scholarly journals Research on Prediction of Vertical Water Wall Temperature of Power Station Boiler Based on Deep Learning and Expert Experience

2021 ◽  
Vol 1922 (1) ◽  
pp. 012011
Author(s):  
Xian-tao Du ◽  
Yu-jia Cui ◽  
Ming-hao Wang ◽  
Liang-wei Xia ◽  
Qiang Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wall Temperature ◽  
Power Station ◽  
Water Wall ◽  
Power Station Boiler

Model and Algorithm for Thermal Performance Calculation of Power Station Boiler Based on Process Steady-State Simulation Theory

2013 ◽  
Vol 483 ◽  
pp. 587-593
Author(s):  
Hong Kai Liao ◽  
Yue Xi Yu ◽  
Yan Ling Wu ◽  
Wei Zhong
Keyword(s):  
Steady State ◽  
Thermal Performance ◽  
Simulation Theory ◽  
General Purpose ◽  
Thermal Calculation ◽  
Process Unit ◽  
Power Station ◽  
Steady State Simulation ◽  
Power Station Boiler ◽  
Performance Calculation

Thermal performance calculation is the core task of designing power station boiler. By abstracting generalized components and generalized fluid nodes, and defining the process unit and process section at the logic level, the universal physical model of boiler was built in a particular form of flowsheet. Meanwhile, a sequential modular approach was proposed as the main algorithm for boiler thermal calculation based on process system steady-state simulation theory. Two key problems in the algorithm, i.e., module calculations and the logics of calling the modules calculations were explained. Finally, a practically developed system BESS, which has excellent flexibility and extensibility was presented. It turns out that the model and algorithm can be successfully employed in developing the general-purpose software for boiler thermal calculation.

Deep learning-based comprehensive monitor for smart power station

2021 ◽  
Vol 12 (4) ◽  
pp. 380
Author(s):  
Yerong Zhong ◽  
Guoheng Ruan ◽  
Jiaming Jiang
Keyword(s):  
Deep Learning ◽  
Power Station ◽  
Smart Power

scholarly journals Thermal station modelling and optimal control based on deep learning

2021 ◽  
Vol 25 (4 Part B) ◽  
pp. 2965-2973
Author(s):  
Min Cao
Keyword(s):  
Deep Learning ◽  
Thermal Power Station ◽  
Control Method ◽  
Learning Algorithm ◽  
Thermal Power ◽  
Heating System ◽  
Utilization Rate ◽  
Power Station ◽  
Deep Learning Algorithm ◽  
Deterministic Strategy

To solve the mismatch between heating quantity and demand of thermal stations, an optimized control method based on depth deterministic strategy gradient was proposed in this paper. In this paper, long short-time memory deep learning algorithm is used to model the thermal power station, and then the depth deterministic strategy gradient control algorithm is used to solve the water supply flow sequence of the primary side of the thermal power station in combination with the operation mechanism of the central heating system. In this paper, a large number of historical working condition data of a thermal station are used to carry out simulation experiment, and the results show that the method is effective, which can realize the on-demand heating of the thermal station a certain extent and improve the utilization rate of heat.

Analysis on Welding Crack of Subcritical Power Station Boiler Platen Superheater Header

2012 ◽  
Vol 251 ◽  
pp. 446-449 ◽  
Author(s):  
Yan Ting Feng ◽  
Xiang Feng Zheng ◽  
Xue Xia Xu ◽  
Guo Zhen Dong ◽  
Yong Wang ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Welding Residual Stress ◽  
Composition Analysis ◽  
Power Station ◽  
Element Analysis ◽  
Test Chemical ◽  
Welding Defects ◽  
Chemical Composition Analysis ◽  
Leakage Failure ◽  
Power Station Boiler

Welding crack analysis was carried out on tee joint crack of boiler platen superheater header (12Cr1MoV) of subcritical power station applying finite element analysis,microanalysis, mechanical properties test, chemical composition analysis, metallography examination and SEM observation etc. Results indicated that the improper structure, the original welding defects, higher welding heat input, excessive welding residual stress and improper heat treatment led to cold crack and reheat crack ,then induced to the leakage failure while operating.the welding crack originated at the original defects, connected with reheat cracks during propagation and led to leakage finally.

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