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Prediction Of Coal H2 Content for Combustion Control In PC-fired Boilers Using Artificial Neural Network

DOI: 10.1615/ICHMT.2005.AustHeatMassTransfConf.340
page 4

H. B. Vuthaluru
Department of Chemical Engineering, Curtin University of Technology GPO,

M. O. Tade
Department of Chemical Engineering, Curtin University of Technology, GPO Box U1987 Western Australia 6845, Australia

H. M. Yao
Department of Chemical Engineering, Curtin University of Technology, GPO Box U1987 Western Australia 6845, Australia

Abstrakt

The solid nature of coal presents greater difficulties in measuring and controlling the combustion process compared to gas and oil fired power plants. Knowing the composition and energy content of coal can be very useful for combustion control. This paper describes the development and training of a feed-forward back-propagation artificial neural network (BPNN), which is used to predict the hydrogen content in coal from proximate analysis. It also describes a proposed design of a combustion control system that applies this network model. The ultimate objective is to enhance the performance of the combustion control system with the aid of regularly obtained knowledge of the elemental content of coal.

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