Prediction model of aluminum consumption with BP neural networks in IF steel production
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摘要: 為了解決某鋼廠IF鋼冶煉RH精煉過程鋁耗偏高問題,通過數理統計和BP神經網絡相結合的方法建立了鋁耗預測模型,并與多元線性回歸模型進行比較,該模型具有更高準確度.該模型分析了不同冶煉工藝參數對鋁耗的具體影響,并對相應工藝參數進行了優化.結果表明:脫碳結束氧活度或RH進站氧活度降低0.005%左右,每噸鋼鋁耗可降低0.07~0.08 kg,鋁脫氧有效利用系數為70.31%~80.35%;RH進站鋼液溫度增加35~40℃,鋁耗降低1 kg左右,鋁熱反應升溫利用系數在97.4%左右;吹氧量小于100 m3和大于100 m3時,氧氣與鋁反應的比例分別為37.3%和74.6%左右,吹氧量每增加50 m3,鋁耗分別增加0.1 kg和0.2 kg左右.工藝參數優化后平均鋁耗由1.359 kg降低到1.113 kg,降幅達18.1%.Abstract: To solve the high aluminum consumption problem in interstitial-free steel production in a steel plant, an aluminum consumption prediction model was established by mathematical statistics and BP neural networks. Compared with the multiple linear regression model, this model's result is more accurate. The influence of different smelting processes on aluminum consumption was analyzed, and the process parameters were optimized. The results show that the amount of aluminum consumption per ton of steel decreases 0.07 to 0.08 kg when the oxygen activity before RH or after decarbonization reduces by 0.005%. The effective utilization coefficient of aluminum-deoxidizing is from 70.31% to 80.35%; the aluminum consumption decreases about 0.1 kg when the temperature of steel before RH increases by 35 to 40℃. The heating utilization coefficient of aluminum thermal reaction is about 97.4%. When the blowing oxygen quantity is less than 100 m3 and greater than 100 m3, the ratio of oxygen reacting with aluminum is about 37.3% or about 74.6% respectively, and the aluminum consumption increases by 0.1 kg or 0.2 kg, respectively, with the blowing oxygen quantity increasing by 50 m3. After the process parameter optimization, the aluminum consumption decreases from 1.359 to 1.113 kg, which results in a decrease of 18.1%.
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Key words:
- IF steel /
- low carbon steel /
- aluminum consumption /
- neural networks /
- prediction models
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參考文獻
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