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基于主成分分析（PCA）与GA-BP结合的转炉炼钢终点锰含量预测研究
发表时间：2019-04-30 浏览量：3596 下载量：789
| 全部作者： | 张壮，曹玲玲，林文辉，孙建坤，冯小明，刘青 |
| 作者单位： | 北京科技大学钢铁冶金新技术国家重点实验室；新余钢铁集团有限公司 |
| 摘 要： | 为提高转炉炼钢终点锰含量预测模型的预测精度，研究分析了影响转炉炼钢终点锰含量的因素，提出了将主成分分析（principal component analysis，PCA）与遗传算法（genetic algorithm，GA）和BP（back propagation）神经网络结合的数据驱动建模方法。使用PCA法对多个影响终点锰含量的因素进行降维处理，将处理后所得的主成分变量输入GA-BP模型进行训练而得到转炉炼钢终点锰含量预测模型。通过将PCA-GA-BP模型预测结果与GA-BP神经网络模型预测结果相比较，结果显示，基于PCA的GA-BP模型的预测精度较高，泛化性能好，预测误差在±0.025%范围内的命中率达到86%，均方误差为2.74×10^-8，且模型的训练速度有了显著提升。 |
| 关 键 词： | 钢铁冶金；转炉；终点锰含量；BP神经网络；遗传算法；主成分分析 |
| Title： | Prediction study of end-point manganese content for BOF steelmaking process based on principal component analysis (PCA) and GA-BP |
| Author： | ZHANG Zhuang, CAO Lingling, LIN Wenhui, SUN Jiankun, FENG Xiaoming, LIU Qing |
| Organization： | State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing; Xinyu Iron & Steel Co. Ltd. |
| Abstract： | In order to improve the prediction accuracy of the prediction model of end-point manganese content for BOF steelmaking process, a data-driven modeling method based on the combination of principal component analysis (PCA) and genetic algorithm-black propagation (GA-BP) neural network was presented by analyzing its influential factors of end-point manganese content during BOF steelmaking process. By using PCA, the amount of variables which affected the end-point manganese content would be reduced. Then the principal components were employed to train the GA-BP neural network in order to obtain the prediction results of end-point manganese content for BOF steelmaking process. Comparing the prediction results of GA-BP neural network and PCA-GA-BP models, the results showed that GA-BP neural network prediction modeling method based on PCA method had the better prediction accuracy and the better generalization performance. The hit ratio of the model was 86% when the predictive errors of the model were within ±0.025%, and the mean square error was 2.74×10^-8. And this modeling method has high training speed. |
| Key words： | ferrous metallurgy; basic oxygen furnace; end-point manganese content; BP neural network; genetic algorithm; principal component analysis (PCA) |
| 发表期数： | 2019年4月第2期 |
| 引用格式： | 张壮，曹玲玲，林文辉，等. 基于主成分分析（PCA）与GA-BP结合的转炉炼钢终点锰含量预测研究[J]. 中国科技论文在线精品论文，2019，12（2）：328-335. |
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