Prediction of coal hydrogen content for combustion control in power utility using neural network approach

被引：42

作者：

Saptoro, A. ^{[1
]}

Yao, H. M. ^{[1
]}

Tade, M. O. ^{[1
]}

Vuthaluru, H. B. ^{[1
]}

机构：

[1] Curtin Univ Technol, Dept Chem Engn, Perth, WA 6845, Australia

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2008年 / 94卷 / 02期

关键词：

Artificial neural network modeling; pc-fired boilers; Coal elemental prediction; Hydrogen; Proximate analysis;

D O I：

10.1016/j.chemolab.2008.07.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 [计算机科学与技术];

摘要：

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 in coal-fired power utilities. In this work, an attempt is made to establish relationships between the hydrogen composition of coal and available data from the proximate analysis. In the present work, artificial neural network based model is developed for the prediction of hydrogen content. For practical implications, a combustion control system utilising the neural network based model is also proposed to show the potential for coal-fired utilities. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：149 / 159

页数：11

共 26 条

[1]

Studies of the relationship between petrography and grindability for Kentucky coals using artificial neural network [J].