery [6], [7]. The ray recognition [8] method is harmful to the human body, and the mechanical vibration [9] method is only applicable to a few scenes.…”
Coal is an important energy source in China. The separation of coal and gangue is a key link in coal mining, and the realization of the intelligent separation of coal and gangue provides technical support to promote the intelligent development of coal mines. To solve the problem of difficult to sort coal and gangue quickly and accurately caused by the fast running speed of belt, the different shapes and sizes of gangue and so on, an online flexible sorting model based on multi-information fusion is proposed. We use a near-infrared camera to identify coal and gangue, and combine it with a depth camera to obtain the height and pose information of gangue. According to the information obtained by the camera, the flexible sorting of coal and gangue can be realized. We also analyzed the different factors that affect the trajectory of gangue before and after pneumatic separation, deduced the theoretical formula of horizontal movement distance of gangue pneumatic separation, established the mathematical model of gangue flexible sorting, and carried out experiments of pneumatic separation with different shapes and sizes of gangue. The error compensation of the model is carried out by using experimental data. Through experimental verification, the proposed gangue flexible sorting model can effectively improve the accuracy of gangue sorting and can still accurately sort gangue of different shapes and sizes despite the high-speed movement of the belt.
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