Use BP Neural Network Set up the Corrosion Prediction Model of Low Temperature Parts of Atmospheric Pressure Device

2012 ◽  
Vol 152-154 ◽  
pp. 1138-1142 ◽  
Author(s):  
Yu Guang Fan ◽  
Zai Dong Piao ◽  
Bing Chen ◽  
Hong Xian Lin ◽  
Yang Yang
Keyword(s):  
Neural Network ◽  
Low Temperature ◽  
Prediction Model ◽  
Corrosion Protection ◽  
Bp Neural Network ◽  
Atmospheric Pressure ◽  
Ph Value ◽  
Set Up

In research of the low temperature parts of atmospheric pressure device, by using BP neural network, the connection of PH value, Cl-, H2S and Fe+2 was setup which can predict Fe+2 content accurately, and obtain the requirement accuracy, hence more accurate corrosion can be predicted and providing more suggests for corrosion protection.

scholarly journals Research on Prediction of Rating of Rockburst Based on BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 463-469 ◽  
Author(s):  
Xiaobo Xiong
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Rock Burst ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Good Prospect ◽  
Underground Engineering ◽  
Minimum Stress ◽  
Set Up ◽  
Economic Construction

With the development of economic construction, underground space development continues to move towards the deep. "More, long, big, deep," will be the general trend of the development of underground engineering in the 21st century. Rock burst is a kind of sudden geological disasters with a higher frequency in deep tunnel construction. Rock burst prediction has very important significance for the construction of underground engineering in highland stress area. This paper described the mechanism of rockburst. The researchers systematically analyzed relevant factors of rockburst. In this paper, the principle and application of Back-Propagation (BP) neural network were introduced, and to improve the algorithm of neural network, the NNT prediction model was set up. The author have taken the seven parameters including (as input values): Index of brittleness, Ratio of Strength stress, Ratio of maximum stress to minimum stress, Depth of engineering, Completeness of rockmass, Structural strength, Depth of pit for rock burst. The results of rockburst also proved the prediction model has high accuracy and stability, indicating that the model has a good prospect in the rock burst forecasting.

Research on Stamping Spring-Back Prediction for Car Body Panel Based on BP Neural Network

2010 ◽  
Vol 97-101 ◽  
pp. 250-254 ◽  
Author(s):  
Xin Jian Zhou
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Front Panel ◽  
Spring Back ◽  
Test Analysis ◽  
Car Body ◽  
Neural Network Prediction ◽  
Mould Design ◽  
Bp Neural Network Model

On the basis of orthogonal test analysis of variance, BP neural network is used to forecast quantitatively the stamping spring-back of front panel of a car body, namely the engine hood, under the conditions of different stamping parameters. Firstly, BP neural network prediction model is established and sample training is done in Matlab. Then, the spring-back prediction using BP neural network and the result of spring-back simulation using Dynaform is compared to verify the precision and stability of the prediction model. Lastly, modification is made to the BP neural network according to practical stamping parameters and an efficient BP neural network model is established. Using this model, stamping spring-back prediction for the front panel of a car body is made. The spring-back prediction could then be used for spring-back compensation in the mould design of the front panel.

Geological Adaptive Cutterhead Selection for EPB Shield Based on BP Neural Network

2014 ◽  
Vol 607 ◽  
pp. 118-123
Author(s):  
Lai Kuang Lin ◽  
Yi Min Xia ◽  
Fei He ◽  
Qing Song Mao ◽  
Kui Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Earth Pressure ◽  
Training Model ◽  
Structure Design ◽  
Pressure Balance ◽  
Selection For ◽  
Earth Pressure Balance ◽  
Set Up ◽  
Epb Shield

In view of complex and fuzziness of geological adaptive cutterhead selection for earth pressure balance (EPB) shield, a cutterhead selection method based on BP neural network is put forward. Considering the structure characteristics of EPB shield cutterhead, typical cutterhead types are classified and summarized based on cutterhead topology structure and number of spokes. After analyzing the determinants of cutterhead selection, one-to-many mapping relation between cutterhead type and geological parameters is put forward, and then core geologic parameters related to cutterhead selection are concluded. The feasibility of using neural network method to choose the cutterhead type is analyzed, and a BP neural network training model for cutterhead selection is set up and tested in testing sample data. The result shows that the selected cutterhead and the construction cutterhead are basically consistent. The feasibility of this method is proved and it can be theoretical basis for the cutterhead structure design which will improve scientific of cutterhead selection.

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

2010 ◽  
Vol 171-172 ◽  
pp. 274-277
Author(s):  
Yun Liang Tan ◽  
Ze Zhang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Creep Curve ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Reference Value ◽  
Network Approach ◽  
Neural Network Approach ◽  
Training Time ◽  
Set Up

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.

Prediction of Sand Thickness Using Seismic Attributes Based on BP-Neural Network

2012 ◽  
Vol 524-527 ◽  
pp. 180-183
Author(s):  
Feng Gao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Seismic Attributes ◽  
Bp Network ◽  
Sand Body ◽  
Precise Prediction ◽  
Energy Maximum ◽  
Bp Model ◽  
Rms Amplitude

Total energy, maximum peak amplitude and RMS amplitude are sensitive to sand body, and they are non-linear relations with sand thickness. In this study, a three-layer BP neural network is employed to build the prediction model. Nine samples were analyzed by three-layer BP network. The relationships were produced by BP network between sand thickness and the three seismic attributes. The precise prediction results indicate that the three-layer BP network based modeling is a practically very useful tool in prediction sand thickness. The BP model provided better accuracy in prediction than other methods.

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