摘要
针对传统高炉温度模型的固有缺陷,提出了一种基于灰色关联分析的ELM(极限学习机—extreme learning machine)温度预报模型。由于炼铁工艺的多变量、非线性、强耦合等特点,所以传统建模方法已经不能满足要求的高精度预报高炉温度。首先通过灰色关联分析对输入变量进行相关性分析,提高模型的性能,然后结合分析后的变量采用ELM学习算法训练神经网络,最后运用现场数据对该网络进行训练和测试,并与传统的模型相比较。结果表明该模型能快速、准确地预报高炉温度,并且能满足指导现场工人操纵高炉的要求。
Aiming at the inherent defects of the traditional blast furnace temperature model, a kind of grey relational analysis based ELM （extreme learning machine） temperature prediction model was put forward. Due to the characteristics of ironmaking process with multivariable nonlinear, strong coupling, the traditional modeling methods were unable to meet the requirements of high precision prediction of blast furnace temperature. Firstly, the correlation of input variables was analyzed with the gray correlation analysis, and then the performance of the model was improved. Secondly, combined with analytical variables, the neural network was trained by ELM learning algorithm. Finally, the field data was used for training and testing of the network, and then compared with the traditional model. The results show that the model can predict the blast furnace temperature quickly and accurately, and also can meet the guide workers to manipulate the needs of blast furnace.
出处
《钢铁研究学报》
CAS
CSCD
北大核心
2015年第11期33-37,共5页
Journal of Iron and Steel Research
基金
国家自然科学基金资助项目(61164018)
关键词
灰色关联
极限学习机
高炉
铁水温度预报
神经网络
grey correlation
extreme learning machine
blast furnace
temperature prediction of molten iron
neural network
