摘要
在水泥熟料性能的控制和优化中,需要及时有效地检测水泥熟料f-CaO含量。目前,水泥厂多采用人工取样和实验室检测的方法来检测熟料中的f-CaO含量,测量结果有很大的滞后性。针对水泥熟料f-CaO含量检测结果滞后的问题,基于水泥熟料生产数据,利用级联森林算法建立了预测模型。首先,通过对水泥熟料生产工艺和f-CaO产生原因机理进行分析,选择了模型的14个输入变量,如生料喂料量、分解炉出口温度、回转窑电流等。其次,采用滑动时间窗口方法构建时间序列,使模型输入包含更多的时序信息。最后,将提出的预测方法与3种传统机器学习模型进行对比。结果表明:该方法具有更高的预测精度和更强的泛化能力,为水泥熟料生产过程中的f-CaO含量预测提供了一种新的解决方案,对于水泥生产过程的控制和优化具有重要意义。
The timely and effective detection of f-CaO content in cement clinker plays an important role in the control and optimization of cement clinker performance.Presently,the traditional method of manual sampling and testing in the laboratory is mostly used in cement plants to detect the clinker f-CaO content.However,the measurement results have a large lag.To address the problem of lagging results of traditional f-CaO content detection,this paper developed a model for f-CaO content prediction using the cascade forest algorithm based on the site data of cement production.First,the fourteen model parameters,for example,raw material feeding rate,decomposing furnace outlet temperature,rotary kiln current,etc.were selected according to the production process of cement clinker and the formation causes of clinker f-CaO production,and then the time series were constructed as model inputs using sliding time windows to include more improved time-series information.Finally,the proposed prediction method was compared with three classical machine learning models,namely support vector machine regression(SVR),k-nearest neighbor(KNN),and random forest(RF).Results show that the built method can provide better prediction accuracy and generalization capability in predicting the f-CaO content of cement clinker.
作者
李小青
张海博
龚先政
邓全亮
马忠诚
叶家元
LI Xiaoqing;ZHANG Haibo;GONG Xianzheng;DENG Quanliang;MA Zhongcheng;YE Jiayuan(National Engineering Laboratory for Industrial Big-data Application Technology,College of Materials Science and Engineering,Beijing University of Technology,Beijing 100124,China;Beijing Avater Technology Corp.,Beijing 100102,China;China Building Materials Academy,Beijing 100036,China)
出处
《北京工业大学学报》
北大核心
2025年第3期250-257,共8页
Journal of Beijing University of Technology
基金
国家自然科学基金委员会创新研究群体项目(51621003)
中国建材集团原创技术策源地“揭榜挂帅”项目(2021YCJS01-4)。
关键词
水泥熟料
质量指标
f-CaO含量预测
时间窗口
机器学习
级联森林
cement clinker
quality indicator
f-CaO content prediction
sliding time window(SW)
machine learning
cascade forest
