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Indexed by:会议论文

Date of Publication:2014-08-24

Included Journals:EI、CPCI-S、CPCI-SSH

Volume:47

Issue:3

Page Number:1041-1046

Key Words:Echo state network; gas pipeline pressure; prediction interval; Bayesian framework.

Abstract:The pipeline pressure of blast furnace gas (BFG) system in steel industry provides effective information for the energy scheduling operations. However, due to the complexity of the byproduct gas pipeline network and the large fluctuations of the gas flow, it is rather difficult to establish an accurate prediction model for the pipeline pressure. Additionally, the quantitative reliability of the prediction accuracy is the key concerns of energy scheduling workers since there are always a variety of uncertainties in industrial process. In this study, an echo state network (ESN) modelling with output feedback is proposed to predict the BFG pipeline pressure. Given the gas flows data and the pressures sampled from the sensors are generally accompanied with noise, a Bayesian framework for the prediction intervals (PIs) is designed, which can quantify the input and output noises. To verify the effectiveness of the proposed method, a number of prediction experiments coming from industrial data are conducted here. And, the experimental results indicate that the proposed approach has a satisfactory performance on PIs for the pipeline pressure.