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[%P] prediction and control model for oxygen-converter process at the end point based on adaptive neuro-fuzzy system

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3 Author(s)
Yang Lihong ; Metall. Tech. Dept., Central Iron & Steel Res. Inst., Beijing, China ; Liu Liu ; He Ping

According to the process and data from spot, the methodology for [%P] prediction and control is discussed. A self-organizing network has been utilized to classify 303 heats from spot, which makes the analysis of the influence of steelmaking variables on [%P] possible. The control variables for the [%P] prediction and control model were determined with the analysis. A model of [%P] prediction and control has been established for BOF at the end point based on an adaptive neuro-fuzzy system. The results show that this model has good performance on prediction and control for [%P] in the BOF process. The R-value of model output and actual [%P] in the experiment reaches 0.5867. The hit rate of the model in the precision ±0.003% [%P] is 79.21%. With this model, if the [%P] was controlled by the model with the value less than target by 0.004%, 91% of heats are up to grade in regard to [%P].

Published in:

Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on  (Volume:3 )

Date of Conference: