Model Parameters Identification for Excess Oxygen by Standard Genetic Algorithm

被引：0

作者：

Rajarathinam, Kumaran ^{[1
]}

Gomm, J. Barry ^{[1
]}

Yu, DingLi ^{[1
]}

Abdelhadi, Ahmed Saad ^{[1
]}

机构：

[1] Liverpool John Moores Univ, Sch Engn, Control Syst Grp, Mech Engn & Mat Res Ctr MEMARC, Byrom St, Liverpool L3 3AF, Merseyside, England

来源：

2016 22ND INTERNATIONAL CONFERENCE ON AUTOMATION AND COMPUTING (ICAC) | 2016年

关键词：

excess oxygen; model parameters identification; genetic algorithm; methane combustion; glass furnace;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a realistic excess oxygen model parameter identification by Standard Genetic Algorithms (SGAs) is proposed and demonstrated. The realistic excess oxygen model is developed by three sub-model; air-fuel ratio conversion model, dynamic continuous transfer function and excess oxygen look-up table to characterise the real excess oxygen plant's numerical data. The predetermined time constant approximation method is applied on 1st, 2nd, 3rd, 4th and 5th model orders for an initial value estimation with SGAs. For an optimal model order assessment and selection, the information criteria are applied. The simulation results assured that the 4th order continuous transfer function as a realistic model well characterises the real excess oxygen plant's response.

引用

页码：198 / 203

页数：6

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