Abstract
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.
Highlights
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
Longwall top coal caving mining is a special type of mining method, which is usually used to mine coal seams with a thickness of more than 4.5 Âm1
There are more than 200 working faces in China using longwall top coal caving mining technology to mine thick coal seams
Summary
Rough set theory to establish a Bayesian classifier model for evaluating and predicting coal roof cavability and used the model to predict eight groups of samples to be tested, with an accuracy of 100%. Where L is the loss function; yt,k is the t-th sample is the output value of the k-th neuron of the activation layer; y* is actual value label vector; η is the learning rate; uij is the i-th variable corresponds to the membership degree of the j-th node; xi is the i-th input variable; mij is the membership function cluster center of the j-th node corresponding to the i-th variable; Ïij is the width of the membership functions the j-th node corresponding to the i-th variable; Ïi is the algebraic product of the membership degree of the i-th node in the reasoning layer; wij is the weight; n is the number of variables. This paper aims to verify the applicability and superiority of the improved and optimized fuzzy neural network in the classification evaluation of top coal cavability.
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