Adaptive soft measurement method for the furnace exit gas temperature based data-driven method

被引：0
|
作者：
Tang, Zhenhao [1 ]
Zhang, Haiyang [1 ]
Li, Jian [1 ]
Su, Qingyu [1 ]
机构：
[1] Northeast Dianli Univ, Coll Automat Engn, Jilin 132012, Peoples R China
来源：
PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) | 2016年
关键词：
furnace exit gas temperature; power plant boiler; soft measurement; least square support vector machine (LSSVM); differential evolution algorithm (DE); PREDICTIVE CONTROL; MODEL;
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：
The furnace exit gas temperature (FEGT) is one of the significant variables for the control of power plant boiler. It is a great challenge to obtain the FEGT online since the high temperature, high pressure and high noise. To solve this problem, a soft measurement method is proposed by fully use of the historical data based on the differential evolution algorithm and least square support vector machine (LSSVM). In this algorithm, the relative variables are selected by the correlation analysis and mechanism analysis. Then the data model is constructed by LSSVM. The DE algorithm is utilized to improve the stability and accurate by optimize the parameters of LSSVM. The experimental results illustrate the effectiveness of proposed algorithm.
引用
收藏
页码：4681 / 4685
页数：5
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