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.

Artificial Neural Network and the Taguchi Method Application for Robust Wheatstone Bridge Design

1994 ◽  
Author(s):  
Jung-eui Hong ◽  
Cihan H. Dagli ◽  
Kenneth M. Ragsdell
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wheatstone Bridge ◽  
Parameter Design ◽  
System Response ◽  
Measurement Circuit ◽  
Wide Range ◽  
Reduce Measurement Error ◽  
On Line ◽  
Artificial Neural

Abstract The primary function of the Wheatstone bridge is to measure an unknown resistance. The elements of this well-known measurement circuit will take on different values depending upon the range and accuracy required for a particular application. The Taguchi approach to parameter design is used to select values for the measurement circuit elements so as to reduce measurement error. Next we introduce the use of an artificial neural network to extrapolate limited experimental results to predict system response over a wide range of applications. This approach can be employed for on-line quality control of the manufacture of such device.

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