工程科学学报2017，Vol.39Issue(4)：511-519,9.DOI:10.13374/j.issn2095-9389.2017.04.005
BP神经网络IF钢铝耗的预测模型
Prediction model of aluminum consumption with BP neural networks in IF steel production
摘要
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 0.08kg 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 0.1kg 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 100m3 and greater than 100m3,the ratio of oxygen reacting with aluminum is about 37.3％or about 74.6％respectively,and the aluminum consumption increases by 0.1kg or 0.2kg,respectively,with the blowing oxygen quantity increasing by 50m3.After the process parameter optimization,the aluminum consumption decreases from 1.359 to 1.113kg,which results in a decrease of 18.1％.关键词
IF钢/低碳钢/铝耗/神经网络/预测模型Key words
IF steel/low carbon steel/aluminum consumption/neural networks/prediction models分类
矿业与冶金引用本文复制引用
张思源,包燕平,张超杰,林路..BP神经网络IF钢铝耗的预测模型[J].工程科学学报,2017,39(4):511-519,9.基金项目
国家自然科学基金资助项目(51404022) （51404022）
钢铁冶金新技术国家重点实验室自主课题(41616003) （41616003）
