Data-Driven Thermal Efficiency Modeling and Optimization for Co-firing Boiler

被引：0
|
作者：
Wang, Jian-Guo [1 ]
Wang, Juan-Juan [1 ]
Xiao, Qian-Ping [1 ]
Ma, Shi-Wei [1 ]
Rao, Wen-Tao [2 ]
Zhang, Yong-Jie [2 ]
机构：
[1] Shanghai Univ, Shanghai Key Lab Power Stn Automat Technol, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China
[2] Baosteel Res Inst, Inst Environm & Resources, Shanghai AH-201900, Peoples R China
来源：
26TH CHINESE CONTROL AND DECISION CONFERENCE (2014 CCDC) | 2014年
关键词：
Multi-fuel boiler; thermal efficiency; variable selection; data-driven; statistics analysis;
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 changes of flow rate and heating value of blast furnace gas (BFG) make the boiler operation more like art than science. In this paper, statistics analysis methods are utilized to justify the significance of the derived variables for the thermal efficiency modeling. By employing nonnegative garrote (NNG) variable selection and auto-regression integrated moving average (ARIMA) correction, an adaptive scheme for thermal efficiency modeling and adjustment is proposed and virtually implemented for a BFG/coal co-firing boiler. The detail analysis shows that there is large room for energy conservation when the boiler operation shifts from the present practice to the model-based control.
引用
收藏
页码：3608 / 3611
页数：4
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