A Dual-Loop Control System for Dense Medium Coal Washing Processes With Sampled and Delayed Measurements

Zhang, Lijun; Xia, Xiaohua; Zhu, Bing

doi:10.1109/tcst.2016.2640946

Cited by 12 publications

(5 citation statements)

References 43 publications

(55 reference statements)

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“…Also, the development and application of dynamic model [ 136 ], dynamic and steady-state model [ 137 ], Rosin-Rammler model [ 138 ], and soft sensor model [ 120 ] have been reported. System controls (i.e., smart sensor [ 130 ], model-data-based switching adaptive control [ 139 ], pump-storage system [ 140 ], medium density control [ 141 ], dual-loop control system [ 142 ], and intelligent control [ 143 ]) were also applied to measure and optimize separation. In addition, DMC has been evaluated for the reprocessing of coal tailings [ 144 ], including the effects of near gravity materials (NGM)—the percentage of material occurring within the range of ±0.1 cut density (CD)—on coal cleaning [ 145 ].…”

Section: Conventional Gravity Separation For Coal Cleaningmentioning

confidence: 99%

Conventional and recent advances in gravity separation technologies for coal cleaning: A systematic and critical review

Phengsaart¹,

Srichonphaisan²,

Kertbundit³

et al. 2023

Heliyon

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Section: Conventional Gravity Separation For Coal Cleaningmentioning

confidence: 99%

Conventional and recent advances in gravity separation technologies for coal cleaning: A systematic and critical review

Phengsaart¹,

Srichonphaisan²,

Kertbundit³

et al. 2023

Heliyon

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“…Dual-loop control of DMS process is developed in [3]. Concretely, the combination of feedforward control and closed-loop model predictive control (MPC) is applied in ash loop to optimize the setpoints of the density loop online based on operation index, and MPC is applied in density loop to adjust the supplement of dense medium and water so that the density can track the setpoints.…”

Section: Introductionmentioning

confidence: 99%

Multi‐rate event‐triggered control with imperfect data for dense medium separation

Dai,

Yang,

Zhang

et al. 2023

IET Control Theory & Appl

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The stability control of suspension density is an important means to ensure the quality of fine coal in dense medium separation (DMS) process. This paper focuses on the tracking control issue of the dense medium suspension density in a non‐uniform sampling and networked control environment, and proposes a multi‐rate event trigger control design method. Firstly, lifting technique and multi‐rate identification method are adopted to establish the multi‐rate model. Secondly, a multi‐rate event‐triggered predictive control (MEPC) algorithm is proposed to reduce the waste of communication resources and ensure tracking performance. Furthermore, by using a Kalman filter to eliminate the effects of noise and developing a compensation mechanism to predict the lost data, a robust MEPC (RMEPC) algorithm is further proposed for imperfect data in the actual production process. Finally, the hardware‐in‐the‐loop simulation experiments with actual parameters of coal preparation plant have been carried out, showing the effectiveness of the proposed method.

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“…Coal consumes 30% of the world's primary energy, and 45% of the global power generation comes from the supply of coal energy. Thus, it is clear that the effective exploitation of coal resources is of great significance for industry development [1][2][3]. China is rich in coal resources reserves, and the total coal resources rank among the top in the world, whether in terms of the coal distribution area, total coal resources reserves, or proven reserves.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Intelligent Control Strategy for Heavy Media Coal Separation Process Based on Deep Learning Model

Wang,

He,

Bai

2023

Applied Mathematics and Nonlinear Sciences

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Intelligent control of heavy dielectric coal beneficiation in coal plants is achieved with the help of deep learning models to optimize the control effect. In this paper, through the study of heavy dielectric coal separation methods and processes, a coal separation control optimization strategy based on a radial basis neural network optimized by the ant colony algorithm is proposed, and the RBF network is optimized by clustering using ant colony algorithm, which is used to determine the center and radius of the basic function of the RBF network. The suspension density, ash content of the fine coal and the level of the Hopper bucket, which affect the control effect, are selected as the inputs of the optimized model, and the control strategy is formulated according to the effect after adjusting the parameters. The experimental simulation results show that the ACO-RBF model has less oscillation when the ash value is changed, the final change is smoother, and the root mean square error of the ash value is 0.075%, which is 36.6% less than that of the PID algorithm. With the control strategy optimized by deep learning, the fluctuation range of the level of the qualified media barrel is controlled between 15 and 25 cm, and the volatility pattern of the level is more regular. The control system based on deep learning can better meet the requirements of the coal processing process and effectively improve the efficiency of a coal processing plant.

show abstract

A Dual-Loop Control System for Dense Medium Coal Washing Processes With Sampled and Delayed Measurements

Cited by 12 publications

References 43 publications

Conventional and recent advances in gravity separation technologies for coal cleaning: A systematic and critical review

Conventional and recent advances in gravity separation technologies for coal cleaning: A systematic and critical review

Multi‐rate event‐triggered control with imperfect data for dense medium separation

Analysis of Intelligent Control Strategy for Heavy Media Coal Separation Process Based on Deep Learning Model

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