Blast Furnace Cross Temperature Prediction Based on Data-driven and Intelligent Optimization

TANG Zhenhao; TANG Lixin; YANG Yang

doi:10.3724/SP.J.1219.2014.00355

Volume 43 Issue 3

Jun. 2014

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INFORMATION AND CONTROL > 2014 > 43(3): 355-360. > DOI: 10.3724/SP.J.1219.2014.00355 CSTR: 32166.14.xk.2014.00355

TANG Zhenhao, TANG Lixin, YANG Yang. Blast Furnace Cross Temperature Prediction Based on Data-driven and Intelligent Optimization[J]. INFORMATION AND CONTROL, 2014, 43(3): 355-360. DOI: 10.3724/SP.J.1219.2014.00355

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Blast Furnace Cross Temperature Prediction Based on Data-driven and Intelligent Optimization

TANG Zhenhao^1,2,
TANG Lixin^1,3,
YANG Yang^1,3, ,

1.
The Logistic Institute, Northeastern University, Shenyang 110819, China
2.
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
3.
Liaoning Key Laboratory of Manufacturing System and Logistics, Northeastern University, Shenyang 110819, China

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Received Date: September 24, 2013
Revised Date: February 27, 2014
Published Date: June 19, 2014

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Abstract

Abstract

Least square support vector machine (LS-SVM) is employed to model of the blast furnace cross temperature. First,correlation analysis is performed to select inputs related to cross temperature. Second,an improved particle swarm optimization (PSO) is proposed to obtain optimized parameters for LS-SVM in order to improve the prediction accuracy. Finally,the prediction model of blast furnace cross temperature based on LS-SVM is achieved. Experiments using practical production data illustrate that the input selection based on correlation analysis can reduce the computation complexity without influencing the prediction accuracy. Compared with the grid search method,the proposed data-driven cross temperature model represents an improvement in accuracy of 3%,and can meet the requirements of production.
- blast furnace,
- cross temperature,
- least square support vector machine,
- particle swarm optimization,
- data-driven

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Blast Furnace Cross Temperature Prediction Based on Data-driven and Intelligent Optimization

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