College of Mining Engineering, Liaoning Technical University, Fuxin, 123000, China.
Sci Rep. 2021 Sep 28;11(1):19179. doi: 10.1038/s41598-021-98630-4.
Longwall top coal caving technology is one of the main methods of thick coal seam mining in China, and the classification evaluation of top coal cavability in longwall top coal caving working face is of great significance for improving coal recovery. However, the empirical or numerical simulation method currently used to evaluate the top coal cavability has high cost and low-efficiency problems. Therefore, in order to improve the evaluation efficiency and reduce evaluation the cost of top coal cavability, according to the characteristics of classification evaluation of top coal cavability, this paper improved and optimized the fuzzy neural network developed by Nauck and Kruse and establishes the fuzzy neural network prediction model for classification evaluation of top coal cavability. At the same time, in order to ensure that the optimized and improved fuzzy neural network has the ability of global approximation that a neural network should have, its global approximation is verified. Then use the data in the database of published papers from CNKI as sample data to train, verify and test the established fuzzy neural network model. After that, the tested model is applied to the classification evaluation of the top coal cavability in 61,107 longwall top coal caving working face in Liuwan Coal Mine. The final evaluation result is that the top coal cavability grade of the 61,107 longwall top coal caving working face in Liuwan Coal Mine is grade II, consistent with the engineering practice.
综放开采顶煤冒放性分类评价对于提高煤炭采出率具有重要意义。然而，目前用于评价顶煤冒放性的经验或数值模拟方法存在成本高、效率低的问题。因此，为了提高评价效率，降低评价成本，本文针对顶煤冒放性分类评价的特点，对 Nauck 和 Kruse 提出的模糊神经网络进行改进和优化，建立了顶煤冒放性分类评价的模糊神经网络预测模型。同时，为了保证优化改进后的模糊神经网络具有神经网络应具备的全局逼近能力，对其进行了全局逼近验证。然后，利用中国知网（CNKI）数据库中发表论文的数据作为样本数据，对建立的模糊神经网络模型进行训练、验证和测试。之后，将测试后的模型应用于刘湾煤矿 61107 综放工作面的顶煤冒放性分类评价中。最终评价结果为刘湾煤矿 61107 综放工作面的顶煤冒放性等级为Ⅱ级，与工程实际情况一致。
