2013 ◽  
Author(s):  
Likun Zheng ◽  
Chang Chen ◽  
Danmei Xie ◽  
Hengliang Zhang ◽  
Yanzhi Yu

For condensing turbine, steam exhaust point is in wet steam area. The exhaust steam humidity of steam turbine is difficult to get due to lacking of effective measuring method. Calculation of exhaust steam humidity has always been one of the key parts of the analysis of thermal power units. The main factors affecting exhaust steam humidity are turbine load and turbine exhaust pressure etc, and they are of non-linearity. This paper develops a calculation method to calculate exhaust steam humidity based on BP neural network. Taking a N1000-25/600/600 ultra-supercritical (USC) steam turbine as an example, the exhaust steam humidity is calculated and the results show that the method has a good accuracy to meet the needs of the engineering application.


2014 ◽  
Vol 496-500 ◽  
pp. 2989-2995
Author(s):  
Zheng Mao Wei ◽  
Xiang Li Zou ◽  
Min Li ◽  
Wei Li ◽  
Cheng Bing Li

In view of the phenomenon that the affected area is broader, more severe losses and more difficult to relief in the urban agglomeration after major natural disasters,it is proposed that transport system reliability calculation method which based on BP neural network model.First of all,analyze the urban transport system network after a disaster, using the weight contribution rate analysis method to extract the key nodes in the network; Second, re-integrating the extracted nodes and establishing a new road network model which based on BP neural network;Then, using the cut set algorithm computing network reliability, and combined with the extent of damage of the road network after a disaster, putting forward the calculation method of urban agglomeration road network reliability after disasters; Finally, for example as changsha-zhuzhou-xiangtan urban agglomeration, examining the authenticity of the method.


2017 ◽  
Vol 105 ◽  
pp. 3173-3178 ◽  
Author(s):  
Libao Yin ◽  
Guicai Liu ◽  
Jielian Zhou ◽  
Yanfen Liao ◽  
Xiaoqian Ma

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods


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