Optimization of the Process Parameters of Fully Mechanized Top-Coal Caving in Thick-Seam Coal Using BP Neural Networks

Liang, Minfu; Hu, Chengjun; Yu, Rui; Wang, Lixin; Baofu, Zhao; Xu, Ziyue

doi:10.3390/su14031340

Cited by 12 publications

(10 citation statements)

References 29 publications

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Section: Model Building Backgroundmentioning

confidence: 99%

“…Fully mechanized top coal caving is one of the main mining methods suitable for thick and extra-thick coal seam mining in China, which has many advantages such as low cost, low tunneling rate, high recovery rate, and strong adaptability. 33,34 The coal seam generally occurs in the coal-bearing rock formation, located between the roof and floor strata. With the advancement of coal mining at the working face, the coal seam floor plays a supporting role, and the roof is above the coal seam.…”

Section: Introduction Of Top Coal Cavingmentioning

confidence: 99%

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Research on Coal Gangue Recognition Based on Multi-source Time–Frequency Domain Feature Fusion

Zhang,

Yang,

Zeng

2023

ACS Omega

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The over-exploitation of resources caused by the increasing coal demand has resulted in a sharp increase in solid waste emissions mainly gangue, which has made the burden on the environment, economy, resources, and society of our country heavier. In order to achieve a balance between energy consumption and solid waste emission in the process of top coal caving, this study carried out coal gangue recognition research based on multi-source time–frequency domain feature fusion (MS-TFDF-F). First, the process of coal gangue symbiosis and the harm of gangue in top coal caving are analyzed, and the fundamental method of comprehensive treatment of gangue is put forward, which is the accurate recognition of the coal gangue interface. Second, by building a top coal caving simulation test bed, the MS signals generated in the caving process of the coal gangue mixture with a gangue content of 0–100% are collected and the TFDFs are extracted. Third, the MS-TFDF-F-based coal gangue recognition model is established. Then, the recognition effect of the two TFDF-F sample sets was compared, and the results show that the time–frequency domain feature selection fusion method (TFDFS-FM) has higher accuracy. On this basis, this paper studies the variation law of the number of sensors on the coal gangue recognition accuracy of MS information fusion. Finally, the economic, social, environmental, and resource benefits of the model are qualitatively described. The final results show that the MS-TFDF-F-based coal gangue recognition model has the strongest recognition ability when fusing six sensor signals, and the recognition accuracy reaches 99% under the AdaBoost algorithm. The establishment of this model brings huge benefits to China’s environment, economy, resources, and society, and it is helpful to realize the balance between loss reduction mining and solid waste emission reduction in the process of top coal caving.

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Section: Model Building Backgroundmentioning

confidence: 99%

Section: Introduction Of Top Coal Cavingmentioning

confidence: 99%

Research on Coal Gangue Recognition Based on Multi-source Time–Frequency Domain Feature Fusion

Zhang,

Yang,

Zeng

2023

ACS Omega

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“…And when a pair of learning modes is provided to the BP neural network, the corresponding input nodes are activated, the activation value propagates from the input layer through each intermediate layer to the output layer, and the input response of the network is obtained at each node of the output layer, and in the direction of reducing the deviation between the expected output and the actual output, the connection weights are corrected layer by layer from the output layer through each intermediate layer, and finally back to the input layer. With the continuous correction of this error reverse propagation, the correct rate of neural network response to the input mode is constantly improving (Horikawa et al, 1992;Liang et al, 2022). The basic structure of neural network is given as Figure 2.…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

Performance control study of interleaved meltblown non-woven materials based on statistical analysis and predictive modeling

et al. 2023

Front. Comput. Neurosci.

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Meltblown nonwoven materials have gained attention due to their excellent filtration performance. The research on the performance of the intercalation meltblown preparation process is complex and a current research focus in the field of chemical production. Based on data related to intercalated and unintercalated meltblown materials under given process conditions, a product performance prediction model of intercalated meltblown materials was established under different process parameters (receiving distance, hot air velocity). The structural variables (thickness, porosity, and compressive resilience), the change in product performance, and the relationship between structural variables and product performance (filtration resistance, efficiency, air permeability) after intercalation were studied. Multiple regression analysis was used to analyze the structural variables, and evaluation of the regression results were made using R2, MSE, SSR, and SST. A BP neural network prediction model for product performance was established. The BP neural network model was used to find the maximum filtration efficiency. The study provides theoretical support for regulating product performance by solving the maximum filtration efficiency using BP neural network model.

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“…BP neural network is a feedforward network by error back-propagation algorithm proposed by Rumelhart and McCelland [48], which regulates parameters by the feedback effect of error self-correction and continuously adjusts the weights and thresholds under the back-propagation effect to finally obtain a prediction model with the minimum sum of squared errors. The use of neural network algorithms to explore data laws has become a trend in urban research and is widely used in the fields of geological hazard prediction [49], biological yield estimation [50], and material quality assessment [51], the use in the direction of landscape gardening is still rare.…”

Section: Evaluation Of Greenway Alignment Suitability Based On Pso-bp...mentioning

confidence: 99%

Research on Urban Greenway Alignment Selection Based on Multisource Data

2022

Sustainability

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At present, people’s growing need for a beautiful ecological environment needs to be met. As a high-quality ecological product, greenways help improve the living environment and promote green and coordinated development. How to scientifically and rationally plan greenway routes and expand the green ecological network has become an important issue for the sustainable development of high-density built-up areas. Therefore, this study determines the suitability evaluation system of urban greenway alignment selection based on spatial constructiveness, greenway accessibility, resource attractiveness and public demand through factor analysis. Taking Xixi Wetland to South Lake in Yuhang District as an example, combining urban multi-source data and GIS spatial analysis methods, all routes suitable for greenway construction in the study area were determined, and four excellent greenway routes were initially identified through manual correction. Subsequently, the PSO-BP neural network model was trained using 172 greenway samples to obtain a greenway alignment suitability evaluation model with an accuracy of more than 96%, which was used for subsequent greenway alignment optimization. The above four preliminary greenway routes are substituted into the PSO-BP model to obtain the optimal greenway routes in the study area. The results show that there are 17 indicators in 4 levels and 8 dimensions in the urban greenway route selection potential model, among which two indicators, public cycling and running demand, have a greater weight and should be highly considered in the route selection assessment. The optimal planned route for the greenway start from Guangfu Temple entrance of Xixi Wetland, follow the roadside green belt and Yuhangtang River greenway to South Lake. The study obtained the best planning route for the greenway from Xixi Wetland to South Lake in Yuhang District, which is a new practice of landscape garden planning and design supported by multisource data and new technologies, with a view to providing a reference for the preferential selection of urban greenway routes in similar high-density built-up areas.

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Optimization of the Process Parameters of Fully Mechanized Top-Coal Caving in Thick-Seam Coal Using BP Neural Networks

Cited by 12 publications

References 29 publications

Research on Coal Gangue Recognition Based on Multi-source Time–Frequency Domain Feature Fusion

Research on Coal Gangue Recognition Based on Multi-source Time–Frequency Domain Feature Fusion

Performance control study of interleaved meltblown non-woven materials based on statistical analysis and predictive modeling

Research on Urban Greenway Alignment Selection Based on Multisource Data

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