Title: Establish the multi-source data fusion model of the shape of blast furnace burden surface based on co-universal kriging estimation method

Authors: Liangliang Miao; Xianzhong Chen; Shilong Zhao; Zhenlong Bai

Addresses: School of Automation & Electrical Engineering, University of Science and Technology Beijing, Key Laboratory of Advanced Control for Iron and Steel Process Ministry of Education, Beijing 100083, China; Capital Engineering & Research Incorporation Limited, Beijing 100176, China ' School of Automation & Electrical Engineering, University of Science and Technology Beijing, Key Laboratory of Advanced Control for Iron and Steel Process Ministry of Education, Beijing 100083, China ' School of Automation & Electrical Engineering, University of Science and Technology Beijing, Key Laboratory of Advanced Control for Iron and Steel Process Ministry of Education, Beijing 100083, China ' School of Automation & Electrical Engineering, University of Science and Technology Beijing, Key Laboratory of Advanced Control for Iron and Steel Process Ministry of Education, Beijing 100083, China

Abstract: This paper presents a multi-source data fusion model method which could improve the blast furnace (BF) burden surface model accuracy. First, the three sections of straight line are used to describe the cross section of BF burden surface, and apply the motion law of the furnace burden to constrain the specific parameters of the three sections of straight line. Secondly, a multi-source data fusion method based on co-universal kriging estimation method is proposed. The temperature and height data are combined to build the unbiased estimation for the burden surface shape. Finally, an example of surface shape model using our proposed method in a 2500 m³ BF of a steel plant is discussed. The application shows that, contrasted with the traditional model, the model accuracy has arisen by 8%, and the resolution of surface shape has arisen by 0.32. The novel method can provide necessary guidance for energy saving and emission reduction in operation of the BF.

Keywords: burden surface shape; co-universal kriging; data fusion models; modelling; blast furnaces; kriging estimation; steel industry; energy saving; emissions reduction.

DOI: 10.1504/IJSNET.2014.064430

International Journal of Sensor Networks, 2014 Vol.15 No.4, pp.231 - 237

Received: 05 Feb 2014
Accepted: 11 Feb 2014
Published online: 25 Aug 2014 *

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