Modelling coal gasification with a hybrid neural network Academic Article

abstract

• Gasification of two coals was carried out in a batch feed fluidized bed reactor at atmospheric pressure using steam as fluidizing medium. A model of coal gasification was developed, incorporating a first-principles model with a neural network parameter estimator. The hybrid neural network was trained with experimental data for the two coals and gave good performance in process modelling. A parameter for the overall reactivity of char, namely 'active char ratio' (ACR), was identified by the neural network, as a function of gasification time and temperature. The ACR profile showed a strong dependence on coal type. Other parameters estimated by the neural network also reflected distinct characteristics of the two coals. 1997 Elsevier Science Ltd.

authors

• Guo, Bing

published proceedings

• FUEL

author list (cited authors)

• Guo, B., Shen, Y. T., Li, D. K., & Zhao, F.

citation count

• 30

complete list of authors

• Guo, B||Shen, YT||Li, DK||Zhao, F

publication date

• October 1997

publisher

• Elsevier  Publisher

published in

• Fuel  Journal

keywords

• Coal Gasification
• Mathematical Modelling
• Neural Networks

Digital Object Identifier (DOI)

• 10.1016/s0016-2361(97)00122-1

start page

• 1159

end page

• 1164

volume

• 76

issue

• 12

URL

• http://dx.doi.org/10.1016/s0016-2361(97)00122-1