scholarly journals A Neural-Network-Based Method for Ash Fouling Prediction of Heat Transfer Surface in Coal-fired Power Plant Boiler

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yuanhao Shi ◽  
Mengwei Li ◽  
Fangshu Cui ◽  
Jie Wen ◽  
Jianchao Zeng
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Power Plant ◽  
Heat Transfer Surface ◽  
Power Plant Boiler ◽  
Ash Fouling ◽  
Plant Boiler

Soft Measuring Model Based on CMAC Artificial Neural Network for Pollutants Release from Coal Combusting in Power Plant Boiler

2012 ◽  
Vol 518-523 ◽  
pp. 2192-2195
Author(s):  
Yong Zheng Wang ◽  
Lei Jiang ◽  
Sheng Xue Chen ◽  
Chun Mei Lu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Plant ◽  
Pollutant Emission ◽  
Power Plant Boiler ◽  
Boiler Operation ◽  
Pollutants Emission ◽  
On Line ◽  
Artificial Neural ◽  
Plant Boiler

The soft measuring model, for pollutants release from coal combusting in power plant boiler, was combined with the test data obtained from boiler operation, established on the basis of technology for CMAC artificial neural network, and adopted the hyper CMAC structure and algorithm. The characteristic data about coal quality from boiler operation and combusting conditions in the furnace were took as its input parameters, to achieve precise prediction and on-line measurement of concentration of sulfur and nitrogen pollutant emission. It was effective to guide operator in the power plant to optimize combustion and control pollutants emission.

scholarly journals An Optimization Study on Soot-Blowing of Air Preheaters in Coal-Fired Power Plant Boilers

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 958 ◽  
Author(s):  
Yuanhao Shi ◽  
Jie Wen ◽  
Fangshu Cui ◽  
Jingcheng Wang
Keyword(s):  
Power Plant ◽  
Ash Deposition ◽  
Computing Systems ◽  
Air Preheater ◽  
Heat Transfer Efficiency ◽  
Optimization Study ◽  
Power Plant Boiler ◽  
Ash Fouling ◽  
Cleanliness Factor ◽  
Plant Boiler

This paper presents a comprehensive approach for optimization of soot-blowing of air preheaters in a coal-fired power plant boiler. In the method, modeling of the cleanliness factor is firstly proposed to monitor the ash deposition status of the air preheaters. Then, the statistical fitting of the ash fouling status is subsequently obtained to analyze the ash fouling dynamics and assessment of optimized soot-blowing strategies. Soot-blowing strategies are finally developed to optimize the steam consumption and heat transfer efficiency. Our methods can achieve the fouling monitoring and soot-blowing optimization of air preheater (APH) by using the existing monitoring data, not requiring additional special instruments and complex computing systems. The methodology is validated with the actual operating data of a 300 MW coal-fired power plant boiler. The results show the effectiveness of the proposed method. It can be used for the soot-blowing optimization in most coal-fired power plant boiler with air preheaters.

MtsPSO-PID Neural Network Decoupling Control in Power Plant Boiler

2013 ◽  
Vol 46 (20) ◽  
pp. 101-105
Author(s):  
Yong Zhang ◽  
Ting Luan ◽  
Yuan Yao
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Decoupling Control ◽  
Power Plant Boiler ◽  
Pid Neural Network ◽  
Plant Boiler

Application of a Multivariable Self-Tuning Controller to a Power Plant Boiler

1984 ◽  
Author(s):  
Majid Amin ◽  
Glenn Y. Masada ◽  
Baxter F. Womack
Keyword(s):  
Power Plant ◽  
Power Plant Boiler ◽  
Self Tuning ◽  
Plant Boiler
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