Noise variance estimate for blast furnace temperature of hot metal based on Autoregressive model in presence of noise

Author

Yong Zhang ; Zhe Zhao ; Guimei Cui

Author_Institution

Sch. of Inf. Eng., Inner Mongolia Univ. of Sci. & Technol., Baotou, China

fYear

2015

fDate

23-25 May 2015

Firstpage

6461

Lastpage

6465

Abstract

Noise variance is an important variable for data filtering. In order to estimate the noise variance of hot iron temperature in process of blast furnace (BF) ironmaking, this work will study parameter estimate of AutoRegressive (AR) process in presence of noise based on BF observed data. Furthermore, a given instrumental variable choosing method and recursive least squares algorithm will be delivered in this paper. The proposed method requires loose assumptions, which are more close to the data fact in blast furnace ironmaking process. Finally, noise variance estimate results are shown by simulation tests.

Keywords

acoustic signal processing; autoregressive moving average processes; blast furnaces; filtering theory; least squares approximations; production engineering computing; steel manufacture; autoregressive model; blast furnace ironmaking; blast furnace temperature; data filtering; hot iron temperature; hot metal; noise variance estimation; recursive least squares algorithm; Blast furnaces; Instruments; Mathematical model; Noise; Noise measurement; Signal processing algorithms; Blast Furnace; Noise Variance; Noisy AutoRegressive (AR) Model; Parameter Estimate;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2015 27th Chinese

Conference_Location

Qingdao

Print_ISBN

978-1-4799-7016-2

Type

conf

DOI

10.1109/CCDC.2015.7161982

Filename

7161982