Abstract
Gas flow temperature of blast furnace is one of significant indicators of judging the anterograde state of blast furnace. Aiming at the defects of traditional gas flow temperature prediction model, this paper proposes a prediction model for gas flow temperature based on improved regularized extreme learning machine algorithm (RELM). Firstly, influencing factors are chosen through analyzing the distribution of blast furnace gas flow. Considering the blast furnace production data contain high frequency noise, the Fourier transform method is used for spectrum analysis, and the appropriate digital filter is selected according to the spectrum analysis result to eliminate the high frequency noise. Secondly, this paper establishes the prediction model of gas flow temperature. Regularized extreme learning machine and finite memory recursive least squares are combined to overcome Data saturation problem of ordinary extreme learning machine. And the improved algorithm is named as RFM-RELM. Finally, actual production data are used to train and test this model. Experimental result indicate that the model can quickly and accurately predict gas flow temperature ,which provides effective help and support for blast furnace operators to analyze the conditions of blast furnace.
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Acknowledgments
This work was supported by National Natural Science Foundation (NNSF) of China under Grant 61673056, 61673055 and the Beijing Natural Science Foundation under Grant 4182039.
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Wu, X., Zhang, S., Su, X., Yin, Y. (2020). The Improved Regularized Extreme Learning Machine for the Estimation of Gas Flow Temperature of Blast Furnace. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 592. Springer, Singapore. https://doi.org/10.1007/978-981-32-9682-4_30
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DOI: https://doi.org/10.1007/978-981-32-9682-4_30
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