Title :
A cascaded neural network and its application to modelling power plant pollutant emission
Author :
Li, Kang ; Thompson, Stephen
Author_Institution :
Dept. of Mech. & Manuf. Eng., Queen´´s Univ., Belfast, UK
Abstract :
Pollutant emission in power generation plant, especially NOx emission, has attracted much attention in the last ten years. For an operating plant, development of an advanced control system is essential for pollutant emission reduction. System modelling is an important stage in emission control. In this paper, a cascading neural network is used to model the NOx emission in a coal-fired power generation plant. This type of neural network has more connections than that found in the layered feedforward neural network. Algorithm for training this type of neural network is suggested and then it is used to build a NOx emission model for a coal-fired power generation plant. Simulation results show the merits of this type of neural network
Keywords :
air pollution control; cascade control; learning (artificial intelligence); neurocontrollers; power generation control; power plants; steam power stations; NOx emission; advanced control system; cascaded neural network; coal-fired power generation plant; emission control; pollutant emission reduction; power generation plant; power plant pollutant emission modelling; training; Air pollution; Artificial neural networks; Environmentally friendly manufacturing techniques; Feedforward neural networks; Function approximation; Neural networks; Nitrogen; Power engineering and energy; Power generation; Power system modeling;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.863383
