scholarly journals Optimization of combined production of electricity and hydrogen at nuclear power plants using a neural network

2020 ◽  
Vol 221 ◽  
pp. 01003
Author(s):  
Ilia Lopyrev ◽  
Vadim Golubev ◽  
Daria Voznesenskaya ◽  
Victoria Verbnikova ◽  
Olga Novikova
Keyword(s):  
Neural Network ◽  
Nuclear Power ◽  
Power Plants ◽  
Planning System ◽  
Energy Industry ◽  
Fourth Generation ◽  
Technological Advancement ◽  
Investment Projects ◽  
The Neural Network ◽  
Production Of Hydrogen

This article discusses a project with a basis on implementation of combined production of electricity and hydrogen based on a HTGR reactor in the Primorsky Krai of Russia. One of the major advantages of the fourth-generation reactors of the HTGR type is, that water vapor reaches 800 degrees Celsius, which allows not only to efficiently transfer thermal energy to external circuits, but also to use it in the production of hydrogen using the steam reforming of methane [1]. The results of the research were composed mainly of two fully-calculated investment projects, which showed an significant increase in the economic efficiency of combined production of electricity and hydrogen when included in the neural network planning system. Moreover, further technological advancement in developing this method of forecasting could prove highly beneficial in implementing a higher percentage of renewable energy sourced power plants into energy industry[2].

Anomaly Detection for Network Traffic of I&C Systems Based on Neural Network

2020 ◽  
Author(s):  
Wen Si ◽  
Jianghai Li ◽  
Ronghong Qu ◽  
Xiaojin Huang
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Traffic ◽  
Nuclear Power ◽  
Power Plants ◽  
Test Results ◽  
Level Control ◽  
Level Control System ◽  
The Neural Network ◽  
Unsupervised Neural Network

Abstract Anomaly detection is significant for the cybersecurity of the I&C systems at nuclear power plants. There are a large number of network packets generated in the network traffic of the I&C systems. There are many attributes of the network traffic can used for anomaly detection. The structure of the network packets is analyzed in detail with examples. Then, Features are extracted from network packets. An unsupervised neural network called autoencoder is applied for anomaly detection. Training and testing database are captured from a physical PLC system which simulates a water level control system. The result of the test results shows that the neural network can detect anomaly successfully.

Artificial Neural Network Model for Diagnosing the Performance and the Conditions of Air-Operated Valves

2020 ◽  
Author(s):  
Taeyun Kim ◽  
Jangbom Chai ◽  
Chanwoo Lim ◽  
Ilyoung Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Physical Models ◽  
Artificial Neural

Abstract Air-operated valves (AOVs) are used to control or shut off the flow in the nuclear power plants. In particular, the failure of safety-related AOV could have significant impacts on the safety of the nuclear power plants and therefore, their performances have been tested and evaluated periodically. However, the current method to evaluate the performance needs to be revised to enhance the accuracy and to identify defects of AOV independently of personal skills. This paper introduce the ANN (Artificial Neural Network) model to diagnose the performance and the condition altogether. Test facilities were designed and configured to measure the signals such as supply pressure, control pressure, actuator pressure, stem displacement and stem thrust. Tests were carried out in various conditions which simulate defects with leak/clogged pipes, the bent stem and so on. First, the physical models of an AOV are developed to describe its behavior and to parameterize the characteristics of each component for evaluating the performance. Secondly, CNN (Convolutional Neural Network) architectures are designed considering the developed physical models to make a lead to the optimal performance of ANN. To train the ANN effectively, the measured signals were divided into several regions, from each of which the features are extracted and the extracted features are combined for classifying the defects. In addition, the model can provide the parameters of maximum available thrust, which is the key factor in periodic verification of AOV with the required accuracy and classify more than 10 different kinds of defects with high accuracy.

scholarly journals Risk assessment of prospective investment projects for the construction of nuclear power plants abroad

2018 ◽  
Vol 7 (2.23) ◽  
pp. 251 ◽  
Author(s):  
Anna I. Guseva ◽  
Matvey V. Koptelov
Keyword(s):  
Risk Assessment ◽  
Construction Projects ◽  
Nuclear Power ◽  
Industrial Development ◽  
Power Plants ◽  
Energy Agency ◽  
Investment Projects ◽  
Development Organization ◽  
State Corporation

The article deals with the development of nuclear energy, classification of risks, approaches to risk assessment of investment projects of NPP construction abroad. Presented integrated methodology for assessing the risks of nuclear investment projects is based on the recommendations of the United Nations Industrial Development Organization (UNIDO), the International Atomic Energy Agency (IAEA), the sectoral methodological recommendations of the State corporation Rosatom. A way of accounting for risks in the calculation of economic efficiency is proposed. Calculations have been made for several real NPP construction projects (Rooppur NPP in Bangladesh, Astravets NPP in Belarus, Hanhikivi NPP in Finland). Analysis of the results of these projects shows that, taking into account all types of risks, its investment attractiveness has significantly decreased. 

scholarly journals Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

2021 ◽  
Vol 54 (6) ◽  
pp. 891-895
Author(s):  
Fawaz S. Abdullah ◽  
Ali N. Hamoodi ◽  
Rasha A. Mohammed
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Power Plants ◽  
Thermal Power ◽  
Machine Learning Algorithms ◽  
Circuit Board ◽  
Electric Circuits ◽  
The Neural Network ◽  
Artificial Neural

Artificial intelligence has proven its effectiveness in many industrial fields to enhance the existing functionality. Artificial intelligence and machine learning algorithms integrated with turbines can be useful in controlling important variables such as pressure, temperature, speed, and humidity. In this research, the Simulink library from MATLAB is used to build an artificial neural network. The NARMA L2 neural controller is used to generate data and for training networks. To obtain the result and compare it with the real-time power plant, data is collected. The input variables provided to the neural network have a large effect on the hidden layer and the output of the neural network. The circuit board used in this research has a DC bridge, a transformer and voltage regulators. The result comparison shows that the integration of artificial neural networks and electric circuits shows enhanced performance with high accuracy of prediction. It was observed that the ANN integration system and electric circuit design have a result deviation of less than 1%. This shows that the integration of ANN improves the performance of turbines.

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