Impact of Coal-Electricity Integration on Chinas Power Grid Development Strategy

Wang, Wei; Zhang, Chen; Liu, Wen Yan

doi:10.4028/www.scientific.net/amr.860-863.2600

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Today, with the continuous growth of global energy demand, coal, as a crucial energy resource that has served industrial production for centuries, still plays a vital role in the modern energy system [ 1 , 2 , 3 ]. It is currently widely used in electricity generation and industrial manufacturing, providing reliable energy support for societal development [ 4 , 5 , 6 ]. However, despite the undeniable significance of coal in the energy industry, the process of coal mining still faces a series of technical difficulties and challenges.…”

Section: Introductionmentioning

confidence: 99%

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face

Fan,

Mao,

et al. 2024

Sensors

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With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for the coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model’s computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model’s computational requirements and volume while maintaining high accuracy.

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Section: Introductionmentioning

confidence: 99%

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face

Fan,

Mao,

et al. 2024

Sensors

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“…Driven by the concept of a green environment, the coal-electricity integrated industry model has become a major development strategy for coal and electricity companies [1][2][3]. An important part of this industrial model is the backfilling of powerplant fly ash underground [4][5][6], which is mainly used for the reinforcement of surrounding rock mass [7][8][9], face end plugging [10], and spontaneous ignition of remnant coal within the gob [11,12].…”

Section: Introductionmentioning

confidence: 99%

Eluviation and Leaching of Elements from Broken Fly-Ash-Based Porous Geopolymer

et al. 2021

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The fly ash from powerplants used for coal mine end backfilling can effectively reduce the impact of ground fly ash accumulation on the environment. However, due to the long-term action of the overlying strata and groundwater, when the backfilling body is broken, heavy metals will also be leached, thus having an impact on the groundwater. Therefore, in this paper, the eluviation and leaching of elements from a broken fly-ash-based porous geopolymer is studied. The fly-ash-based geopolymer material was prepared to perform a dynamic eluviation and static leaching test, and it was found that the amount of Cu and Zn in the leachate was less abundant, whereas Pb was more abundant, but far less than the limit of the Class III groundwater quality standard. An acidic environment and a smaller solid–liquid ratio can promote the leaching of Cu and Zn, while the leaching of Pb is basically unaffected by the pH value. Moreover, the amount of Cu, Zn, and Pb in the lixivium increased with the increase in leaching time, and the amount of Cu and Zn in the lixivium was still low after 150 h of leaching, whereas the amount of Pb was high, approaching the limit value of the Class III groundwater quality standard, showing a tendency to increase after 100 h of leaching. A leaching orthogonal experiment was designed, and the results showed that the main order of each factor affecting the leaching of heavy metals from the fly-ash-based geopolymer was grain size > pH > solid–liquid ratio; thus, the leaching of heavy metals from fly-ash-based geopolymer can be controlled, which is significant with respect to the extensive use of fly-ash materials underground.

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