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| Short-term Prediction of Linz-Donawitz Gas Generation Tendency Based on SVD-NCF-GA-BP |
| Zhimin Lv, Ting Li, Zhao Wang, Ziyang Wang |
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Abstract The prediction of Linz-Donawitz Gas (LDG) production and consumption
tendency was paramount important in gas balancing and scheduling
since it's an important secondary energy which each process in the
steel and iron enterprise needed. Therefore, this paper proposed a
prediction method combining curve fitting and GA optimized BP neural
network to predict LDG short-term production trend. Specifically,
proposed method firstly utilized SVD decomposition to preprocess
instantaneous values of LDG produc-tion in order to extract a
standard type of LDG production during a smelting cycle. Then the
standard type was curve fitted to attain function formulas of the
overall recovery about time series and meanwhile a series of
function clusters and values were procured. Afterwards, GA optimized
BP neural network was employed to train parameters of function
clusters and thus a recovery trend of LDG during a production period
was obtained, which was also called the prediction of short-time
production trend. Finally, the actual data from a certain steel and
iron enterprise was adopted to verify feasibility and efficiency of
the proposed method, the results showed that proposed method had a
good performance in predicting short-term LDG generation trend.
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| DOI:10.12733/jics20105771 |
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