基于ICEEMDAN-KPCA-RVM的铁水温度预测  

Prediction of molten iron temperature based on ICEEMDAN-KPCA-RVM

作　　者：许威 何朝辉 杨凯[1,2] 李文岗 肖清泰[1,2] XU Wei;HE Zhaohui;YANG Kai;LI Wengang;XIAO Qingtai(State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization,Kunming University of Science and Technology,Kunming 650093,China;Faculty of Metallurgical and Energy Engineering,Kunming University of Science and Technology,Kunming 650093,China;Design&Research Institute Co.,Ltd.,Kunming University of Science and Technology,Kunming 650051,China)

机构地区：[1]昆明理工大学省部共建复杂有色金属资源清洁利用国家重点实验室,云南昆明650093 [2]昆明理工大学冶金与能源工程学院,云南昆明650093 [3]昆明理工大学设计研究院有限公司,云南昆明650051

出　　处：《冶金自动化》2024年第1期18-25,共8页Metallurgical Industry Automation

基　　金：云南省科技厅基础研究计划项目(202201BE070001-026,202101AU070031);云南省科技人才与平台计划项目(202305AO350011);昆明理工大学学科交叉研究专项(KUST-xk2022001)。

摘　　要：为解决高炉铁水温度传统单一预测模型存在的模型精度不高、鲁棒性差等难题,提出了一种融合改进的自适应噪声完备集合经验模态分解(intrinsic computing expressive empirical mode decomposition with adaptive noise, ICEEMDAN)、核主成分分析(kernel principal component analysis, KPCA)和相关向量机(relevance vector machine, RVM)的组合模型来精准和稳定预测铁水温度。首先,利用ICEEMDAN对铁水温度时间序列进行分解,以获取若干本征模态函数。然后,利用KPCA对钢铁生产过程中的多维关键变量进行降维处理,提取关键变量的主要特征。最后,利用RVM对降维后的变量分别预测铁水温度时间序列,得到铁水温度的累加预测结果。结果表明,相较于传统的自适应噪声完备集合经验模态分解模型(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN),新模型的均方根误差(root mean square error, RMSE)减少了13.0%,训练速度提升了10.9%,能够更好地理解铁水温度的动态变化规律;相较于单一的RVM等传统模型,新模型的平均绝对误差(mean absolute error, MAE)减少了2.47,训练时间缩短了0.463 s,具有模型精度更高和速度更快的优势。因此,新模型为高炉温度实时调控提供了理论支持,对保证高炉冶炼稳定性、实施冶金过程智能化具有实际意义。To address the challenges of low model accuracy and poor robustness in traditional single models for blast furnace molten iron temperature prediction,a combined model was proposed.The model integrates the intrinsic computing expressive empirical mode decomposition with adaptive noise(ICEEMDAN),kernel principal component analysis(KPCA)and relevance vector machine(RVM)to achieve precise and stable predictions of molten iron temperature.Firstly,ICEEMDAN is employed to decompose the time series of molten iron temperature,yielding several intrinsic mode functions.Secondly,KPCA is applied to reduce the dimensionality of the multidimensional key variables in the steel production process and extract the main features of these key variables.Lastly,RVM is utilized to predict the molten iron temperature time series based on the reduced variables,resulting in the cumulative prediction results of molten iron temperature.The results demonstrate that compared to the traditional complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)model,the new model exhibits a 13.0%reduction in root mean square error(RMSE)and a 10.9%increase in training speed,enabling a better understanding of the dynamic changes in molten iron temperature.Compared to traditional single models like RVM,the new model reduces the mean absolute error(MAE)by 2.47 and the training time by 0.463 s,which has the advantages of higher model accuracy and faster speed.Thus,the proposed model provides theoretical support for real-time control of blast furnace temperature,which is of practical significance in ensuring the stability of blast furnace smelting and accelerating the metallurgical process towards intelligent automation.

关 键 词：铁水温度 智能预测 相关向量机 改进的自适应噪声完备集合经验模态分解 核主成分分析 

分 类 号：TF53[冶金工程—钢铁冶金] TP18[自动化与计算机技术—控制理论与控制工程]