scholarly journals Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meng Wang ◽  
Caiwang Tai ◽  
Qiaofeng Zhang ◽  
Zongwei Yang ◽  
Jiazheng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Coal Mine ◽  
Fuzzy Neural Network ◽  
Engineering Practice ◽  
Global Approximation ◽  
Working Face ◽  
Fuzzy Neural ◽  
Classification Evaluation ◽  
Seam Mining ◽  
Longwall Top Coal Caving

AbstractLongwall 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.

scholarly journals Application of BigML in the Classification Evaluation of Top Coal Caving

2021 ◽  
Vol 2021 ◽  
pp. 1-28
Author(s):  
Meng Wang ◽  
Caiwang Tai ◽  
Qiaofeng Zhang ◽  
Zongwei Yang ◽  
Jiazheng Li ◽  
...  
Keyword(s):  
Coal Seam ◽  
Coal Mine ◽  
Evaluation Model ◽  
Engineering Practice ◽  
Coal Seams ◽  
Mining Technology ◽  
Evaluation Result ◽  
Sample Data ◽  
Classification Evaluation ◽  
Longwall Top Coal Caving

Longwall top coal caving mining is one of the main methods of mining thick coal seams in China. Therefore, carrying out the classification evaluation of top coal caving is of great significance to ensure mining success and reduce the risk of mining technology. In order to realize the classification evaluation of top coal caving, this article introduces the method of using BigML to establish the classification evaluation model of top coal caving. Furthermore, using the data from the CNKI database as sample data, a classification evaluation model of top coal caving is established on BigML. After training, testing, and optimization, the model is used to evaluate the top coal caving in No. 3 coal seam of Gucheng Coal Mine, and the evaluation result is grade 1, which is consistent with the engineering practice. The final research results show that the application of BigML in the classification evaluation of top coal caving is successful; the evaluation of top coal caving through BigML is reliable; BigML provides another scientific reliability way for the classification evaluation of top coal caving.

Fault Intelligent Diagnosis of Coal Mine Hoist Based on GCFNN

2013 ◽  
Vol 634-638 ◽  
pp. 3716-3720 ◽  
Author(s):  
Li Li Dong ◽  
Qing Qing Ding
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Coal Mine ◽  
Fuzzy Neural Network ◽  
Correct Diagnosis ◽  
Intelligent Diagnosis ◽  
Grey Clustering ◽  
Fuzzy Neural ◽  
Network Fault ◽  
Mine Hoist

Equipment running subtle condition can’t be clearly expressed by clustering result of explicit affiliation in the fuzzy neural network fault diagnosis. In order to solve the problems in the present, the integration of grey clustering theory and fuzzy neural network was researched, and the fault intelligent diagnosis methods based on grey clustering fuzzy neural network (GCFNN) was proposed, the structure and the algorithm of GCFNN were designed, and the model of GCFNN was established. In coal mine hoist hydraulic subsystem fault diagnosis as an example, the feasibility and validity of the method is simulated and verified. The experiment results show that GCFNN can make a correct diagnosis, express more detailed equipment condition information. The method proposed provides basis for the maintenance of the mine hoist, and provides a new approach for the fault diagnosis of the other mine equipment.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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