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 de-composition 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.
Prediction of molten iron temperature based on ICEEMDAN-KPCA-RVM
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 varia-bles 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(CEEM-DAN)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.
molten iron temperatureintelligent predictionrelevance vector machine(RVM)intrin-sic computing expressive empirical mode decomposition with adaptive noise(ICEEMDAN)kernel principal component analysis(KPCA)
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维普
