Main Steam Temperature Modeling Based on Levenberg-Marquardt Learning Algorithm

被引：4

作者：

Mazalan, N. A. ^{[1
]}

Malek, A. A. ^{[2
]}

Wahid, Mazlan A. ^{[3
]}

Mailah, M. ^{[3
]}

Saat, Aminuddin ^{[3
]}

Sies, Mohsin M. ^{[3
]}

机构：

[1] Fac Mech Engn, Utm Skudai 81310, Johor, Malaysia

[2] Malakoff Corp Berhad, Pontian Johor, Malaysia

[3] Univ Teknol Malaysia, Fac Mech Engn, Dept Thermofluids, High Speed Reacting Flow Lab HiREF, Skudai, Malaysia

来源：

ADVANCES IN THERMOFLUIDS | 2013年 / 388卷

关键词：

Main Steam Temperature; Neural Network; Levenberg-Marquardt Learning Algorithm; THERMAL POWER-PLANTS;

D O I：

10.4028/www.scientific.net/AMM.388.307

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Main steam temperature is one of the most important parameters in coal fired power plant. Main steam temperature is often described as non-linear and large inertia with long dead time parameters. This paper present main steam temperature modeling method using neural network with Levenberg-Marquardt learning algorithm. The result of the simulation showed that the main steam temperature modeling based on neural network with Levenberg-Marqurdt learning algorithm is able to replicate closely the actual plant behavior. Generator output, main steam flow, main steam pressure and total spray water flow are proven to be the main parameters affected the behavior of main steam temperature in coal fired power plant.

引用

页码：307 / +

页数：2

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