Study on Sensor with Mechanical Properties in Nuclear Power Plant with Application of BP Neural Network to Fault Tolerant Control

2013 ◽  
Vol 644 ◽  
pp. 56-59
Author(s):  
Jin Yang Li ◽  
Hong Xia ◽  
Shou Yu Cheng
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Bp Neural Network ◽  
Nuclear Power ◽  
Control Method ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Level Sensor

All kinds of sensor with mechanical properties often can go wrong in nuclear power plant. In this kind of situation, it puts forward a kind of active fault tolerant control method based on the improved BP neural network. Firstly, the method will train sensor by BP neural network. Secondly, it will be established dynamic model bank in all kinds of running state. The system will be detected by using BP neural network real time. When the sensor goes wrong, it will be controled by reconstruction. Taking pressurizer water-level sensor as the case, a simulation experiment was performed on the nuclear power plant simulator. The results showed that the proposed method is valid for the fault tolerant control of sensor in nuclear power plant.

Application of Fuzzy Neural Network to Fault Diagnosis of Sensor with Mechanical Properties in Nuclear Power Plant

2013 ◽  
Vol 644 ◽  
pp. 68-71 ◽  
Author(s):  
Jin Yang Li ◽  
Hong Xia
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Fuzzy Neural Network ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Fuzzy Neural

In view of the sensor fault in nuclear power plant, it puts forward a method to fault diagnosis of sensor with mechanical properties based on fuzzy neural network. The method would be fuzzy logic control combined with neural network. It adjusted and corrected membership function parameters and network weights with back propagation algorithm. After the completion of fuzzy neural network training, it could get the credibility of sensor with mechanical properties real time. Taking pressurizer water-level sensor as the case, the simulation experiment results showed that the method is valid for the fault diagnosis of sensor with mechanical properties in nuclear power plant.

Dissolution, Mechanical Properties, and Thermal Stability of Microparticles Containing Radioactive Cesium on Plant Litter Derived from the Fukushima Daiichi Nuclear Power Plant Accident,  and Soil Decontamination Trials 

2021 ◽  
Author(s):  
Ichiro Tanaka ◽  
Atsushi Yamaguchi ◽  
Kenji Kikuchi ◽  
Nobuo Niimura ◽  
Yume Saeki ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Thermal Stability ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Plant Litter ◽  
Radioactive Cesium ◽  
Soil Decontamination ◽  
Fdnpp Accident ◽  
Fukushima Daiichi

<p>The radioactive cesium (<sup>134</sup>Cs and <sup>137</sup>Cs), which originated from the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, has remained in the soil and on plants as water-insoluble microparticles (termed as CsMPs) to some extent, and maintained relatively high radioactivity levels in the district. However, it has been reported that the radioactive Cs has been absorbed by plants. To interpret this phenomenon, the authors investigated CsMPs to determine if they become soluble during filtration and dialysis experiments. Moreover, other physical properties, such as mechanical properties and thermal stability, were observed during the course of the relevant experiments. These properties can be obtained by using carbonized charcoal litter with CsMPs. And simple and economic decontamination trials of the soil were performed by sieving after drying and roughly crushing.</p>

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