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Indexed by:Journal Papers

Date of Publication:2016-01-01

Journal:CONTROL ENGINEERING PRACTICE

Included Journals:SCIE、EI

Volume:46

Page Number:94-104

ISSN No.:0967-0661

Key Words:BFG generation; High level noises and outliers; Echo state networks; Quantile regression; Prediction intervals

Abstract:The usual huge fluctuations in the blast furnace gas (BFG) generation make the scheduling of the gas system become a difficult problem. Considering that there are high level noises and outliers mixed in original industrial data, a quantile regression-based echo state network ensemble (QR-ESNE) is modeled to construct the prediction intervals (PIs) of the BFG generation. In the process of network training, a linear regression model of the output matrix is reported by the proposed quantile regression to improve the generalization ability. Then, in view of the practical demands on reliability and further improving the prediction accuracy, a bootstrap strategy based on QR-ESN is designed to construct the confidence intervals and the prediction ones via combining with the regression models of various quantiles. To verify the performance of the proposed method, the practical data coming from a steel plant are employed, and the results indicate that the proposed method exhibits high accuracy and reliability for the industrial data. Furthermore, an application software system based on the proposed method is developed and applied to the practice of this plant. (c) 2015 Elsevier Ltd. All rights reserved.