Forecasting Thermal State in Blast Furnace Using Fuzzy Prediction Controller Optimized by Wavelet De-Noising

Abstract:

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A new fuzzy prediction controller is established to prediction for silicon content in blast furnace hot metal. The forecasting process is only used the historical information of silicon content. This new algorithm consists of five steps: step 1 de-noises silicon content numerical data by wavelet analysis to smooth out noise; step 2 divides the input and output spaces of the de-noising numerical data into fuzzy regions; step 3 generates fuzzy rules from the de-noising data; step 4 assigns a degree to each of the generated rules for the purpose of resolving conflicts among the generated rules; step 5 determines a fuzzy prediction controller from input space to output space based on such rules. Simulation results show that: 84% hit rates of prediction in the range of [Si] 0.1% is attained using the prediction controller.

Info:

Periodical:

Key Engineering Materials (Volumes 439-440)

Edited by:

Yanwen Wu

Pages:

1612-1617

DOI:

10.4028/www.scientific.net/KEM.439-440.1612

Citation:

S. H. Luo "Forecasting Thermal State in Blast Furnace Using Fuzzy Prediction Controller Optimized by Wavelet De-Noising", Key Engineering Materials, Vols. 439-440, pp. 1612-1617, 2010

Online since:

June 2010

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Price:

$35.00

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